Signature in the Cell
DNA and the Evidence for Intelligent Design

Stephen C. Meyer

FOR ELAINE, ONCE AND FUTURE COMPANIONContents

Prologue

Chapter 1

DNA, Darwin, and the Appearance of Design

Chapter 2

The Evolution of a Mystery and Why It Matters

Chapter 3

The Double Helix

Chapter 4

Signature in the Cell

Chapter 5

The Molecular Labyrinth

Chapter 6

The Origin of Science and the Possibility of Design

Chapter 7

Of Clues to Causes

Chapter 8

Chance Elimination and Pattern Recognition

Chapter 9Ends and Odds

Chapter 10 Beyond the Reach of Chance
Chapter 11 Self-Organization and Biochemical Predestination
Chapter 12 Thinking Outside the Bonds
Chapter 13 Chance and Necessity, or the Cat in the Hat Comes Back
Chapter 14 The RNA World
Chapter 15 The Best Explanation
Chapter 16 Another Road to Rome
Chapter 17 But Does It Explain?
Chapter 18 But Is It Science?
Chapter 19 Sauce for the Goose
Chapter 20 Why It MattersEpilogue: A Living Science
Appendix A: Some Predictions of Intelligent Design
Appendix B: Multiverse Cosmology and the Origin of Life
Notes
Bibliography
Searchable Terms
Acknowledgments
About the Author
PraiseCredits
Copyright
About the Publisher

Prologue“Dad, that’s you!” my fourteen-year-old son exclaimed as he looked at thenewspaper while we stood waiting to check out at the tiny general store. His
shock at seeing my face in the front section of the Seattle Post-Intelligencer,
when he just went to look for baseball scores, was no doubt compounded byhis awareness of our location.1 The general store on Shaw Island, one of themost remote in the San Juan chain north of Puget Sound, was the only
commercial establishment on the island. This irony was not lost on my wife,
whose raised eyebrow said it all. “I thought we were coming here to get awayfrom all of this.” We were. But then how was I to know that the local Seattle
paper would rerun the previous day’s front-page story from the New YorkTimes about the program of scientists I directed and the controversy
surrounding our work?2
The controversy about the origin of life and whether it arose from anundirected material process or from some kind of designing intelligence is notnew. It goes back in Western civilization at least as far as the ancient Greeks,
who produced philosophers representing both schools of thought. But thecontroversy over the contemporary theory of intelligent design (ID) and its
implied challenge to orthodox evolutionary theory became big news
beginning in 2004 and 2005. And, for better or worse, I found myself right inthe middle of it.
Three events sparked intense media interest in the subject. First, inAugust 2004, a technical journal housed at the Smithsonian Institution in
Washington, D.C., called the Proceedings of the Biological Society ofWashington published the first peer-reviewed article explicitly advancing thetheory of intelligent design in a mainstream scientific periodical. After the
publication of the article, the Smithsonian’s Museum of Natural History
erupted in internal controversy, as scientists angry with the editor—an
evolutionary biologist with two earned Ph.D.’s—questioned his editorial
judgment and demanded his censure. Soon the controversy spilled over into
the scientific press as news stories about the article and editor’s decisionappeared in Science, Nature, The Scientist, and the Chronicle of Higher
Education.3
The media exposure fueled further embarrassment at the Smithsonian,resulting in a second wave of recriminations. The editor, Richard Sternberg,
lost his office and his access to scientific samples and was later transferred toa hostile supervisor. After Sternberg’s case was investigated by the U.S.

Office of Special Counsel, a government watchdog organization, and by the
U.S. House Committee on Government Reform, a congressional committee,
other questionable actions came to light.4 Both investigations found that
senior administrators at the museum had interrogated Sternberg’s colleagues
about Sternberg’s religious and political beliefs and fomented amisinformation campaign designed to damage his scientific reputation andencourage his resignation.5 Sternberg did not resign his research appointment,
but he was eventually demoted.
As word of his mistreatment spread, the popular press began to run stories
about his case. Ordinarily, my reaction to such reports might have been to
shake my head in dismay and move on to the next story in the news cycle. But
in this case, I couldn’t. As it happened, I was the author of the offending
article. And some of the reporters interested in Sternberg’s mistreatment werecoming to me with questions. They wanted to know more about the theory ofintelligent design and why it had provoked such alarm among establishment
scientists.
Then in December 2004, two other events generated worldwide interest in
the theory of intelligent design. First, a renowned British philosopher, Antony
Flew, announced that he had repudiated a lifelong commitment to atheism,
citing, among other factors, evidence of intelligent design in the DNA
molecule.6 Flew noted in his announcement that his views about the origin oflife bore a striking resemblance to those of “American design theorists.”
Again, intelligent design was in the news. But what was it? This time I foundmyself on the BBC debating a prominent evolutionary biologist about thetheory.
Later in the month, the American Civil Liberties Union (ACLU)
announced a suit against a school board in the western Pennsylvania town of
Dover. The school board had just announced its intention to let high school
students learn about the theory of intelligent design. To do this, it proposed to
inform students about the existence of a book in the school library—one thatmade the case for intelligent design in opposition to the standard evolutionary
theories presented in the existing biology textbooks. When the ACLU
announced its own intentions to sue, the national media descended upon the
town en masse.
The press corps covering the story no doubt already knew about the 1925
Scopes “monkey trial” from the fictionalized Spencer Tracy movie Inherit theWind, if from no other source. In Dover they sensed they had the makings of asequel. During 2005, all the major American network and cable news
programs ran segments about the theory of intelligent design, the Dovercontroversy, or both. Stories not only appeared in major U.S. newspapers, but
in papers around the world, from the Times of London, Sekai Nippo (Tokyo),the Times of India, and Der Spiegel to the Jerusalem Post.
Then in August 2005, just as an end to the media buzz seemed near, anumber of political and religious leaders—including figures as diverse as theDalai Lama, President George W. Bush, and the pope—made publicstatements supportive of either intelligent design or allowing students to learn
about the controversy surrounding it. When Time magazine followed suit witha cover story about the controversy, our phones started ringing all over again.
As summer was drawing to an end, my wife and I decided it was time forour family to get away after friends offered us the use of their island cabin.
But in the two-week period corresponding to our vacation, the New YorkTimes ran its two front-page stories about our program at the Discovery
Institute, the Washington Post broke a story about the latest developments in
the Sternberg case, and the New York Times editorial page offered criticism ofSternberg in its main staff-written editorial.7 After Sternberg decided toappear on The O’Reilly Factor to tell his side of the story, we knew it wastime to head back to Seattle.8
My temporary notoriety provided something my colleagues and I sorely
needed—a platform for correcting much of the misinformation circulating
about the theory of intelligent design. Many news articles and reports
confused intelligent design with biblical creationism and its literal reading of
the book of Genesis. Other articles echoed the talking points of our critics andportrayed our work as either “giving up on science” or a sneaky attempt tocircumvent the legal prohibitions against teaching creationism in the publicschools that the Supreme Court had enacted in 1987.
Yet I knew that the modern theory of intelligent design was not developed
as a legal strategy, still less as one to abet creationism. Instead, it was firstconsidered in the late 1970s and early 1980s by a group of scientists—Charles
Thaxton, Walter Bradley, and Roger Olsen—as a possible explanation for an
enduring mystery of modern biology: the origin of the digital information
encoded along the spine of the DNA molecule.9
As I explained repeatedly to reporters and cable-news hosts, the theory ofintelligent design is not based on a religious text or document, even if it does
have implications that support theistic belief (a point to which I will return in
Chapter 20). Instead, intelligent design is an evidence-based scientific theory
about life’s origins that challenges strictly materialistic views of evolution.
Indeed, the theory of intelligent design challenges a specific tenet ofcontemporary evolutionary theory. According to modern neo-Darwinists suchas Oxford’s Richard Dawkins, living systems “give the appearance of having
been designed for a purpose.” But, to Dawkins and other contemporary
Darwinists, that appearance of design is entirely illusory, because wholly
undirected processes such as natural selection and random mutations canproduce the intricate design–like structures in living systems. In their view,
natural selection can mimic the powers of a designing intelligence without
being guided or directed in any way.
In contrast, the theory of intelligent design holds that there are tell-talefeatures of living systems and the universe that are best explained by an
intelligent cause—that is, by the conscious choice of a rational agent—rather
than by an undirected process. Either life arose as the result of purely
undirected processes, or a guiding intelligence played a role. Advocates ofintelligent design argue for the latter option based on evidence from thenatural world. The theory does not challenge the idea of evolution defined aschange over time or even common ancestry, but it does dispute the Darwinianidea that the cause of all biological change is wholly blind and undirected.
Even so, the theory is not based on biblical doctrine. Intelligent design is aninference from scientific evidence, not a deduction from religious authority.
Despite the opportunity I had been given in the media to clarify ourposition, my experiences left me with a sense of unfinished business. By
2005, I had devoted nearly twenty years of my life to developing a case forintelligent design based upon the discovery of the information-bearing
properties—the digital code—stored in the DNA molecule. I had written aseries of scientific and philosophical articles developing this idea,10 but thesearticles were neither particularly accessible nor gathered into one volume.
Now I repeatedly found myself in the position of having to defend anargument in sound bites that my audience did not know well enough toevaluate. How could they? Perhaps the central argument for intelligent design,
the one that first induced me to consider the hypothesis, had not been
explained adequately to a general, scientifically literate audience.
Of course, by 2005 many excellent books and articles—including several
important peer-reviewed books—had already been published on different
aspects of the theory of intelligent design. In 1996, Lehigh University
biochemist Michael Behe made a detailed case for intelligent design based
upon the discovery of nanotechnology in cells—such as the now famousbacterial flagellar motor with its thirty-part rotary engine. Behe’s Darwin’s
Black Box sold over a quarter of a million copies and almost single-handedly
put the idea of intelligent design on the cultural and scientific map. In 1998,
William Dembski, a mathematician and philosopher with two Ph.D.’s(including one from the University of Chicago), followed suit by publishing agroundbreaking work on methods of design detection. Dembski’s work, The
Design Inference, published by Cambridge University Press, established ascientific method for distinguishing the effects of intelligence from the effects
of undirected natural processes. His work established rigorous indicators ofintelligent design, but did not make any specific argument for intelligent
design based on the presence of these indicators in living organisms.

These were seminal works, but I had become convinced of intelligent
design by another route. Over the years, I began to develop a related, butlargely independent, case for intelligent design. Unfortunately I had apenchant for writing long, dense essays in obscure journals and anthologies.
Even my article in the Proceedings of the Biological Society of Washington
attracted more attention because of the controversy at the Smithsonian thanbecause of controversy over the argument itself, though there had been morethan a bit of that in some scientific circles.11
In any case, when the national media came calling, I simply could not getthem to report why I thought DNA pointed to intelligent design. Reporters
refused to cover the argument in their articles or backgrounders; debate
partners scrupulously avoided responding to it, but instead continued to recite
their talking points about the dangers of “intelligent design creationism.”
Even the judge in the Dover case decided the scientific validity of intelligent
design without considering the DNA evidence.
Though I wasn’t too keen on having federal judges decide the merit ofany scientific argument, let alone one that I favored, the Dover trial and its
associated media coverage made me aware that I needed to make myargument in a more prominent way. Many evolutionary biologists had
acknowledged that they could not explain the origin of the first life. Leading
theories failed in large measure because they could not explain where themysterious information present in the cell came from. So it seemed there were
no good counterarguments to the case I wanted to make. Yet various
avoidance strategies continued to work because the argument did not havesufficient public prominence to force a response. Too few people in the
public, the scientific community, and the media even knew about it. And yet it
provided—arguably—one of the most important and fundamental reasons forconsidering intelligent design.
None of this was actually too surprising. Since World War II, scientists
have stressed the importance of publishing their work in specialized peerreviewed journals, but throughout the history of science “paradigm-shifting”
ideas and theories have typically been presented in books, including many
that we might now call “trade press” (rather than academic) books.
There are a couple of reasons for this. First, books allow scientists tomake sustained and comprehensive arguments for synthetic new ideas. As the
Italian philosopher of science Marcello Pera has shown, scientists often argue
about competing interpretations of the evidence.12 Although this is sometimes
done successfully in short articles—as Einstein did in making his case for
special and general relativity and Watson and Crick did in their nine-hundredword article proposing a double helix structure for DNA—books have often
been the go-to genre for presenting and evaluating new arguments forsynthetic interpretations of a relevant body of evidence.
Perhaps, the best-known example of this form of scientific discourse wasprovided by Charles Darwin himself, who famously described his work in On
the Origin of Species by Means of Natural Section as “one long argument.”13
There, Darwin proposed a comprehensive interpretation of many diverse lines
of evidence. He also argued for the superior explanatory power of his theory
and its two key propositions: (1) the creative power of natural selection and,(2) the descent of all life from a common ancestor. As part of his case, he also
argued against the explanatory adequacy of rival interpretations of theevidence and refuted arguments for them. Other scientists such as Newton,
Copernicus, Galileo, and Lyell as well as a host of lesser figures have usedbooks to advance scientific arguments in favor of novel and comprehensive
interpretations of the scientific evidence in their disciplines.
There are other reasons that books are used to advance paradigm-shifting
ideas. New scientific theories often synthesize a broad range of evidence frommany related disciplines or subdisciplines of science. As such, they are often
inherently interdisciplinary in scope. On the Origin of Species incorporated
data from several disciplines, including embryology, paleontology,
comparative anatomy, and biogeography. Modern scientific journals, typicallyfocused as they are on topics within a narrowly defined subdiscipline, rarely
permit the kind of comprehensive review and assessment of evidence that theadvancement of a new interpretive framework requires.
Additionally, by creating a larger audience for a new idea, a book, andparticularly a popular trade book, can go over the heads of an entrenchedestablishment to force the reevaluation of an established theory by creatingwider interest in its standing. Darwin did this by publishing On the Origin ofSpecies by Means of Natural Selection with John Murray, a prominent tradepress in Victorian England. Michael Behe has done this as well. By making acase for intelligent design based upon various examples of nanotechnology inthe cell, Behe’s book focused international attention on the problem thatcomplex systems have posed for neo-Darwinism. It also gave the theory ofintelligent design public and, arguably, scientific standing.
This book makes a case for that same idea. It does so, however, on thebasis of a different class of evidence: the information—the digital code—
stored in DNA and the other large biological molecules. The case I make forintelligent design is less well known than Professor Behe’s and, therefore, tomany completely new. Even so, it is not based upon a new discovery. It is,
instead, based upon one of the most famous breakthroughs of modern
biology: the discovery in 1953 of the information-bearing capacities of the
DNA molecule, what I call the “signature in the cell.”
In 2005, when I was repeatedly placed in the position of defending thetheory of intelligent design in the media, the argument that I most wanted tomake in its favor had little public standing. I have written this book to remedy
that deficiency. This book attempts to make a comprehensive,interdisciplinary argument for a new view of the origin of life. It makes “onelong argument” for the theory of intelligent design.
Before coming to work full-time at the Discovery Institute, I worked fortwelve years as a college professor. In teaching I’ve found that it is often
easier to understand a scientific theory if one can follow the historicalprogression of thought that led to its formulation. Following a story of
discovery is not only more engaging, it can also illuminate the process ofreasoning by which investigators came to their conclusions. For this reason,
I’ve chosen to present my case for intelligent design in the context of a larger
historical and personal narrative.
Thus, Signature in the Cell does not just make an argument; it also tells astory, a mystery story and the story of my engagement with it. It tells about
the mystery that has surrounded the discovery of the digital code in DNA andhow that discovery has confounded repeated attempts to explain the origin of
the first life on earth. Throughout the book I will call this mystery “the DNA
enigma.”A brief word about the organization of the book: in Chapters 1 and 2 I
define the scientific and philosophical issues at stake in the DNA enigma andgive some historical background about the larger origin-of-life debate. In
Chapters 3, 4, and 5 I describe the mystery surrounding DNA in more detailin order to establish what it is that any theory of the origin of life must
explain. After a short interlude in Chapters 6 and 7 in which I examine what
scientists in the past have thought about biological origins and how scientists
currently investigate these questions, I examine (in Chapters 8 through 14) thecompeting explanations for the origin of biological information. Then, in
Chapters 15 and 16, I present a positive case for intelligent design as the best
explanation for the origin of the information necessary to produce the firstlife. Finally, in Chapters 17 through 20, I defend the theory of intelligent
design against various popular objections to it. In the Epilogue, I show that
intelligent design offers a fruitful approach to future scientific research. Not
only does it illuminate some very recent and surprising discoveries ingenomics, but it also suggests productive new lines of scientific investigation
for many subdisciplines of biology.
My interest in the DNA enigma stretches back nearly twenty-five years.
And though there were times (particularly in 2005) when I was frustrated withmyself for not having already produced this work, my protracted production
schedule has had at least two unintended advantages. First, it has given me theopportunity to engage in both private conversation and public debate withsome of the leading scientific figures involved in this controversy. That has
made it possible for me to present what I hope is an unusually thoroughanalysis of the competing explanations for the origin of the information inliving cells.
Second, because of the timing of its release, this book may contribute tothe ongoing assessment of Darwin’s legacy just when many scientists,
scholars, reporters, and others will be doing so. This year marks the 200th
anniversary of Darwin’s birth and the 150th anniversary of the publication of
On the Origin of Species. In the Origin, Darwin accomplished many things.
He introduced a new framework for understanding the history of life. He
identified a new mechanism of biological change. And, according to manyscholars and scientists, he also refuted the scientific argument for design. He
did this by explaining away any presumed vestiges of an actual designing
intelligence, showing instead that these “appearances of design” had been
produced by a purely undirected process—indeed, one that could mimic thepowers of a designing mind. As evolutionary biologist Francisco Ayala hasrecently explained, Darwin explained the appearance of design without
recourse to an actual designer. He gave us “design without a designer.”14 But
is this really true? Even if we grant Darwin’s argument in the Origin, does itreally follow that he refuted the design hypothesis? This book will present a
fresh perspective on this question by examining one of the most enduring
mysteries of modern biology.

1DNA, Darwin, and the Appearance of Design

When James Watson and Francis Crick elucidated the structure of DNA in
1953, they solved one mystery, but created another.
For almost a hundred years after the publication of On the Origin ofSpecies by Charles Darwin in 1859, the science of biology rested secure in theknowledge that it had explained one of humankind’s most enduring enigmas.
From ancient times, observers of living organisms had noted that living things
display organized structures that give the appearance of having been
deliberately arranged or designed for a purpose, for example, the elegant form
and protective covering of the coiled nautilus, the interdependent parts of theeye, the interlocking bones, muscles, and feathers of a bird wing. For the mostpart, observers took these appearances of design as genuine. Observations ofsuch structures led thinkers as diverse as Plato and Aristotle, Cicero and
Maimonides, Boyle and Newton to conclude that behind the exquisite
structures of the living world was a designing intelligence. As Newton wrote
in his masterpiece The Opticks: “How came the Bodies of Animals to becontrived with so much Art, and for what ends were their several parts? Was
the Eye contrived without Skill in Opticks, and the Ear without Knowledge ofSounds?…And these things being rightly dispatch’d, does it not appear fromPhænomena that there is a Being incorporeal, living, intelligent…?”1
But with the advent of Darwin, modern science seemed able to explain
this appearance of design as the product of a purely undirected process. In theOrigin, Darwin argued that the striking appearance of design in livingorganisms—in particular, the way they are so well adapted to theirenvironments—could be explained by natural selection working on randomvariations, a purely undirected process that nevertheless mimicked the powers
of a designing intelligence. Since then the appearance of design in living
things has been understood by most biologists to be an illusion—a powerfully
suggestive illusion, but an illusion nonetheless. As Crick himself put it thirtyfive years after he and Watson discerned the structure of DNA, biologists
must “constantly keep in mind that what they see was not designed, but rather
evolved.”2
But due in large measure to Watson and Crick’s own discovery of theinformation-bearing properties of DNA, scientists have become increasinglyand, in some quarters, acutely aware that there is at least one appearance ofdesign in biology that may not yet have been adequately explained by natural
selection or any other purely natural mechanism. Indeed, when Watson andCrick discovered the structure of DNA, they also discovered that DNA storesinformation using a four-character chemical alphabet. Strings of precisely
sequenced chemicals called nucleotide bases store and transmit the assembly
instructions—the information—for building the crucial protein molecules andmachines the cell needs to survive.
Crick later developed this idea in his famous “sequence hypothesis,”according to which the chemical parts of DNA (the nucleotide bases) function
like letters in a written language or symbols in a computer code. Just as lettersin an English sentence or digital characters in a computer program mayconvey information depending on their arrangement, so too do certain
sequences of chemical bases along the spine of the DNA molecule convey
precise instructions for building proteins. Like the precisely arranged zeros
and ones in a computer program, the chemical bases in DNA convey
information in virtue of their “specificity.” As Richard Dawkins notes, “Themachine code of the genes is uncannily computer-like.”3 Software developer
Bill Gates goes further: “DNA is like a computer program but far, far moreadvanced than any software ever created.”4
But if this is true, how did the information in DNA arise? Is this striking
appearance of design the product of actual design or of a natural process thatcan mimic the powers of a designing intelligence? As it turns out, thisquestion is related to a long-standing mystery in biology—the question of theorigin of the first life. Indeed, since Watson and Crick’s discovery, scientists
have increasingly come to understand the centrality of information to even thesimplest living systems. DNA stores the assembly instructions for building themany crucial proteins and protein machines that service and maintain even themost primitive one-celled organisms. It follows that building a living cell in
the first place requires assembly instructions stored in DNA or someequivalent molecule. As origin-of-life researcher Bernd-Olaf Küppers
explains, “The problem of the origin of life is clearly basically equivalent to
the problem of the origin of biological information.”5

Figure 1.1. James Watson and Francis Crick at the Cavendish Laboratory in
Cambridge. Courtesy of Barrington Brown/Photo Researchers, Inc.
Much has been discovered in molecular and cell biology since Watson
and Crick’s revolutionary discovery more than fifty years ago, but these
discoveries have deepened rather than mitigated the enigma of DNA. Indeed,
the problem of the origin of life (and the origin of the information needed to
produce it) remains so vexing that Harvard University recently announced a$100 million research program to address it.6 When Watson and Crickdiscovered the structure and information-bearing properties of DNA, they did
indeed solve one mystery, namely, the secret of how the cell stores andtransmits hereditary information. But they uncovered another mystery thatremains with us to this day. This is the DNA enigma—the mystery of theorigin of the information needed to build the first living organism.
In one respect, of course, the growing awareness of the reality of
information within living things makes life seem more comprehensible. We
live in a technological culture familiar with the utility of information. We buy
information; we sell it; and we send it down wires. We devise machines tostore and retrieve it. We pay programmers and writers to create it. And weenact laws to protect the “intellectual property” of those who do. Our actions
show that we not only value information, but that we regard it as a real entity,
on par with matter and energy.
That living systems also contain information and depend on it for theirexistence makes it possible for us to understand the function of biologicalorganisms by reference to our own familiar technology. Biologists have alsocome to understand the utility of information, in particular, for the operationof living systems. After the early 1960s advances in the field of molecularbiology made clear that the digital information in DNA was only part of acomplex information-processing system, an advanced form of
nanotechnology that mirrors and exceeds our own in its complexity, storage
density, and logic of design. Over the last fifty years, biology has advanced as
scientists have come to understand more about how information in the cell isstored, transferred, edited, and used to construct sophisticated machines andcircuits made of proteins.
The importance of information to the study of life is perhaps nowheremore obvious than in the emerging fields of genomics and bioinformatics.
Over the last decade, scientists involved in these disciplines have begun tomap—character by character—the complete sequence of the genetic
instructions stored on the human genome and those of many other species.
With the completion of the Human Genome Project in 2000, the emerging
field of bioinformatics entered a new era of public interest. News
organizations around the world carried President Clinton’s announcement of
the project’s completion on the White House lawn as Francis Collins,scientific director of the project, described the genome as a “book,” arepository of “instructions,” and the “book of life.”7 The Human Genome
Project, perhaps more than any discovery since the elucidation of the structureof DNA in 1953, has heightened public awareness of the importance of
information to living things. If Watson and Crick’s discovery showed that
DNA stores a genetic text, Francis Collins and his team took a huge step
toward deciphering its message. Biology has irrevocably entered an
information age.
In another way, however, the reality of information within living things
makes life seem more mysterious. For one thing, it is difficult to understandexactly what information is. When a personal assistant in New York types a
dictation and then prints and sends the result via fax to Los Angeles, something will arrive in L.A. But that thing—the paper coming out of the fax
machine—did not originate in New York. Only the information on the paper
came from New York. No single physical substance—not the air that carried
the boss’s words to the dictaphone, or the recording tape in the tiny machine,or the paper that entered the fax in New York, or the ink on the paper coming
out of the fax in Los Angeles—traveled all the way from sender to receiver.
Yet something did.
The elusive character of information—whether biological or otherwise—
has made it difficult to define by reference to standard scientific categories.
As evolutionary biologist George Williams notes, “You can speak of galaxies

and particles of dust in the same terms because they both have mass andcharge and length and width. [But] you can’t do that with information andmatter.”8 A blank magnetic tape, for example, weighs just as much as one“loaded” with new software—or with the entire sequence of the humangenome. Though these tapes differ in information content (and value), they do
not do so because of differences in their material composition or mass. As
Williams concludes, “Information doesn’t have mass or charge or length inmillimeters. Likewise matter doesn’t have bytes…. This dearth of shareddescriptors makes matter and information two separate domains.”9When scientists during the late 1940s began to define information, they
did not make reference to physical parameters such as mass, charge, or watts.
Instead, they defined information by reference to a psychological state—thereduction of uncertainty—which they proposed to measure using themathematical concept of probability. The more improbable a sequence ofcharacters or signals, the more uncertainty it reduces, and thus the more
information it conveys.10
Not surprisingly, some writers have come close to equating information
with thought itself. The information technology guru George Gilder, forexample, notes that developments in fiber optics have allowed more and more
information to travel down smaller and smaller (and lighter and lighter) wires.
Thus, he notes that as technology advances, we convey ever more thought
across ever less matter—where the numerator in that ratio, namely, thought,
corresponds precisely to information.11
So should we think of information as thought—as a kind of mental
chimera etched in stone or burned onto compact discs? Or can we define
information less abstractly as, perhaps, just an improbable arrangement ofmatter?
Whatever information is—whether thought or an elaborate arrangementof matter—one thing seems clear. What humans recognize as information
certainly originates from thought—from conscious or intelligent activity. A
message received via fax by one person first arose as an idea in the mind ofanother. The software stored and sold on a compact disc resulted from thedesign of a software engineer. The great works of literature began first as
ideas in the minds of writers—Tolstoy, Austen, or Donne. Our experience of
the world shows that what we recognize as information invariably reflects theprior activity of conscious and intelligent persons.
What, then, should we make of the presence of information in livingorganisms? The Human Genome Project, among many other developments inmodern biology, has pressed this question to the forefront of publicawareness. We now know that we do not just create information in our own
technology; we also find it in our biology—and, indeed, in the cells of every

living organism on earth. But how did this information arise? And what does
the presence of information in even the simplest living cell imply about life
and its origin? Who or what “wrote” the book of life?
The information age in biology officially began in the mid-1950s with theelucidation of the chemical structure and information-bearing properties ofDNA (deoxyribonucleic acid)—the molecule of heredity. Beginning in 1953
with their now famous communication to the British scientific journal Nature,
James Watson and Francis Crick identified DNA as the molecular repositoryof genetic information.12 Subsequent developments in the field of molecularbiology confirmed this idea and showed that the precisely sequenced bases
attached to the helical backbone of DNA store the information for building
proteins—the sophisticated enzymes and machines that service the cells in allliving things.
Though the discovery of the information-bearing properties of DNA dates
back over a half century, the recognition of the full significance of this
discovery has been slow in coming. Many scientists have found it difficult torelinquish an exclusive reliance upon the more traditional scientific categories
of matter and energy. As George Williams (himself an evolutionary biologist)
notes, “Evolutionary biologists have failed to realize that they work with two
more or less incommensurable domains: that of information and that ofmatter…. The gene is a package of information, not an object. The pattern ofbase pairs in a DNA molecule specifies the gene. But the DNA molecule isthe medium, it’s not the message.”13
Yet this recognition begs deeper questions. What does it mean when wefind information in natural objects—living cells—that we did not ourselves
design or create? As the information theorist Hubert Yockey observes, the“genetic code is constructed to confront and solve the problems ofcommunication and recording by the same principles found…in modern
communication and computer codes.” Yockey notes that “the technology of
information theory and coding theory has been in place in biology for at least
3.85 billion years,” or from the time that life first originated on earth.14 Whatshould we make of this fact? How did the information in life first arise?
Our commonsense reasoning might lead us to conclude that the
information necessary to the first life, like the information in humantechnology or literature, arose from a designing intelligence. But modern
evolutionary biology rejects this idea. Many evolutionary biologists admit, ofcourse, that living organisms “appear to have been carefully and artfully
designed,” as Richard Lewontin puts it.15 As Richard Dawkins states,“Biology is the study of complex things that appear to have been designed fora purpose.”16 Nevertheless, Lewontin and Dawkins, like evolutionary
biologists generally, insist that the appearance of design in life is illusory.

Life, they say, looks designed, but was not designed by an actual intelligent or
purposive agent.
Darwin’s Designer SubstituteWhy do evolutionary biologists so confidently assert that the appearance ofdesign in living organisms is illusory? Of course, the answer to this question
is well known. Evolutionary biologists have a theory that can apparently
explain, or explain away, the appearance of design without invoking an actual
designer. According to classical Darwinism, and now modern neo-Darwinism,
the mechanism of natural selection acting on random variations (or mutations)can mimic the effects of intelligence, even though the mechanism is, ofcourse, entirely blind, impersonal, and undirected.17

Figure 1.2. English naturalist Charles Robert Darwin (1809–82), age seventytwo. Courtesy of SPL/Photo Researchers, Inc.
Darwin developed his principle of natural selection by drawing on an
analogy with artificial selection: the process of selective breeding to change
the characteristics (whether anatomical, physiological, or behavioral) of agroup of organisms. For example, a farmer might observe that some of his
young stallions are faster than others. If he allows only the fastest of these to
breed with the fastest mares, then, after several generations of selective
breeding, he will own a small group of speedy “thoroughbreds” suitable for
racing on the Downs.
Darwin realized that nature could imitate this process of selective
breeding. The presence of unusually fast predatory wild cats would imperil allbut the fastest horses in a wild herd. After several generations of suchpredatory challenge, the speed of the remaining herd might exhibit adiscernable increase. Thus, environmental forces (predators, changes inweather, competition for food, etc.) could accomplish the work of a human
breeder. By causing a population to adapt to its environment, blind forces ofnature could come to mimic, over time, the action of a selecting or designing
intelligence.
Yet if natural selection, as Darwin called this process, could improve thespeed of a horse or an antelope, why couldn’t it also produce those animals in
the first place? “Reason,” wrote Darwin “ought to conquer…imagination”18—namely, our incredulity about the possibility of such happenings and our
impression that living things appear to have been designed. According toDarwin, if given enough time, nature’s selective power might act on any
variation perfecting any structure or function far beyond what any human
could accomplish. Thus, the complex systems in life that we reflexively
attribute to intelligence have wholly natural causes. As Darwin explained,“There seems to be no more design in the variability of organic beings, and in
the action of natural selection, than in the course which the wind blows.”19 Or
as evolutionary biologist Francisco Ayala explains, “The functional design oforganisms and their features would…seem to argue for the existence of adesigner. It was Darwin’s greatest accomplishment [however] to show that thedirective organization of living beings can be explained as the result of anatural process, natural selection, without any need to resort to a Creator orother external agent.”20 Thus, Ayala and other Darwinian biologists not only
affirm that natural selection can produce “design without a designer,” theyalso assert that it is “creative without being conscious.”21
The Appearance of Design
To many outside evolutionary biology, the claim that design arises without adesigner may seem inherently contradictory. Yet, in theory at least, thepossibility that life is not what it seems represents nothing particularly
unusual. Science often shows that our perceptions of nature do not match
reality. A straight pencil appears bent when inserted in a glass of water; thesun appears to circle the earth; and the continents appear immobile. Perhaps,
living organisms only appear to be designed.
Even so, there is something curious about the scientific denial of ourordinary intuition about living things. For almost a hundred and fifty years,
since its putative explanation by Darwinian theory, this impression of design
persists as incorrigibly as ever. Public opinion polls suggest that nearly 90
percent of the American public does not accept the full-fledged neo-

Darwinian account of evolution with its denial of any role for a purposefulcreator.22 Though many of these people accept some form of evolutionarychange and have a high view of science generally, they apparently cannot
bring themselves to repudiate their deepest intuitions and convictions about
the design of the living world. In every generation since the 1860s, scientificcritics of Darwinism and neo-Darwinism have arisen marshaling serious
evidential objections to the theory. Since the 1980s a growing number ofscientists and scholars have expressed deep reservations about both biologicaland chemical evolutionary theory, each with their implicit denial of design.
And even orthodox evolutionary biologists admit the overwhelming
impression of design in modern organisms. To quote Francis Crick again,“Biologists must constantly keep in mind that what they see was not designed,
but rather evolved.”23
Perhaps more curiously, modern biologists can scarcely describe livingorganisms without resorting to language that seems to imply the very thing
they explicitly deny: intentional and purposive design. As philosopher ofscience Michael Ruse notes, biologists ask about “the purpose of the fins on
the back of the stegosaurus” or “the function of the bird’s feathers” anddiscuss whether “the Irish elk’s antlers did or did not exist in order to
intimidate rivals.” “It is true,” Ruse continues, “that during the nineteenth
century [some physicists] suggested that the moon exists in order to light theway home of lonely travelers, but no physicist would use such language
today. In biology, however, especially evolutionary biology, this kind of talkis commonplace.” He concludes, “The world of the evolutionist is drenched in
the anthropomorphism of intention.” And yet “paradoxically, even theseverest critics” of such intentional language slip into it “for the sake of
convenience.”24
In theory, at least, the use of such metaphor in science derives fromignorance. Physicists talk about gravitational “attraction,” because they don’t
really know what causes action at a distance. Metaphors reign where mystery
resides. Yet, on these grounds, we might have expected that as biology
advanced, as new discoveries explicated the molecular basis of biological
functions, biology’s reliance upon the language of purpose, upon teleologicalmetaphor, might have diminished. Yet the very opposite has taken place. The
advent of the most reductionistic subdiscipline of modern biology—molecularbiology—has only deepened our dependence on teleological language.
In fact, molecular biologists have introduced a new “high-tech” teleology,
taking expressions, often self-consciously, from communication theory,
electrical engineering, and computer science. The vocabulary of modern
molecular and cell biology includes apparently accurate descriptive terms thatnevertheless seem laden with a “meta-physics of intention”: “genetic code,”“genetic information,” “transcription,” “translation,” “editing enzymes,”“signal-transduction circuitry,” “feedback loop,” and “information-processing
system.” As Richard Dawkins notes, “Apart from differences in jargon, thepages of a molecular-biology journal might be interchanged with those of acomputer-engineering journal.”25 As if to underscore the point, University of
Chicago cell biologist James Shapiro describes the integrated system ofproteins that constitutes the mammalian blood-clotting system “as a powerfulreal-time distributed computing system.” In the same context he notes thatmany biochemical systems within the cell resemble “the wiring diagram foran electronic circuit.”26 As the historian of biology Timothy Lenoir observes,“Teleological thinking has been steadfastly resisted by modern biology. And
yet in nearly every area of research, biologists are hard pressed to findlanguage that does not impute purposiveness to living forms.”27
Thus, it seems that an acquaintance with biological organisms, to say
nothing of the molecular biology of the cell, leads even those who repudiate
design to use language that seems incompatible with their own reductionistic
and Darwinian perspective—with their official denial of actual design.
Although this may ultimately signify nothing, it does at least raise a question.
Does the persistence of our perception of design, and the use of incorrigibly
teleological language, indicate anything about the origin of life or theadequacy of scientific theories that deny (actual) design in the origin of living
systems?
As always, in science the answer to such questions depends entirely on
the justification that scientists can provide for their theories. Intuitions andperceptions can be right or wrong. It might well be, as many in biology assure
us, that public and even scientific doubts about evolutionary theory derivesolely from ignorance or religious prejudice, and that teleological language
reflects nothing more than a metaphor of convenience, like saying the sun has
set behind the horizon. Yet the persistence of dissenting scientific opinion andthe inability of biologists to avoid the language of purpose raise a pardonablecuriosity. Have evolutionary biologists discovered the true cause of theappearance of design in living systems, or should we look for another? Should
we trust our intuitions about living organisms or accept the standard
evolutionary account of biological origins?
The Origin of Biological Information
Consider the following sequence of letters:
AGTCTGGGACGCGCCGCCGCCATGATCATCCCTGTACGCTGCTTCACTTGT
GGCAAGATCGTCGGCAACAAGTGGGAGGCTTACCTGGGGCTGCTGCAGGCCGAGTACACCGAGGGGTGAGGCGCGGGCCGGGGCTAGGGGCTGAGTCCGC
CGTGGGGCGCGGGCCGGGGCTGGGGGCTGAGTCCGCCCTGGGGTGCGCGC
CGGGGCGGGAGGCGCAGCGCTGCCTGAGGCCAGCGCCCCATGAGCAGCTTCAGGCCCGGCTTCTCCAGCCCCGCTCTGTGATCTGCTTTCGGGAGAACC

This string of alphabetic characters looks as if it could be a block of encoded
information, perhaps a section of text or machine code. That impression isentirely correct, for this string of characters is not just a random assortment of
the four letters A, T, G, and C, but a representation of part of the sequence ofgenetic assembly instructions for building a protein machine—an RNA
polymerase28—critical to gene expression (or information processing) in aliving cell.
Now consider the following string of characters:
01010111011010000110010101101110001000000110100101
1011100010000001110100011010000110010100100000010
0001101101111011101010111001001110011011001010010
00000110111101100110001000000110100001110101011011
0101100001011011100010000001100101011101100110010
1011011100111010001110011001000000110100101110100
This sequence also appears to be an information-rich sequence, albeit written
in binary code. As it happens, this sequence is also not just a random array ofcharacters, but the first words of the Declaration of Independence (“When in
the course of human events…”)29 written in the binary conversion of theAmerican Standard Code for Information Interchange (ASCII). In the ASCII
code, short specified sequences of zeros and ones correspond to specificalphabetic letters, numerals, or punctuation marks.
Though these two blocks of encoded information employ different
conventions (one uses the ASCII code, the other the genetic code), both arecomplex, nonrepeating sequences that are highly specified relative to thefunctional or communication requirements that they perform. This similarity
explains, in part, Dawkins’s observation that, “The machine code of the genes
is uncannily computer-like.” Fair enough. But what should we make of thissimilarity between informational software—the undisputed product ofconscious intelligence—and the informational sequences found in DNA andother important biomolecules?
Introduction to an Enigma
I first encountered the DNA enigma as a young scientist in Dallas, Texas, in1985. At the time, I was working for one of the big multinational oil
companies. I had been hired as an exploration geophysicist several years
earlier just as the price of oil had spiked and just as I was graduating fromcollege with degrees in physics and geology. My job, as the Texas oilmen put
it, was to “look for awl out in the guff.”
Though I had been a physics and geology student, I had enough exposure
to biology to know what DNA did. I knew that it stored the instruction set, theinformation, for building proteins in the cell and that it transmitted hereditary
traits in living things using its four-character chemical alphabet. Even so, likemany scientists I had never really thought about where DNA—or the
information it contained—came from in the first place. If asked, I would havesaid it had something to do with evolution, but I couldn’t have explained theprocess in any detail.
On February 10, 1985, I learned that I wasn’t the only one. On that day I
found myself sitting in front of several world-class scientists who werediscussing a vexing scientific and philosophical question: How did the firstlife on earth arise? As recently as the evening before, I had known nothing
about the conference where this discussion was now taking place. I had been
attending another event in town, a lecture at the Southern Methodist
University by a Harvard astronomer discussing the big-bang theory. There I
learned of a conference taking place the following day that would tackle three
big scientific questions—the origin of the universe, the origin of life, and thenature of human consciousness. The conference would bring together
scientists from competing philosophical perspectives to grapple with each ofthese issues. The next morning I walked into the downtown Hilton where theconference was being held and heard an arresting discussion of what scientists
knew they didn’t know.
I was surprised to learn—contrary to what I had read in many text-books—that the leading scientific experts on the origin of life had no satisfactory
explanation for how life had first arisen. These experts, many of whom were
present that weekend in Dallas, openly acknowledged that they did not havean adequate theory of what they called “chemical evolution,” that is, a theoryof how the first living cell arose from simpler chemicals in the primordial
ocean. And from their discussions it was clear that DNA—with its mysterious
arrangements of chemical characters—was a key reason for this impasse.
The discussion changed the course of my professional life. By the end ofthat year, I was preparing to move to the University of Cambridge in England,in part to investigate questions I first encountered on that day in February.
On its face, my change of course looked like a radical departure from myprevious interests, and that’s certainly how my friends and family took it. Oilcompany geophysics was a highly practical, commercially relevant form of
applied science. A successful study of the subsurface of the earth could net

the company millions of dollars of revenue from the resulting discovery of oil
and gas. The origin of life, however, was a seemingly intractable—even
arcane—theoretical question, with little or no direct commercial or practicalimport.
Nevertheless, at the time, the transition seemed entirely natural to me.
Perhaps it was because I had long been interested in scientific questions anddiscoveries that raised larger philosophical issues. In college, I had taken
many philosophy courses while pursuing my scientific training. But perhaps it
was what I was doing at the oil company itself. By the 1980s looking for oil
required the use of sophisticated computer-assisted seismic-imaging
techniques, at the time a cutting-edge form of information technology. Aftersending artificial seismic waves down into the earth, geophysicists would
time the resulting echoes as they traveled back to the surface and then use theinformation from these signals to reconstruct a picture of the subsurface of theearth. Of course, at every stage along the way we depended heavily on
computers and computer programs to help us process and analyze theinformation we received. Perhaps what I was learning about how digital
information could be stored and processed in machines and about how digitalcode could direct machines to accomplish specific tasks made life itself—andthe digital code stored in its DNA—seem less mysterious. Perhaps this madethe problem of the origin of life seem more scientifically tractable andinteresting. In any case, when I learned of the enigma confronting origin-oflife researchers and why DNA was central to it, I was hooked.
A controversy that erupted at the conference added to my sense of
intrigue. During a session on the origin of life, the scientists were discussing
where the information in DNA had come from. How do chemicals arrange
themselves to produce code? What introduced drama into what might haveotherwise been a dry academic discussion was the reaction of some of thescientists to a new idea. Three of the scientists on the panel had just publisheda controversial book called The Mystery of Life’s Origin with a prominent
New York publisher of scientific monographs. Their book provided acomprehensive critique of the attempts that had been made to explain how the
first life had arisen from the primordial ocean, the so-called prebiotic soup.
These scientists, Charles Thaxton, Walter Bradley, and Roger Olsen, hadcome to the conclusion that all such theories had failed to explain the originof the first life. Surprisingly, the other scientists on the panel—all experts inthe field—did not dispute this critique.
What the other scientists did dispute was a controversial new hypothesisthat Thaxton and his colleagues had floated in the epilogue of their book in an
attempt to explain the DNA enigma. They had suggested that the information
in DNA might have originated from an intelligent source or, as they put it, an“intelligent cause.” Since, in our experience, information arises from anintelligent source, and since the information in DNA was, in their words,“mathematically identical” to the information in a written language orcomputer code, they suggested that the presence of information in DNA
pointed to an intelligent cause. The code, in other words, pointed to a
programmer.
That was where the fireworks started. Other scientists on the panel
became uncharacteristically defensive and hostile. Dr. Russell Doolittle, of
the University of California at San Diego, suggested that if the three authors
were not satisfied with the progress of origin-of-life experiments, then theyshould “do them.” Never mind that another scientist on the panel who had
favored Thaxton’s hypothesis, Professor Dean Kenyon, of San Francisco State
University, was a leading origin-of-life researcher who had himself performed
many such experiments. It was clear that Doolittle regarded the threescientists, despite their strong credentials, as upstarts who had violated some
unspoken convention. Yet it was also clear, to me at least, that the authors of
the new book had seized the intellectual initiative. They had offered a bold
new idea that seemed at least intuitively plausible, while those defending thestatus quo offered no plausible alternative to this new explanation. Instead,the defenders of the status quo were forced to accept the validity of the new
critique. All they could do was accuse the upstarts of giving up too soon andplead for more time.
I left deeply intrigued. If my sense of the scientific status of the problem
was accurate—if there was no accepted or satisfactory theory of the origin of
the first life—then a mystery was at hand. And if it was the case thatevolutionary theory could not explain the origin of the first life because it
could not explain the origin of the genetic information in DNA, thensomething that we take for granted was quite possibly an important clue in amystery story. DNA with its characteristic double-helix shape is a cultural
icon. We see the helix in everything from music videos and modern art toscience documentaries and news stories about criminal proceedings. We knowthat DNA testing can establish guilt, innocence, paternity, and distantgenealogical connections. We know that DNA research holds the key tounderstanding many diseases and that manipulating DNA can alter thefeatures of plants and animals and boost food production. Most of us knowroughly what DNA is and what it does. But could it be that we do not know
anything about where it came from or how it was first formed?

Figure 1.3. Charles Thaxton. Printed by permission from Charles Thaxton.
The controversy at the conference served to awaken me to the strange
combination of familiarity and mystique that surrounds the double helix andthe digital code it contains. In the wake of the conference, I learned that oneof the scientists who participated in the origin-of-life discussion was living in
Dallas. It was none other than Charles Thaxton, the chemist who with his
coauthors had proposed the controversial idea about an intelligence playing arole in the origin of biological information. I called him, and he offered tomeet with me. We began to meet regularly and talk, often long after work
hours. As I learned more about his critique of “origin-of-life studies” and his
ideas about DNA, my interest in the DNA enigma grew.
These were heady and exciting days for me as I first encountered andgrappled with these new ideas. If Thaxton was right, then the classical design
argument that had been dismissed first by Enlightenment philosophers such as
David Hume in the eighteenth century and then later by evolutionary
biologists in the wake of the Darwinian revolution might have legitimacy
after all. On a visit back home to Seattle, I described what I had been learning
to one of my earlier college mentors whose critical faculties I greatly
respected, a philosophy professor named Norman Krebbs. He surprised mewhen he told me that the scientific idea I was describing was potentially oneof the most significant philosophical developments in three hundred years ofWestern thought. Could the design argument be resuscitated based upon
discoveries in modern science? And was DNA the key?
As intriguing as this new line of thinking was for me, I had a growing listof questions. I wondered, what exactly is information in a biological context?
When biologists referred to the sequences of chemicals in the DNA moleculeas “information,” were they using the term as a metaphor? Or did thesesequences of chemicals really function in the same way as “code” or “text”
that humans use? If biologists were using the term merely as a metaphor, thenI wondered whether the genetic information designated anything real and, if
not, whether the “information” in DNA could be said to point to anything,much less an “intelligent cause.”
But even if the information in DNA was in some important sense similar
to the information that human agents devise, it didn’t necessarily follow that a
prior intelligent cause was the only explanation of such information. Were
there causes for information that had not yet been considered at theconference that day? Maybe some other cause of information would be
discovered that could provide a better explanation for the informationnecessary for the origin of life. In short, I wondered, is there really evidence
for the intelligent design of life, and if so, just how strong is that evidence?
Was it, perhaps, scientifically premature or inappropriate to consider such aradical possibility, as Thaxton’s critics had suggested?
My concerns about this were heightened because of some of the things
that Thaxton and his colleagues had written to justify their conclusion. The
Mystery of Life’s Origin had made the radical claim that an intelligent cause
could be considered a legitimate scientific hypothesis for the origin of life. To
justify this claim Thaxton and colleagues argued that a mode of scientificinquiry they called origins science allowed for the postulation of singular acts
of intelligence to explain certain phenomena. Thaxton and his colleagues
distinguished what they called “origins sciences” from “operation sciences.”
Operation sciences, in their view, focus on the ongoing operation of theuniverse. These sciences describe recurring phenomena like the motions of
the planets and chemical reactions that can be described by general laws ofphysics and chemistry. Origins sciences, on the other hand, deal with unique
historical events and the causes of those events—events such as the origin of
the universe, the formation of the Grand Canyon, and the invention of ancient
tools and agriculture. Thaxton and his colleagues argued that inferring an
intelligent cause was legitimate in origins science, because such sciences deal
with singular events, and the actions of intelligent agents are usually uniqueoccurrences. On the other hand, they argued that it was not legitimate to
invoke intelligent causes in operations sciences, because such sciences only
deal with regular and repeating phenomena. Intelligent agents don’t act inrigidly regular or lawlike ways and, therefore, cannot be describedmathematically by laws of nature.
Though their terminology was admittedly cumbersome, it did seem tocapture an intuitively obvious distinction. But still I had questions. Thaxton
had argued that theories in the operation sciences are readily testable against
the repeating phenomena they describe. Regularity enables prediction. If a

theory describing a repeating phenomenon was correct, then it should be able
to predict future occurrences of that phenomenon at a specific time or undercontrolled laboratory conditions. Origins theories, however, do not make suchpredictions, because they deal with unique events. For this reason, Thaxton
thought that such theories could not be tested. Theories about the past can
produce plausible, but never decisive conclusions. As a geophysicist, I knewthat earth scientists often formed hypotheses about past events, but I wasn’t
sure that such hypotheses were never testable or decisive. We have very goodscientific reasons for thinking that dinosaurs existed before humans and that
agriculture arose after the last ice age. But if Thaxton was right, then suchconclusions about the past were merely plausible—no more than possibly true
—and completely untestable.
Yet I wondered if a hypothesis about the past couldn’t be tested—if there
is no way to judge its strength or compare it against that of competing
hypotheses—then why regard the claims of historical or “origins” theories as
significant? It is provocative to claim that the evidence from DNA and ourbest scientific reasoning points strongly to an intelligent cause of life. It is notvery interesting to claim that it is possibly true (“plausible”) that DNA owes
its origin to such cause. Many statements are merely plausible or possibly
true. But that doesn’t mean we have any reason to think them likely to be true.
Rigorous scientific testing usually provides evidence-based reasons formaking such claims or for preferring one hypothesis over another. Absent
such testability, I wasn’t sure how significant, or scientific, Thaxton’sargument really was.
Even so, I was deeply fascinated with the whole issue. In September
1985, I learned that I was to be laid off from my oil-company job, as the priceof oil had dropped from $32 to $8 per barrel. I was strangely relieved. I used
the rather generous severance the company provided to begin supporting
myself as a freelance science writer. But soon after I started, I also learned
that I had received a Rotary scholarship to study in England. The following
spring a thin airmail letter arrived informing me that I had been accepted to
study the history and philosophy of science at the University of Cambridge.
This course of study would enable me to explore many of the questions that
had long fascinated me at the intersection of science and philosophy. It would
also allow me to investigate the questions that had arisen in my discussions
with Charles Thaxton.
What methods do scientists use to study biological origins? Is there a
distinctive method of historical scientific inquiry? And what does thescientific evidence tell us about the origin of biological information and how
life began? Is it possible to make a rigorous scientific argument for theintelligent design of life? I eventually completed a Ph.D. dissertation on the
topic of origin-of-life biology. In it, I was able to investigate not only thehistory of scientific ideas about the origin of life, but also questions about thedefinition of science and about how scientists study and reason about the past.
The Current Controversy
I couldn’t have known as I was leaving for England, but the two mainquestions I had about Dr. Thaxton’s idea—“Is it scientific?” and “How strongis the evidence for it?”—would resurface with a vengeance twenty years laterat the center of an international controversy, indeed, one that would engage
the attention of the mainstream media, the courts, the scientific establishment,
and the publishing and movie industries. In 2005, a federal judge would rulethat public-school science students in Dover, Pennsylvania, could not learn
about the idea that life pointed to an intelligent cause, because the idea wasneither scientific nor testable. Mainstream scientific organizations—such as
the National Academy of Sciences and American Association for the
Advancement of Science—would issue similar pronouncements.
In 2006 and 2007, a spate of books with titles like The God Delusion and
God Is Not Great would argue there is no evidence for design in biology and,
therefore, no good evidence for the existence of God. According to Oxford
evolutionary biologist Richard Dawkins and other New Atheists, the lack ofevidence for design has made the idea of God tantamount to a “delusion.” In2008, the controversy surrounding what is now known as the “theory ofintelligent design” moved into movie theaters, video stores, and candidate
press conferences. And this year, with the celebration of the 200th
anniversary of Darwin’s birth and the 150th anniversary of the publication of
On the Origin of Species, the main question that Darwin himself addressed—“Was life designed or does it merely appear designed?”—has reemerged as
scientists, scholars, teachers, and media commentators evaluate his legacy.
Yet in all of this discussion—from Dover to Dawkins to Darwin’s big
anniversary—there has been very little discussion of DNA. And yet for me
and many other scientists and scholars, the question of whether science has
refuted the design argument or resuscitated it depends critically upon thecentral mystery of the origin of biological information. This book examines
the many successive attempts that have been made to resolve this enigma—the DNA enigma—and will itself propose a solution.

2
The Evolution of a Mystery and Why It Matters

Few schoolchildren commit to memory the name of nineteenth-century
chemist Friedrich Wöhler, nor is the waste product associated with his most
famous experiment easily romanticized. Yet in 1828 the German scientist
performed an experiment that revolutionized our understanding of life.
As a professor at the Polytechnic School in Berlin, he had begun
investigating substances that released cyanide when heated. One day heheated some ammonium cyanate, figuring it would release cyanide. It didn’t.
The heat transformed the ammonium cyanate crystals, altering both their
melting point and appearance. Indeed, the resulting material, a white
crystalline substance, possessed none of the properties typical of cyanates.
What had happened? The new material seemed familiar somehow. Where had
he encountered it before? At first he thought it might be an alkaloid, but hehad to discard this idea after the mysterious substance failed to respond to
tests in ways typical of alkaloids. Wöhler cast about in his memory, riflingthrough his extensive learning in both chemistry and medicine. Then he had
it. Urea!1 Wöhler dashed off a letter to fellow chemist Jöns Jakob Berzelius:“I can no longer, as it were, hold back my chemical water; and I have to let
out that I can make urea without needing a kidney, or even of an animal,
whether of man or dog: the ammonium salt of cyanic acid (cyansäures
Ammoniak) is urea.”2
The experiment, eventually replicated in laboratories around the world,
showed that the chemical compounds in living organisms could be artificiallysynthesized.3 Though chemists before Wöhler had synthesized naturally
occurring mineral substances, many assumed it was impossible to synthesize
compounds found in organisms, since it was thought that organic matter
contained mysterious and immaterial “vital forces.”4 As Sir Fredrick
Gowland Hopkins later suggested, Wöhler’s discovery marked the beginningof a challenge to the “primitive faith in a boundary between the organic andthe inorganic which could never be crossed.”5 For this reason, Wöhler’s work
would also exert a profound influence on scientific ideas about the origin oflife for over a century and would serve as a starting point for my own
investigation of the topic.

Beginning at the Beginning
By the time I arrived in England I was fascinated with the origin of life andwanted to learn everything I could about the history of scientific thinking on
the subject. I also wanted to investigate, following my discussions withCharles Thaxton, whether scientists who studied origin events in the remote
past used a distinctive method of scientific investigation, and if so, what thatmethod of investigation entailed.
Unfortunately, being an American untutored in the intricacies of theuniversity system in the United Kingdom, I found it difficult to findinformation about the British academic programs that best fit my interests.
The Rotary scholarship I had received allowed me to attend any one of fiveoverseas universities, provided I could gain admittance. Several of themoffered programs in the history or philosophy of science, but in a pre-Internet
era it was difficult to extract detailed information from them about the
specializations of their faculties. In the end, I set my hopes on Cambridge,
since it had more of a reputation for science than the other universities on mylist.
When my wife, Elaine, and I arrived in the fall of 1986, parking our rental
car underneath the imposing Gothic architecture on Trumpington Street near
the center of Cambridge, I was more than a little intimidated. Yet within a few
weeks I began to settle into my life as a graduate student. I soon discoveredthat I had made a far better choice of programs than I could have known while
making my decision. Not only were many of the critical discoveries about
DNA and molecular biology made in Cambridge, but it also had an excellent
program in the history and philosophy of science that included a kindly Dutch
scholar named Harmke Kamminga, who happened to be an expert on thehistory of scientific theories about the origin of life.
During my first year of study—in between tutorials and lectures on
everything from the history of molecular biology to the philosophy of physics
and the sociology of science—I began to meet regularly with Harmke todiscuss the origin of life. Under her supervision, I began to investigate somecurrent theories about the origin of life, but also the early theories that gave
rise to them. Thus, I began at the beginning—with a study of how origin-oflife studies first emerged as a scientific enterprise in the nineteenth century at
the time of Darwin and his scientific contemporaries.
I was soon confronted with an interesting historical puzzle. With theacceptance of Darwin’s theory of evolution, most biologists agreed thatnatural selection could explain the appearance of design in biology. For thisreason, most philosophers and scientists have long thought that Darwin’s
theory of evolution by natural selection destroyed the design argument. Yet I
also discovered that Darwin himself admitted that his theory did not explainthe origin of life itself. In fact, one day Peter Gautry, an archivist in the
manuscripts room of the university library, allowed me in to read a letter byCharles Darwin on the subject written in 1871, twelve years after the
publication of On the Origin of Species. The letter, handwritten on brittle
paper, made clear that Darwin had little more than vague speculations to offer
as to how the first life on earth had begun.6
This was consistent with what I knew. In the Origin, Darwin did not try to
explain the origin of the first life. Instead, he sought to explain the origin of
new forms of life from simpler preexisting forms, forms that alreadypossessed the ability to reproduce. His theory assumed rather than explained
the origin of the first living thing. Since this limitation of Darwin’s theory was
widely recognized, it raised a question: Why were nineteenth-and twentiethcentury biologists and philosophers so sure that Darwin had undermined thedesign argument from biology? If scientists at the time had no detailed
explanation for how life had first arisen, how did they know that design—that
is, actual intelligent design—played no role in this critically important event?
This chapter tells the story of what I learned as I sought to answer thesequestions. In the process, it describes some of the earliest scientific theories
about the origin of life. This background will later prove helpful, since many
contemporary theories have been formulated on the foundation of theseearlier approaches. This chapter highlights something else I learned in myinvestigations as well. From the beginning, scientific theories about the originof life have inevitably raised deeper philosophical issues not only about life,
but also about the nature of ultimate reality. As I discuss at the close of thebook, these philosophical issues remain with us today and are an integral partof the DNA enigma.
Of course, during the late nineteenth century, scientists were not trying to
explain the origin of biological information, let alone the information stored
in DNA. They did not know about DNA, at least not by that name, nor were
they thinking about biological information even as a concept. But they did
seek to explain how life began and were keenly aware of the philosophicalimplications of the theories they proposed. And despite their lack ofknowledge about the inner workings of the cell, they were often oddly
confident about the adequacy of these theories. That confidence had much to
do with Friedrich Wöhler’s “Eureka!”—or “Urea!”—moment and how
scientists at the time viewed the nature of life.
Setting the Philosophical Stage
Since the time of the ancient Greeks, there have been two basic pictures ofultimate reality among Western intellectuals, what the Germans call aWeltanschuung, or worldview. According to one worldview, mind is theprimary or ultimate reality. On this view, material reality either issues from apreexisting mind, or it is shaped by a preexistent intelligence, or both. Mind,not matter, is, therefore, the prime or ultimate reality—the entity from which
everything else comes, or at least the entity with the capacity to shape thematerial world. Plato, Aristotle, the Roman Stoics, Jewish philosophers suchas Moses Maimonides, and Christian philosophers such as St. Thomas
Aquinas each held some version of this perspective.7 Most of the founders ofmodern science during the period historians of science call the scientificrevolution (1300–1700) also held this mind-first view of reality. Many ofthese early modern scientists thought that their studies of nature confirmed
this view by providing evidence, in Sir Isaac Newton’s words, of “anintelligent and powerful Being” behind it all.8 This view of reality is often
called idealism to indicate that ideas come first and matter comes later.
Theism is the version of idealism that holds that God is the source of the ideasthat gave rise to and shaped the material world.
The opposite view holds that the physical universe or nature is theultimate reality. In this view, either matter or energy (or both) are the thingsfrom which everything else comes. They are self-existent and do not need tobe created or shaped by a mind. Natural interactions between simple material
entities governed by natural laws eventually produce chemical elements fromelementary particles, then complex molecules from simple chemical elements,
then simple life from complex molecules, then more complex life from
simpler life, and finally conscious living beings such as ourselves. In thisview matter comes first, and conscious mind arrives on the scene much laterand only then as a by-product of material processes and undirected
evolutionary change. The Greek philosophers who were called atomists, suchas Leucippus and Democritus, were perhaps the first Western thinkers toarticulate something like this view in writing.9 The Enlightenment
philosophers Thomas Hobbes and David Hume also later espoused thismatter-first philosophy.10 Following the widespread acceptance of Darwin’s
theory of evolution in the late nineteenth century, many modern scientists
adopted this view. This worldview is called either naturalism or materialism,or sometimes scientific materialism or scientific naturalism, in the latter casebecause many of the scientists and philosophers who hold this perspective
think that scientific evidence supports it.
The age-old conflict between the mind-first and matter-first world-views
cuts right through the heart of the mystery of life’s origin. Can the origin oflife be explained purely by reference to material processes such as undirectedchemical reactions or random collisions of molecules? Can it be explained
without recourse to the activity of a designing intelligence? If so, then such anexplanation would seem to make a materialistic worldview—with its claimthat all of reality can be explained solely by undirected material processes—
all the more credible. Who needs to invoke an unobservable designing
intelligence to explain the origin of life, if observable material processes can
produce life on their own? On the other hand, if there is something about lifethat points to the activity of a designing intelligence, then that raises otherphilosophical possibilities. Does a matter-first or a mind-first explanation best
explain the origin of life? Either way, the origin of life was not only an
intrinsically interesting scientific topic, but one that raised incorrigibly
philosophical issues as well. For me, that was part of what made it interesting.
The Mystery of the Missing Mystery
By the close of the nineteenth century, many scientists had accepted thematter-first view. Whereas many of the founders of early modern science—such as Johannes Kepler, Robert Boyle, and Isaac Newton—had been men ofdeep religious conviction who believed that scientific evidence pointed to arational mind behind the order and design they perceived in nature, many latenineteenth-century scientists came to see the cosmos as an autonomous, selfexistent, and self-creating system, one that required no transcendent cause, no
external direction or design.
Several nineteenth-century scientific theories provided support for thisperspective. In astronomy, for example, the French mathematician Pierre
Laplace offered an ingenious theory known as the “nebular hypothesis” toaccount for the origin of the solar system as the outcome of purely natural
gravitational forces.11 In geology, Charles Lyell explained the origin of theearth’s most dramatic topographical features—mountain ranges and canyons—as the result of slow, gradual, and completely naturalistic processes ofchange such as erosion or sedimentation.12 In physics and cosmology, a belief
in the infinity of space and time obviated any need to consider the question of
the ultimate origin of matter. And, in biology, Darwin’s theory of evolution by
natural selection suggested that an undirected process could account for theorigin of new forms of life without divine intervention, guidance, or design.
Collectively, these theories made it possible to explain all the salient events innatural history from before the origin of the solar system to the emergence ofmodern forms of life solely by reference to natural processes—unaided andunguided by any designing mind or intelligence. Matter, in this view, had
always existed and could—in effect—arrange itself without the help of any
preexisting intelligence.
But the origin of the first life remained a small hole in this elaborate
tapestry of naturalistic explanation. Although Laplace’s nebular hypothesis

provided additional support for a materialistic conception of the cosmos, italso complicated attempts to explain life on earth in purely material terms.
Laplace’s theory suggested that earth had once been too hot to sustain life,
since the environmental conditions needed to support life existed only after
the planet had cooled below the boiling point of water. For this reason, thenebular hypothesis implied that life had not existed eternally, but instead
appeared at a definite time in earth’s history.13 To scientific materialists, life
might be regarded as an eternal given, a self-existent reality, like matter itself.
But this was no longer a credible explanation for life on earth. There was atime when there was no life on earth. And then life appeared. To many
scientists of a materialistic turn of mind, this implied that life must have
evolved from some nonliving materials present on a cooling prebiotic earth.
Yet no one had a detailed explanation for how this might have happened. As
Darwin himself noted in 1866, “Though I expect that at some future time the[origin] of life will be rendered intelligible, at present it seems to me beyond
the confines of science.”14
The problem of the origin of life was, at this time, rendered more acute by
the failure of “spontaneous generation,” the idea that life originates
continually from the remains of once living matter. This theory suffered aseries of setbacks during the 1860s because of the work of Louis Pasteur. In
1860 and 1861, Pasteur demonstrated that micro-organisms or germs exist in
the air and can multiply under favorable conditions.15 He showed that if air
enters sterile vessels, contamination of the vessels with microorganisms
occurs. Pasteur argued that the observed “spontaneous generation” of mold orbacterial colonies on rotting food or dead meat, for example, could be
explained by the failure of experimenters to prevent contamination withpreexisting organisms from the atmosphere.16 Pasteur’s work seemed torefute the only naturalistic theory of life’s origin then under experimental
scrutiny.17
Despite the impasse, late-Victorian-era biologists expressed little, if any,
concern about the absence of detailed explanations for how life had firstarisen. The obvious question for me was, Why? From my vantage point in
1986, having just learned about the current impasse in contemporary originof-life research, the nonchalance of the Victorians seemed itself a bit
mysterious.
As I began investigating these questions during my first year at
Cambridge, I discovered that these scientists actually had several reasons forholding this point of view. Even though many scientists knew that Darwin had
not solved the origin-of-life problem, they were confident that the problem
could be solved because they were deeply impressed by the results ofFriedrich Wöhler’s experiment. Before the nineteenth century many biologists

had taken it as almost axiomatic that the matter out of which life was madewas qualitatively different than the matter in nonliving chemicals. These
biologists thought living things possessed an immaterial essence or force, anélan vital, that conferred a distinct and qualitatively different kind ofexistence upon organisms.18 Scientists who held this view were called“vitalists,” a group that included many pioneering biologists.
Since this mysterious élan vital was responsible for the distinctive
properties of organic matter, vitalists also thought that it was impossible tochange ordinary inorganic matter into organic matter. After all, the inorganic
matter simply lacked the special ingredient—the immaterial right “stuff.”That’s why Wöhler’s experiment was so revolutionary. He showed that two
different types of inorganic matter could be combined to produce organic
matter, albeit of a somewhat inglorious kind. Though some scientists
continued to support vitalism well into the twentieth century, they had to do
so on other grounds.
Thus, Wöhler’s experiment had a direct influence on thinking about theorigin of life. If organic matter could be formed in the laboratory by
combining two inorganic chemical compounds, then perhaps organic matter
could have formed the same way in nature in the distant past. If organic
chemicals could arise from inorganic chemicals, then why couldn’t life itself
arise in the same way? After all, if vitalism was as wrong as it now appeared,
then what is life but a combination of chemical compounds?
Developments in other scientific disciplines reinforced this trend inthought. In the 1850s, a German physicist named Hermann von Helmholtz, apioneer in the study of heat and energy (thermodynamics), showed that the
principle of conservation of energy applied equally to both living andnonliving systems. The conservation of energy is the idea that energy isneither created nor destroyed during physical processes such as burning or
combustion, but merely converted to other forms.
The chemical energy in gasoline, for example, is used by an engine topropel a car. The engine burns the gasoline and uses it up. But the energy
contained in the gasoline is not destroyed; it is converted into heat (orthermal) energy, which in the cylinders is turned into mechanical or kinetic
energy to propel the car. Helmholtz demonstrated that this same principle ofenergy conservation applied to living systems by measuring the amount ofheat that muscle tissues generated during exercise.19 His experiment showedthat although muscles consume chemical energy, they also expend energy inthe work they perform and the heat they generate. That these processes werein balance supported what became known as the “first law ofthermodynamics”—energy is neither created nor destroyed.
Even before this first law of thermodynamics had been refined, Helmholtz

used a version of it to argue against vitalism. If living organisms are notsubject to energy conservation, if an immaterial and immeasurable vital force
can provide energy to organisms “for free,” then perpetual motion would bepossible.20 But, argued Helmholtz, we know from observation that is
impossible. Other developments supported this critique of vitalism. During
the 1860s and 1870s scientists identified the cell as the energy converter ofliving organisms. Experiments on animal respiration established the utility ofchemical analysis for understanding respiration and other energetic processes
in the cell.21 Since these new chemical analyses could account for all theenergy the cell used in metabolism, biologists increasingly thought it
unnecessary to refer to vital forces.22
As new scientific discoveries undermined long-standing vitalist doctrines,
they also bolstered the confidence of scientific materialists. Germanmaterialists, such as the biologist Ernst Haeckel, denied any qualitative
distinction between life and nonliving matter: “We can no longer draw afundamental distinction between organisms and anorgana [i.e., thenonliving].”23 In 1858, in an essay entitled “The Mechanistic Interpretation ofLife,” another German biologist, Rudolf Virchow, challenged vitalists to“point out the difference between chemical and organic activity.”24 With
vitalism in decline, Virchow boldly asserted his version of the materialist
credo: “Everywhere there is mechanistic process only, with unbreakable
necessity of cause and effect.”25 Life processes could now be explained byvarious physical or chemical mechanisms. Since, in our experience,mechanisms—like cogged wheels turning axles—involve material parts inmotion and nothing more, this meant that the current function of organisms
could be explained by reference to matter and energy alone.
This outlook encouraged scientific materialists to assume they could
easily devise explanations for the origin of life as well. Haeckel himself
would be one of the first scientists to try. If life was composed solely ofmatter and energy, then what else besides matter in motion—material
processes—could possibly be necessary to explain life’s origin? For
materialists such as Haeckel, it was inevitable that scientists would succeed in
explaining how life had arisen from simpler chemical precursors and that theywould do so only by reference to materialistic processes. For Haeckel, finding
a materialistic explanation for the origin of life was not just a scientificpossibility; it was a philosophical imperative.26
Evolution on a Roll
If the imperative for many scientists during this time was matter first, thecentral image was increasingly that of evolution, of nature unfolding in anundirected way, with the nebular and Darwinian hypotheses suggesting thepossibility of an unbroken evolutionary chain up to the present. Yes, theorigin of life was a missing link in that chain, but surely, it was thought, thegap would soon be bridged. Darwin’s theory, in particular, inspired many
evolutionary biologists to begin formulating theories to solve the origin-oflife problem. My supervisor, Dr. Kamminga, had a memorable way ofdescribing this phenomenon. She noted that the success of Darwin’s theory
inspired attempts at “extending evolution backward” in order to explain theorigin of the first life.
Darwin’s theory inspired confidence in such efforts for several reasons.
First, Darwin had established an important precedent. He had shown that
there was a plausible means by which organisms could gradually produce new
structures and greater complexity by a purely undirected material process.
Why couldn’t a similar process explain the origin of life from preexistingchemicals?
Darwin’s theory also implied that living species did not possess anessential and immutable nature. Since Aristotle, most biologists had believedthat each species or type of organism possessed an unchanging nature orform; many believed that these forms reflected a prior idea in the mind of adesigner. But Darwin argued that species can change—or “morph”—overtime. Thus, his theory challenged this ancient view of life. Classification
distinctions among species, genera, and classes did not reflect unchanging
natures. They reflected differences in features that organisms might possess
only for a time. They were temporary and conventional, not set in stone.27 If
Darwin was right, then it was futile to maintain rigid distinctions in biology
based on ideas about unchanging forms or natures. This reinforced theconviction that there was no impassable or unbridgeable divide between
inanimate and animate matter. Chemicals could “morph” into cells, just as one
species could “morph” into another.28
Darwin’s theory also emphasized the importance of environmental
conditions on the development of new forms of life. If conditions arose thatfavored one organism or form of life over another, those conditions would
affect the development of a population through the mechanism of natural
selection.29 This aspect of Darwin’s theory suggested that environmental
conditions may have played a crucial role in making it possible for life toarise from inanimate chemistry. It was in this context that Darwin himself first
speculated about the origin of life. In the 1871 letter to botanist Joseph
Hooker, which I had seen in the Cambridge library archive, Darwin sketchedout a purely naturalistic scenario for the origin of life. He emphasized the roleof special environmental conditions and just the right mixture of chemical
ingredients as crucial factors in making the origin of life possible: “It is often

said that all the conditions for the first production of a living organism arepresent…. But if (and Oh! what a big if!) we could conceive in some warm
little pond, with all sorts of ammonia and phosphoric salts, light, heat,electricity, etc., that a protein compound was chemically formed ready toundergo still more complex changes, at the present day such matter would beinstantly devoured or absorbed, which would not have been the case before
living creatures were formed.”30 Although Darwin conceded that his
speculations ran well ahead of available evidence, the basic approach heoutlined would seem increasingly plausible as a new theory about the natureof life came to prominence in the 1860s and 1870s.
The Protoplasmic Theory of Life
In my first year of research, I came across a statement by Russian scientist
Aleksandr Oparin. Oparin was the twentieth century’s undisputed pioneer oforigin-of-life studies, and his comment helped me to identify another key
reason for the Victorian lack of concern about the origin-of-life problem.
“The problem of the nature of life and the problem of its origin have become
inseparable,” he said.31
To explain how life originated, scientists first have to understand what life
is. That understanding, in turn, defines what their theories of the origin of life
must explain. The Victorians weren’t especially concerned with the origin-oflife problem because they thought simple life was, well, simple. They really
didn’t think there was much to explain. Biologists during this period assumed
that the origin of life could eventually be explained as the by-product of a few
simple chemical reactions.
Then, as now, scientists appreciated that many intricate structures inplants and animals appeared designed, an appearance that Darwin explained
as the result of natural selection and random variation. But for Victorianscientists, single-celled life didn’t look particularly designed, most obviously
because scientists at the time couldn’t see individual cells in any detail. Cells
were viewed as “homogeneous and structure-less globules of protoplasm,”32
amorphous sacs of chemical jelly, not intricate structures manifesting theappearance of design.
In the 1860s, a new theory of life encouraged this view. It was called the“protoplasmic theory,” and it equated vital function with a single, identifiablechemical substance called protoplasm.33 According to this theory, theattributes of living things derived from a single substance located inside thewalls of cells. This idea was proposed as a result of several scientificdevelopments in the 1840s and 1850s.34 In 1846, a German botanist named
Hugo von Mohl demonstrated that plant cells contained a nitrogen-richmaterial, which he called protoplasm.35 He also showed that plant cells needthis material for viability. Mohl and Swiss botanist Karl Nägeli latersuggested that protoplasm was responsible for the vital function and attributesof plant cells and that the cell wall merely constituted an “investment lying
upon the surface of the [cell] contents, secreted by the contents themselves.”36
This turned out to be fantastically inaccurate. The cell wall is a separate
and fascinatingly intricate structure containing a system of gates and portals
that control traffic in and out of the cell. Nevertheless, Mohl and Nägeli’s
emphasis on the importance of the cell contents received support in 1850
when a biologist named Ferdinand Cohn showed that descriptions ofprotoplasm in plants matched earlier descriptions of the “sarcode” found in
the cavities of unicellular animals.37 By identifying sarcode as animal-cell
protoplasm, Cohn connected his ideas to Mohl’s. Since both plants andanimals need this substance to stay alive, Cohn established that protoplasm
was essential to all living organisms. When, beginning in 1857, a series of
papers by scientists Franz Leybig, Heinrich Anton de Bary, and Max Shultze
suggested that cells could exist without cellular membranes (though, in fact,
we now know they cannot), scientists felt increasingly justified in identifying
protoplasm as life’s essential ingredient.38 Thus, in 1868 when the famous
British scientist Thomas Henry Huxley declared in a much publicized addressin Edinburgh that protoplasm constituted “the physical basis or matter of life”(emphasis in original), his assertion expressed a gathering consensus.39
With the protoplasmic theory defining the chemical basis of life, it
seemed plausible that the right chemicals, in the right environment, might
combine to make the simple protoplasmic substance. If so, then perhaps theorigin of life could be explained by analogy to simple processes of chemical
combination, such as when hydrogen and oxygen join to form water. If water
could emerge from the combination of two ingredients as different from water
as hydrogen and oxygen, then perhaps life could emerge from the
combination of simple chemical ingredients that by themselves bore no
obvious similarity to living protoplasm.
Early Theories of Life’s Origin: The Chemical Two-Step
I discovered another reason that scientists had maintained their confidence in
a completely materialistic account of life and the cosmos. Beginning in thelate 1860s, scientists began to offer materialistic theories of the origin of life.
And for the better part of the next eighty-five years or so (with the exceptionof one gap after the turn of the century), these theories kept pace with newscientific discoveries about the complexity of life. That is, for the most part,these new theories about how life came to be were able to explain what

scientists were learning about what life is.
Two scientists, Thomas Henry Huxley and Ernst Haeckel, were first tooffer theories of how life had arisen from nonliving chemicals. Though
Huxley was British and Haeckel German, the two men had much in common
intellectually. Both men rejected vitalism. Both men were staunch defendersof Darwin’s evolutionary approach to the origin of species. Both were ardent
scientific materialists. And both had articulated or defended the protoplasmic
theory of life. In these respects, Huxley and Haeckel embodied the various
reasons for Victorian insouciance about the origin-of-life problem. Each man
would formulate a theory of abiogenesis (life arising from nonliving matter)that reflected this intellectual posture.
Huxley imagined that the origin of life had occurred by a simple two-stepchemical process in which simple elements such as carbon, hydrogen,nitrogen, and oxygen first reacted to form common compounds such as water,
carbonic acid, and ammonia.40 He believed that these compounds then
combined, under some unspecified conditions, to form protoplasm, thechemical essence of life.
Meanwhile, in Germany, Haeckel41 offered a bit more detail, though not
much. He identified “constructive inner forces” or “formative tendencies”
inherent in matter—like those we find in inorganic crystal formation—as thecause of the self-development of life. He asserted that the causes that produce
form are the same in both inorganic crystals and living organisms.42 Thus, for
Haeckel, the origin of life could be explained by the spontaneous
crystallization of “formless lumps of protein” from simpler carbon
compounds.43 Haeckel believed that, once formed, the first one-celledorganisms, which he called Monera, would have gradually attained therelatively simple structure he assumed them to possess as they assimilated
new material from the environment. Then, due to their semifluid constitution,these primitive cells would continue to rearrange themselves internally overtime.44 Even so, he clearly regarded the essential step in the process of
abiogenesis as complete after the spontaneous crystallization of the“homogeneous and structure-less globules of protoplasm.”45
Huxley also viewed the nature of life as scarcely distinguishable frominorganic crystals. Many other biologists adopted similar views. Eduard
Pflüger, Karl Wilhelm von Nägeli, August Weismann, and Oscar Loew each
attributed the essential properties of life to a single chemical entity rather thanto complex processes involving many interrelated parts.46 Pflüger, forexample, thought the presence of carbon and nitrogen (in the form of the
cyanogen radical,–CN) distinguished “living” proteins from “dead” ones.47
By equating the essence of life with a single chemical unit such as “living
proteins” (Pflüger), “active proteins” (Loew), “biophors” (Weismann),

“probionts” (Nägeli), or “homogeneous protoplasm” (Haeckel and Huxley),scientists during the 1870s and 1880s made it easy to explain the origin oflife. Yet only as long as their simplistic conceptions of the nature of life heldsway did their equally simplistic models of the origin of life seem credible.
Over the next sixty years biologists and biochemists gradually revised
their view of the nature of life. During the 1890s, scientists began to learn
about enzymes and other types of proteins. Before 1894, scientists had only
observed enzymes catalyzing reactions outside the cell.48 With the advance oflaboratory techniques allowing scientists to gather evidence of the activity ofenzymes within cells, and with the discovery of enzymes responsible for suchmetabolic reactions as oxidation, fermentation, and fat and protein synthesis,a new theory called the “enzymatic theory” displaced the protoplasmic theoryof life.49 By the turn of the century, most biologists came to see the cell as ahighly complex system of integrated chemical reactions, not at all the sort ofthing that could be adequately explained by vague references to processes ofcrystallization. For a time, the growing awareness of this chemical complexity
impeded attempts to explain the origin of life. But by the 1920s and 1930s, a
pioneering Russian scientist formulated a new theory to keep pace with thisgrowing scientific awareness of the cell’s complexity.
Oparin to the Rescue
A new theory of evolutionary abiogenesis that envisioned a multibillion-year
process of transformation from simple chemicals to a complex metabolic
system50 was proposed to the Russian Botanical Society in May 1922 by theyoung Soviet biochemist Aleksandr I. Oparin (1894–1980).51 Oparin firstpublished his theory in Russian in 1924 and then refined and developed it,
publishing it again in English in 1938. Both books were simply called The
Origin of Life.
Oparin’s interest in the origin of life was first awakened after hearing
lectures on Darwinism from the plant physiologist Kliment Arkadievich
Timiriazev, who was himself a staunch Darwinian. “According to Oparin,”
writes historian of science Loren Graham, “Timiriazev described Darwinianevolution and revolutionary political thought as being so intimately connectedthat they amounted to the same thing. In this view Darwinism wasmaterialistic, it called for change in all spheres, it was atheistic, it waspolitically radical, and it was causing a transformation of thought andpolitics.”52

Figure 2.1. Aleksandr Oparin (1894–1980), pioneering chemical evolutionary
theorist. Courtesy of Novosti/Photo Researchers, Inc.
Oparin was a fascinating figure from a fascinating time. He published his
first theory of the origin of life just five years after the Bolshevik Revolution
while living in Moscow, where Marxist slogans and thinking were popular,
especially in intellectual circles.53 It seemed a little odd to me at first thatanyone could think about something as seemingly remote as the origin of thefirst life while such cataclysmic changes were taking place in society, but I
discovered many of the early Marxists were quite interested in the subject of
biological origins. Marx himself had corresponded with Darwin, and hethought that Darwin’s theory of evolution put his own theory about howsocieties evolved on a firm materialistic and scientific footing.54
Friedrich Engels, Marx’s intellectual collaborator, actually wrote an essayon the origin of the first life.55 Like Marx, he was convinced that majorsocietal changes took place in sudden spurts in response to changes in thematerial conditions of life and society. He wanted to show that a similar“revolution” had taken place to produce life, so that he could demonstrate theplausibility of Marxist doctrine. A key Marxist idea was that a small
quantitative increase in the intensity of some condition or situation could
suddenly produce a qualitative or revolutionary change. Dissatisfaction andalienation with the capitalist system among workers, for example, might
gradually increase over time, but it would eventually grow to a point where arevolutionary change would suddenly occur, ushering in a completely new
way of ordering society. Engels thought he could illustrate this key Marxist
concept if he showed that a quantitative increase in the complexity of asystem of chemicals could suddenly produce a qualitative (i.e., revolutionary)change in that system, resulting in the first life.56
Was Oparin influenced or motivated by such specifically Marxist ideas?
In addition to Timiriazev, whose politics Oparin described as “veryprogressive” and Leninist, Oparin was also closely associated with an older
Marxist biochemist and former revolutionary, A. N. Bakh, after 1920.57 Evenso, it’s not clear how much Marxism per se influenced Oparin’s thinking
about the origin of life. It is clear, however, that Oparin rejected all forms ofidealism. Instead, he embraced a materialistic view of reality. Accordingly, hesaw that the origin-of-life problem needed to be solved within a materialistic
framework of thought.58
At the same time, Oparin thought there were a number of scientificreasons for supposing that the origin of life could be explained by reference topurely chemical processes. First of all, there was Wöhler’s famous synthesisof urea, which showed that both living and nonliving matter share a common
chemical basis. It was clear from Oparin’s writing that, a hundred years after
Wöhler’s experiment, it continued to have a profound influence on thinking
about the nature and origin of life. To Oparin, Wöhler’s experimentestablished that “there is nothing peculiar or mysterious” about the processes
at work in a living cell “that cannot be explained in terms of the general lawsof physics and chemistry.” Oparin also noted that several nonliving materials,
not just urea, manifest attributes like those once thought to characterize only
living organisms.59 For example, carbon, the element common to all living
protoplasm and organisms, also occurs naturally in inanimate minerals suchas graphite, diamond, marble, and potash. Besides, argued Oparin, like livingorganisms, many inorganic materials display chemical organization andstructure. Inanimate materials like crystals and magnets have a very definite
and orderly organization. Crystals even reproduce themselves, though not in
the same way cells do. Although Oparin admitted inanimate materials likecrystals do not have the kind of “complicated order” observed in living cells,
the similarities he did identify between life and nonlife made him optimisticthat scientists could explain the origin of life by reference to ordinary
chemical processes.60
Even so, given the complexity of the chemical reactions going on inside
the cell, Oparin thought any return to spontaneous generation was untenable.
As he stated, “The idea that such a complicated structure with a completely
determinate fine organization could arise spontaneously in the course of a few
hours…is as wild as the idea that frogs could be formed from the May dew ormice from corn.”61 Instead, in his view, biological organization must have
evolved gradually from simpler chemistry over a long period of time.62

Oparin Sets the Stage
Oparin’s theory envisioned many discrete events along the way to thedevelopment of life. Nevertheless, his theory described processes that can bedivided into two basic stages. The first part of his theory described how thechemical building blocks of life arose from much simpler chemicals in theearth’s atmosphere and oceans. The second stage tells how the first organism
arose from these molecular building blocks. Let’s look at the first part of
Oparin’s scenario first.

Figure 2.2. Oparin’s 1936 early earth origin-of-life scenario.
Oparin thought that the early earth had a core made of heavy metals.63 As
the early earth cooled after its initial formation, he postulated, its core would
have contracted, exposing cracks and fissures in the surface of the earth.
Heavy metals from the core would have then combined with carbon forming
compounds called iron carbides. These compounds were squeezed to thesurface of the earth like toothpaste through a tube (see Fig. 2.2).
After arriving at the surface these carbide compounds would have begun
to react with the atmosphere. By 1936, Oparin had come to think that theearly earth’s atmosphere was devoid of free oxygen. Instead, he envisioned anearly atmosphere containing a noxious mixture of energy-rich gases such as
ammonia (NH3), dicarbon (C2), cyanogen (CN), steam, and simple
hydrocarbons like methene (CH) and methylene (CH2). He then envisioned
these simple hydrogen-rich molecules in the atmosphere reacting with theiron carbides arriving at the surface of the earth. This would have resulted in
the formation of heavy energy-rich hydrocarbons, the first organic
molecules.64
The compounds65 produced in this way would have then reacted withammonia (NH3) in the atmosphere to form various nitrogen-richcompounds.66 This was a significant step, because Oparin knew that theamino acids out of which protein molecules are made are themselves rich innitrogen. Oparin also thought that energy-rich hydrocarbon derivatives inwater could participate in every type of chemical change occurring in the cell,
including polymerization. This was important because polymerization is thekind of reaction by which amino acids link up to form proteins. Thus, Oparinsuggested that these hydrocarbon derivatives reacted with one another andwith other chemicals in the oceans to produce amino acids, which then linked
together to form proteins.
Oparin’s Account of the First Organisms
The second stage of Oparin’s scenario used specifically Darwinianevolutionary concepts to explain the transformation of organic molecules into
living things. In particular, he suggested that competition for survival arose
between little enclosures of protein molecules. This competition eventually
produced primitive cells with all kinds of complex chemical reactions goingon inside them. But before he could describe how competition between
protocells produced life, he had to find a chemical structure that could
function as a primitive cell, or at least as a primitive cell membrane. He
needed an inanimate structure that could enclose proteins and separate themfrom the environment.
He found what he was looking for in the work of an obscure Dutchchemist named H. G. Bungenberg de Jong. In 1932, Bungenberg de Jong
described a structure called a “coacervate,” (from the Latin coacervare,meaning “to cluster”). A coacervate is a little cluster of fat molecules thatclump together into a spherical structure because of the way they repel water.
(See Fig. 2.3.) Because these fat molecules, or lipids, have a water-repellingside and a water-attracting side, they will form a structure that both repels
water on the outside and encloses water on the inside. Thus, these coacervates
define a distinct boundary with the surrounding environment. They even

allow organic molecules to pass in and out of the coacervate cluster, thus
simulating the function of a cell membrane.
Oparin suggested that biologically significant molecules likecarbohydrates and proteins could have been enclosed in such structures in theprebiotic ocean. As these molecules began to react with one another inside thecoacervate clumps, they developed a kind of primitive metabolism. For thisreason, Oparin regarded them as intermediate structures between animate andinanimate chemistry: “With certain reservations we can even consider the firstpiece of organic slime which came into being on the earth as being the firstorganism.”67

Figure 2.3. Two-dimensional cross section of a coacervates (left) and threedimensional cutaway of half of a coacervate (right).
Oparin proposed that the attributes of these coacervate clumps would
enable the complex biochemical organization that now characterizes living
cells to arise gradually through a process of natural selection. As somecoacervates grew, they would develop increasingly efficient means for
assimilating new substances from the environment, causing their growth rates
to increase. Those that failed to develop efficient means of assimilating
essential nutrients would languish. The good “eaters” thrived, while the poor
“eaters” did not. As the relative abundance of nutrients in the environmentchanged, conditions arose that favored more highly organized organic bodies.
Less efficient protocells would soon exhaust their stored potential energy anddecompose as their supplies of nutrients diminished. But those primitive
organisms that had (by chance) developed crude forms of metabolism would
continue to develop. A Darwinian-style competition developed, eventually
resulting in the first living cell.68 (See Fig. 2.4.)
So Aleksandr Oparin explained the origin of life using Darwinian
principles. He showed how complex structures could arise gradually from
simpler ones after environmental changes had occurred that favored thecomplex structures in their competition for survival.
The Miller-Urey ExperimentOparin’s theory stimulated considerable scientific activity following theEnglish publication of his book; several scientists during the 1940s and early
1950s developed and refined Oparin’s scenario in pursuit of a more detailed
theory of chemical evolution. Perhaps the most significant attempts toadvance Oparin’s research program came in the form of laboratory
experiments, including several attempts to simulate an important step in his
historical narrative, the production of biological building blocks from simpler
atmospheric gases. The most famous, immortalized in high-school biology
textbooks around the world, is the Miller-Urey experiment.
In December 1952, while doing graduate work under Harold Urey at the
University of Chicago, Stanley Miller conducted the first experimental test of
the Oparin-Haldane chemical evolutionary model. Using boiling water, Miller
circulated a gaseous mixture of methane, ammonia, water, and hydrogenthrough a glass vessel containing an electrical discharge chamber.69 Miller
sent a high-voltage charge into the chamber via tungsten filaments to simulate
the effects of lighting on prebiotic atmospheric gases. (See Fig. 2.5.) After
two days, Miller found amino acids in the U-shaped water trap he used tocollect reaction products at the bottom of the vessel. Using a technique foranalyzing mixtures called paper chromatography, he identified the aminoacids glycine, alpha-alanine, and beta-alanine.

Figure 2.4. Simplified schematic of Oparin’s 1936 chemical evolutionary
scenario showing the main steps from simple chemicals to a more complex
living cell.

Figure 2.5. The Miller-Urey experiment.
Miller’s success in producing these protein building blocks was heralded
as a breakthrough and as powerful experimental support for Oparin’s
theoretical work. After the publication of his findings in 1953, others
replicated Miller’s results, and soon hopes were running high that acomprehensive theory of life’s origin was within reach. Miller’s experimentreceived widespread coverage in popular publications such as Time magazine
and gave chemical evolutionary theory the status of textbook orthodoxy
almost overnight.70 As science writer William Day reflected, “It was anexperiment that broke the logjam. The simplicity of the experiment, the highyields of the products and the specific biological compounds…produced by
the reaction were enough to show the first step in the origin of life was not achance event, but was inevitable.”71
By the Darwinian centennial in 1959, spirits were running high. The final
holdout in the grand materialistic story of life, earth, and the cosmos seemedat last to be falling into line. With the modern “neo-Darwinian” version of
Darwin’s theory firmly established and the emergence of an experimentally
validated theory of chemical evolution, evolutionary science had now
provided a comprehensive and fully naturalistic explanation for every
appearance of design in the living world from the humble single-celledbacterium to the most intricate neural structure of the human brain. A
seamless and fully naturalistic account of the origin and development of lifeforms appeared, if not complete, then at least sketched in enough detail topreclude anachronistic speculations about a designing hand. The problem of
the origin of life had at last been solved. Or at least so it seemed, until
scientists began to reflect more deeply on the other great discovery of 1953.

3
The Double Helix

The information revolution in biology officially began in 1953 with theelucidation of the structure of the DNA molecule. Yet the scientists who
eventually ignited this revolution were a seemingly unimpressive pair. Two
unknowns in the developing field of biochemistry, James Watson and Francis
Crick possessed no firsthand experimental data and a very limited knowledgeof the relevant chemistry. Crick hadn’t even finished his Ph.D., and the degree
he did have was in physics.1
There were three teams in the running to unlock the mystery of thestructure of DNA, which by now most biologists assumed would help explain
how hereditary traits are passed from one generation to another. The obvious
front-runner was Nobel laureate Linus Pauling. Next was an imposing team ofCambridge scientists headed by Lawrence Bragg and Max Perutz. The third
was a team with the most sophisticated imaging equipment in the world,headed by Maurice Wilkins at King’s College, University of London. As for
Watson and Crick, no scientist at the time would have even put them in therace. By most appearances, they were mere hangers-on at the Cavendish lab
in Cambridge, a couple of young men lurking around pilfering other people’sdata, out of their depth and out of the race.
While the London team leader, Maurice Wilkins, and its X-ray specialist,
Rosalind Franklin, did the painstaking work of collecting the hard data on the
mystery molecule, it looked as if Watson and Crick did little more than playwith toy models. One they presented was greeted with laughter by RosalindFranklin. Drawing on her extensive knowledge of the X-ray images that she
had generated of crystals made of DNA material, she quickly convinced
Watson, Crick, and everyone else in the room that their toy model was far
wide of the mark.
Watson, with his wild hair and perfect willingness to throw off work for a
Hedy Lamarr film, and Crick, a dapper and no longer especially young fellow
who couldn’t seem to close the deal on his dissertation—who were theseguys? They even got their lab space stripped away at one point. Eventually
they got it back, but a peek into the Cavendish lab months later would havedone little to inspire confidence. Crick hadn’t arrived yet, and there was wildhaired Watson at the table tinkering around with cardboard cutouts—a far cry

from the sophisticated technology on display at the King’s lab.
But it was in the end Watson and Crick who sparked a revolution. The
molecular biological revolution, as it came to be called, would redefine ourunderstanding of the nature of life by highlighting the importance of
information to the inner workings of living things. This revolution would also
redefine the questions that scientists investigating the origin of life would,
from that time forward, have to answer.
Of Natures and Origins
During my Ph.D. studies, I learned that scientists investigating the past often
reason much like detectives in a whodunit. Detectives consider a number ofsuspects in order to determine the culprit as they try to reconstruct the sceneof the crime. In a similar way, historical scientists—such as geologists,archaeologists, paleontologists, cosmologists, and evolutionary biologists—
weigh the merits of competing explanations as they try to figure out whatcaused a particular event in the past to occur or what caused a given structureor piece of evidence to arise. In doing so, historical scientists use a scientificmethod called the “method of multiple working hypotheses.”2 But before
scientists can evaluate competing ideas about the cause of a given event or
structure, they must have a clear understanding of what it is that needs to be
explained.
For scientists trying to explain the origin of life, one of the most important
clues we have is life itself—its structure, function, and composition. That’s
why Aleksandr Oparin, the first scientist to propose a comprehensive
scientific theory of the origin of life, said, “The problem of the nature of life
and the problem of its origin have become inseparable.”3 Harmke Kamminga
puts it this way: “At the heart of the problem of the origin of life lies afundamental question: What is it exactly that we are trying to explain theorigin of?”4
Watson and Crick’s discovery, and those that soon followed in its wake,revolutionized our understanding of the nature of life. These discoveries also
defined the features of life that scientists are now “trying to explain the originof.” This chapter tells the story of the discovery that inaugurated thisrevolution in biological understanding—the story of the double helix. Thishistorical background will prove indispensable in later chapters. In order toevaluate competing ideas about the origin of life and biological information, it
is important to know what DNA is, what it does, and how its shape andstructure allow it to store digital information. As I show in subsequent
chapters, some recent theories of the origin of life have failed precisely
because they have failed to account for what scientists have discovered overthe last century about the chemical structure of DNA and the nature of
biological information.
The Mystery of Heredity
From ancient times, humans have known a few basic facts about living things.
The first is that all life comes from life. Omne vivum ex vivo. The second isthat when living things reproduce themselves, the resulting offspring
resemble their parents. Like produces like. But what inside a living thing
ensures that its offspring will resemble itself? Where does the capacity toreproduce reside?
This was one of the long-standing mysteries of biology, and many
explanations have been proposed over the centuries. One theory proposed thatanimals contained miniature replicas of themselves stored in the reproductive
organs of males. Another theory, called pangenesis, held that every tissue ororgan of the body sent parts of itself—called gemmules—to the reproductive
organs to influence what was passed on to the next generation. But by the
mid-nineteenth century the target began to narrow as scientists increasingly
focused on the small spherical enclosures called cells, only recently withinreach of the best microscopes of the day. In 1839, Matthias Schleiden and
Theodor Schwann proposed the “cell theory,” which asserted that cells are thesmallest and most fundamental unit of life. In the wake of their proposal,
biologists increasingly focused their search for the secret of heredity on these
seemingly magical little entities and their critical contents. But through muchof the rest of the nineteenth century, the structure of cells was a complete
mystery, which is why prominent scientists like Ernst Haeckel could describe
the cell as “homogeneous and structure-less globules of protoplasm.”5
Meanwhile, however, scientists began to notice that the transmission ofhereditary traits—wherever the capacity for producing these traits might be
stored—seemed to occur in accord with some predictable patterns. The workof Gregor Mendel in the 1860s was particularly important in this regard.
Mendel studied the humble garden pea. He knew that some pea plants havegreen seeds, while some have yellow. When he crossed green peas withyellow peas, the second-generation plants always had yellow peas. If Mendel
had stopped there, he might have assumed that the capacity for making green
seeds in the next generation had been lost. But Mendel didn’t stop there. He
crossed the crosses. Each of these parent plants had yellow seeds, but theiroffspring had 75 percent yellow seeds and 25 percent green. Apparently the
first generation of crossed seeds, the all-yellow batch, nevertheless had
something for making “green” seeds tucked away inside of them, waiting toemerge in a subsequent generation, given the right circumstances.6

Mendel called the yellow trait “dominant” and the green trait “recessive.”
The latter might disappear in a given generation, but it hadn’t dropped out ofexistence. It was stored inside the seed in the form of some sort of signal,
memory, or latent capacity, waiting to express itself in a future generation.
Mendel showed that the entity or factor responsible for producing a trait(which was later called a “gene”) has some kind of existence of its own
independent of whether the trait is seen in an individual plant.
Mendel’s discovery raised an obvious question: Where and how was thishereditary memory or signal being stored? Beginning with experiments donein the years after the Civil War, biologists began to focus on the cell nucleus.
In 1869, Friedrich Miescher, the son of a Swiss physician, discovered what
would later be called DNA. Miescher was interested in the chemistry of whiteblood cells. To find such cells, he collected pus from postoperative bandages.
He then added hydrochloric acid to the pus, dissolving all the material in thecell except the nuclei. After that he added alkali and then acid to the nuclei.
Miescher called the gray organic material that formed from this procedure“nuclein,” since it was derived from the nucleus of the cell. Other scientists
using staining techniques soon isolated banded structures from the nucleus.
These came to be called “chromatin” (the material we now know as
chromosomes) because of the bright color they displayed once stained. Whenit was later shown that chromatin bands and Miescher’s nuclein reacted toacid and alkali in the same way, scientists concluded that nuclein andchromatin were the same material. When biologists observed that an equal
number of chromatin strands combine when an egg and sperm fuse into asingle nucleus, many concluded that chromatin was responsible for heredity.7
To make further progress toward a solution to the mystery of heredity,
geneticists needed to study these chromatin bands more closely. In 1902 and1903, Walter Sutton published two papers suggesting a connection between
the laws of Mendelian genetics and chromosomes.8 Sutton suggested that
Mendel’s laws could be explained by observing chromosomes during
reproduction. Since offspring receive an equal number of chromosomes fromeach parent, it was possible that they were receiving the capacity for different
characteristics—Mendel’s traits—from separate maternal and paternal
chromosomes. Since traits often occurred in pairs, and chromosomes occurredin pairs, perhaps the capacity for producing these traits was carried on
chromosomes.
Some scientists thought that this idea could be tested by altering thecomposition of the chromatin bands to see what effect various changes would
have on the creatures that possessed them. What was needed was a creaturethat reproduced quickly, possessed a relatively simple set of features, andcould be bathed in change-producing or “mutation-inducing” radiation

without raising ethical concerns. Fruit flies were the perfect choice. They had
a fourteen-day life cycle and only four pairs of chromosomes, and nobody
was likely to start picketing on their behalf.
Beginning in 1909 at Columbia University, Thomas Hunt Morgan
undertook experiments with large populations of fruit flies, subjecting them toa variety of mutagens (i.e., substances that cause mutations), increasing theirmutation rate manyfold. Then he bred them, steadily assembling a mountainof data about the resulting mutations and how often they were passed fromone generation to the next. He encountered all of the fruit-fly mutations foundin natural populations, such as “bithorax” and “white eye,” along with newones, such as “vestigial wing” (see Fig. 3.1). After studying many
generations, Morgan found that some of these traits were more likely to occur
in association. Specifically, he noticed four linkage groups, suggesting that
information-bearing entities responsible for passing along these mutations
were located physically next to each other on the chromosome. Morgan
devised a number of experiments to show that genes have a definite, linear
order on the chromosome.9
By 1909, scientists had been able to separate an acidic material from otherproteinaceous material in the chromatin bands. Chemists soon determined thechemical composition of this acidic material. They called it a “nucleic acid,”
because it had come from the nucleus. They called it a “deoxyribose nucleic
acid,” because they were able to identify a deoxygenated sugar molecule
called ribose in the molecule (see Fig. 3.2, comparing the structure ofdeoxyribose sugar and ribose sugar). Scientists also determined that themolecule was made of phosphates and four bases, called adenine, cytosine,
guanine, and thymine, the chemical formulas and structures of which had
been known for a while. (Figure 3.3 shows the formulas and structures ofeach of the chemical parts of deoxyribonucleic acid, or DNA.)

Figure 3.1. A normal fruit fly and three mutant fruit flies of the kind studied
by Thomas Morgan.

Figure 3.2. Structure of ribose sugar (left) and deoxyribose sugar (right).
Science historians often describe the process that leads to a great
discovery as “putting pieces of a puzzle together.” In the case of DNA, that
metaphor is unusually apt. By 1909, the composition and structure of thechemical parts of DNA were mostly known. But the structure of the whole
molecule was not. Further progress in the search for the secret of hereditary
information required scientists to piece together the constituent parts of themolecule in various different ways in search of a solution to this puzzle.
When the pieces locked in place properly—in accord with all that was known
about the dimensions, shapes, and bonding proclivities of the constituent parts—a solution to the puzzle would be obvious. Everything would fit. But in
1909, scientists were far from understanding how all the pieces of the DNA
molecule fit together. In fact, for years, many showed little interest in
determining the structure of DNA, because they did not think that DNA had
anything to do with heredity.
Many scientists overlooked DNA because they were convinced that
proteins played the crucial role in the transmission of hereditary traits. They
favored proteins over DNA primarily due to a misunderstanding about thechemical structure of DNA. By the early part of the twentieth century,
scientists knew that in addition to containing sugars and phosphates, nucleic
acid was composed of the four bases adenine, thymine, cytosine, and guanine,
but in 1909 chemist P. A. Levene incorrectly reported that these four
nucleotide bases always occurred in equal quantities within the DNA
molecule.10 To account for this putative fact, he formulated what he called the“tetranucleotide hypothesis.” According to this hypothesis the four nucleotide
bases in DNA linked together in the same repeating order to form a sequencesuch as ATCGATCGATCGATCGATCGATCG.

Figure 3.3. The structural formulas of each of the chemical parts of DNA (aswell as the nucleotide base uracil, which is present in RNA). RNA consists ofphosphates; the bases uracil, cytosine, guanine, and adenine; and ribose sugar(see Fig. 3.2).
Levene’s model threw many scientists off the scent, but forunderstandable reasons. For DNA to be the material responsible for producing
hereditary traits, it had to have some feature that could account for, or
produce, the great variety of physiological traits found in living organisms.
Even the humble fruit flies that Morgan used in his mutation studies had
many different features—different kinds of eyes, legs, wings, bristles, andbody proportions. If the capacity for building these structures and traits was
something like a signal, then a molecule that simply repeated the same signal(e.g., ATCG) over and over again could not get the job done. At best, such amolecule could produce only one trait. Instead, scientists knew that theyneeded to discover some source of variable or irregular specificity, a source of
information, within the heritable material (or germ line) of organisms toaccount for the many different features present in living things. Since thesequence of bases in DNA was, according to Levene, rigidly repetitive andinvariant, DNA’s potential seemed inherently limited in this regard.
That view began to change in the mid-1940s for several reasons. First, ascientist named Oswald Avery successfully identified DNA as the key factor

in accounting for heritable differences between different bacterial strains.11
When Avery was working at the Rockefeller Institute in New York hebecame intrigued by an experiment on Pneumococci bacteria performed byFrederick Griffith. The experiment moved from the unsurprising to thesurprising. If a deadly strain of the bacteria was first heated to death, thestrain was harmless when injected into mice. No surprise there. The mice
were also unharmed when injected with a living but nonvirulent strain of thevirus. No surprise there either. But then Griffith injected mice with both thelethal strain of bacteria that had been heated to death and the living but
harmless strain of bacteria. The mice died. This was surprising. One would
expect the mice to be unaffected, since both forms of bacteria had proventotally harmless before. Injected with either of the two strains separately, themice lived. But when the strains were injected together, the mice died as if thedead bacteria had become suddenly lethal again.12 (See Fig. 3.4.)
It was almost too strange to believe. It was like those old zombie movies,
where the walking dead attack and convert nice ordinary people into killingmachines. Avery wanted to get to the bottom of this strange phenomenon. His
laboratory started by taking the mice out of the equation. The scientists
prepared a rich medium for the bacteria, then placed the two strains ofbacteria—the living harmless Pneumococci bacteria along with the once
lethal but now dead strain—into direct contact with each other in the richmedium. After several life cycles, Avery was able to begin detecting living
versions of the lethal, but previously dead strain.
There were two possibilities. Either the dead strain was coming back tolife—but this was absurd—or something in the dead strain of bacteria wasbeing transferred to the living strain, making it suddenly lethal. With thecomplicating variable of the mice out of the picture, the way was clear for
Avery to locate the culprit; he set out to isolate the material responsible forthis surprising transformation. In 1944, Avery and two of his colleagues,
Colin MacLeod and Maclyn McCarty, published their findings in the Journalof Experimental Medicine. What was the transforming agent? To everyone’s
surprise, including Avery’s, it appeared to be Levene’s seemingly
uninteresting nucleic acid, DNA. DNA from the dead strain was beingtransferred to the living strain, rendering the once harmless living strain
suddenly lethal.13

Figure 3.4. Frederick Griffith’s injected mice experiment.
When Erwin Chargaff, of Columbia University, read Avery’s paper, he
immediately sensed its importance. He saw “in dark contours the beginning ofa grammar of biology,” he recounted. “Avery gave us the first text of a new
language or rather he showed us where to look for it. I resolved to search forthis text.”14
Chargaff’s experimental work eventually provided additional evidence
that DNA could be the source of biological variability. It also provided an
important clue about the structure of the DNA molecule. Chargaff purified
samples of DNA and then separated its chemical constituents, the sugars, thephosphates, and the four bases. Using techniques of quantitative chemical
analysis, he determined the relative proportions of each of these constituents
and, in the process, discovered a puzzling regularity. The quantity of adenine
always equaled the quantity of thymine, while the quantity of the guanine
always equaled the quantity of cytosine.15
This discovery was puzzling, in part because Chargaff also discovered asurprising irregularity—one that contradicted the earlier “tetranucleotide
hypothesis” of Levene. Chargaff discovered that individual nucleotide
frequencies actually differ between species, even if they often hold constantwithin the same species or within the same organs or tissues of a single
organism.16 More important, Chargaff recognized that even for nucleic acids

with the same proportion of the four bases (A, T, C, and G), “enormous”
numbers of variations in sequence were possible. As he put it, different DNA
molecules or parts of DNA molecules might “differ from each other…in thesequence, [though] not the proportion, of their constituents.”17
In other words, a strand of DNA might be like a strand of binary computercode. The information-rich string of zeroes and ones will have a completely
irregular and nonrepetitive sequence of the two characters, but given a fairly
long string of the binary code, one can expect to find very close to the same
number of zeroes as ones. Thus Chargaff argued that, contrary to thetetranucleotide hypothesis, base sequencing in DNA might well display thehigh degree of variability and irregularity required by any potential carrier ofheredity.18 And so, by the late 1940s many had begun to suspect that DNA
was a good candidate for transmitting hereditary information. But still, no one
knew how it did this.
Unlikely Heroes
James Watson crossed an ocean to find out. As a twenty-three-year-old who
had once spent much of his spare time bird watching, he did not appear, on
casual inspection, to possess either the experience or the fire to solve themystery. Yet there was more to the seemingly easygoing young man than met
the eye. Watson was a former Chicago Quiz Kid game-show prodigy who hadentered the University of Chicago at the tender age of fifteen. By nineteen, he
had finished his bachelor’s degree in biology. By twenty-two, he had earned a
Ph.D. from the University of Indiana under Salvador Luria, an expert on thegenetics of viruses. Watson’s doctoral studies focused on viral genetics, but
along the way he learned a fair amount of biochemistry and radiation
genetics. At one point, he took a course from Hermann J. Muller, of fruit fly
fame. Though he “aced” the course, Watson concluded that the best days forgene-radiation studies were past. New methods were needed to get DNA to
divulge its secrets.19
After he graduated, Watson was almost constantly ruminating about
biology and keeping his ears open for any word about new ways to study
DNA. He traveled to Copenhagen for postdoctoral research. While there, he
performed experiments alongside Danish scientist Ole Maaløe thatstrengthened his growing sense that DNA, not protein, was the carrier ofgenetic information. Then in the spring of 1951, at a conference in Naples on
X-ray crystallography, he met Maurice Wilkins, the head of the lab at King’s
College, London. From their conversations, Watson was seized with the ideaof moving to Cambridge, England, where various experts were gathering todiscover the secret of heredity.20

He landed a position with the Cavendish Laboratory in 1951 under theAustrian Max Perutz and Englishman William Lawrence Bragg, both
distinguished experts in the use of X rays to study large biological molecules.
Watson quickly formed a partnership with Francis Crick, a theoreticalphysicist who knew precious little chemistry, but who had used advanced
mathematics to develop theoretical insights about how to study the structureof proteins by using X rays.21 In Crick, Watson found a partner who shared
his thinking about DNA. Both men were interested in genetics, but boththought that a deeper understanding of heredity would emerge only afterscientists understood “what the genes were and what they did.”22 For Watson,at least, that meant understanding the structure of DNA. And he soon
convinced Crick that cracking this problem would make it possible tounderstand the transmission of genetic information. Crick’s expertise inunderstanding protein structure and X-ray imaging techniques would come inhandy, as would his knack for taking insights from disparate fields andfinding significant patterns in them that other, more specialized scientists
missed.
Watson and Crick also possessed an important quality rarely appreciatedin scientists, but vital to those attempting to make discoveries or challenge an
outmoded framework of thought. The two men were perfectly willing to ask
questions exposing their own ignorance, to embarrass themselves, if
necessary, in pursuit of answers.23 They had no world-class reputations to risk
and little to lose as they pursued answers unfettered by concerns about theirown respectability. Nor could they be shamed into pouring their energies into
gathering original data when what was needed was new thinking. They would
leave data gathering to others while they focused on the big picture,
constantly reassembling the pieces of a growing puzzle in search of an elegant
and illuminative synthesis.24
In 1951, after only a few months working on the problem, Watson andCrick presented their first model in a seminar at the Cavendish Laboratory in
Cambridge. Maurice Wilkins, Rosalind Franklin, and two other scientists
from King’s College, London, attended, as did Professor Lawrence Bragg,
their supervisor at the Cavendish. The meeting went poorly. Watson and Crick
represented DNA as a triple-stranded helix. Franklin objected. Though thesugar-phosphate backbone of the molecule might form a helix, she insisted,
there was not yet “a shred of evidence” for this idea from X-ray studies.25
Such ideas were merely in the air as the result of recent discoveries about thepresence of helical structures in proteins.
Other aspects of their model were more obviously mistaken. Watson had
miscalculated the density of water in the DNA molecule. (The amount ofwater absorbed by DNA determines its dimensions and whether it will adopt

one of two structures, the “A-form” or “B-form.”) When the correct water
density was used to calculate the spatial dimensions of DNA, the justification
for their model dissolved. Watson and Crick also had placed the sugar
phosphate backbone in the center of the molecule with the bases sticking
outward, thus producing a structure that looked something like a gnarled treewith stubby branches. Franklin correctly pointed out that DNA could take upwater as easily as it did only if the phosphate molecules were on the outside,
not the inside, of the structure. The phosphate groups had to be on the outsideof the model where they could attract and hold water easily.26
Embarrassed for them, if not for himself, Professor Bragg asked Watson
and Crick to stop work on DNA. Crick was to finish his Ph.D. dissertation;
Watson was assigned to study viruses.
Collecting the Clues
Undaunted by their failure, the two gradually wormed their way back into theaction. Together they mused and snooped, tinkered with toy models, andpicked the brains of the various specialists in England and abroad. By 1952 a
growing number of scientists had set aside the distraction of protein
molecules and were focusing squarely on deoxyribonucleic acid, including
two-time Nobel laureate Linus Pauling, the Caltech chemist who had earlier
determined the form of an important structure within proteins, the alpha-helix.
Watson and Crick sensed that time was short. At any moment somebody
might beat them to the discovery of the structure of the mystery molecule. Butthat fear raised an exhilarating possibility: perhaps collectively the scientificcommunity knew enough already. Perhaps it wasn’t fresh evidence that wasneeded, but rather a flash of insight about how all the pieces of evidence fit
together.
While others approached the problem methodically, steadily gathering
data in their labs, Watson and Crick behaved more like gumshoe detectives,
heading from one place to another looking for clues to help them weigh themerits of competing hypotheses. Then on January 28, 1953, Watson procured
a draft copy of a scientific manuscript written by Linus Pauling from
Pauling’s son, Peter, who was working in Cambridge as a Ph.D. student underJohn Kendrew.27 The senior Pauling was proposing a triple-helix structure for
DNA that was very similar to the model Watson and Crick had proposed theyear before. He sent the draft to Peter, who showed it to Watson and Crick.
Like Watson and Crick’s original model, Pauling’s envisioned a triplesugar-phosphate backbone running up the middle (or inside) of the moleculewith the nucleotide bases attached on the outside and pointing outward.
Watson exhaled in relief. He knew all too well that Pauling’s model couldn’t

be right. But what was? Two days later, with Pauling’s manuscript in hand, he
traveled to the lab at the University of London to see what he could turn up.28
The King’s College lab was the place to go to see the best current X-ray
images of DNA.
Although the lab employed a cutting-edge technology, conceptually the
essence of its technique was a simple old trick from physics: throw somethingat an object and see what bounces back or passes through. Then collect theresulting signal and see what you can tell about the object under study byanalyzing it. Bats navigate the airways using this technique. Their echolocation system sends sound waves at objects and then times the returning
signals so the bats can “see,” or locate the objects around them. As noted in
Chapter 1, geophysicists use a similar technique. They send sound waves
deep underground and then collect the resulting echoes in order to image thesubsurface of the earth.
The key technology in the search for the structure of DNA employed avariation of this strategy as well. Instead of projecting sound waves at DNA,the scientists at King’s College directed X rays at fibers of DNA. And insteadof analyzing what bounced back, they collected the rays that passed throughthe molecules. By seeing how the direction of the X rays changed—how they
were diffracted by their target—these scientists were eventually able to learn
about the structure of DNA.
Rosalind Franklin was the acknowledged expert on this technique forstudying DNA. That’s why Watson was there. Franklin had earlier discoveredthat DNA has two distinct forms with different dimensions depending upon
whether water is present. This in itself was a huge breakthrough, sincepreviously the two forms were jumbled together, muddying the results
anytime someone tried to use X-ray diffraction to discern the form of DNA.
Armed with this fresh insight, Franklin set about developing a method forseparating the two forms. The technique she arrived at was highly demanding,but also highly effective. Now she could and did obtain revealing diffraction
patterns of the “B-form DNA.” When Watson showed up at King’s lab, he
had a tense conversation with Franklin. He lectured her about helical theory
and why DNA must be a helix. Franklin insisted that there was no proof ofthat yet. Franklin rose in anger from behind her lab bench, visibly annoyed at
Watson’s presumption and condescension. Watson made a hasty retreat fromFranklin’s lab, later saying he feared that she might strike him.29

Figure 3.5. Portrait of Rosalind Franklin (1920–58). Courtesy of Science
Source/Photo Researchers.
But before leaving the lab, Watson stopped by to see Maurice Wilkins.
After a little prodding, Wilkins gave Watson a look at Franklin’s best X ray of
DNA in the B-form. The picture showed, quite distinctly, a pattern known as a
Maltese cross (see Fig. 3.6). Watson was elated. What Crick had taught him
told him he was looking at evidence of a helix. On the train ride home,
Watson sketched the image from memory.30 Upon seeing Watson’s sketch andafter questioning Watson, Crick agreed it must be a helix. But what kind, andhow did the chemical constituents of DNA fit into this structure?
Clues gleaned from another quarter would ultimately help Watson andCrick answer these questions. The year before, they had shared a meal withthe gruff, brilliant chemist Erwin Chargaff while he was visiting in
Cambridge. During the meal they asked a number of questions that exposed
their own ignorance of some of the relevant chemistry. In particular, it became
apparent during the meal that they did not know about Chargaff’s famouscorrespondences, or “rules,” establishing that the amount of guanine in DNA
equaled the amount of cytosine (G = C) and the amount of adenine equaled
the amount of thymine (A = T).31 The eminent biochemist, who at the timeknew virtually everything there was to know about the chemistry of DNA,
was aghast that this breezy, ambitious pair didn’t know these basics. Chargaff
even got Crick to admit that he did not know the differences in the chemical
structure of the four nucleotides bases in the DNA molecule, which by thenwas common knowledge to everyone else in the race.32

Figure 3.6. Maltese cross X-ray image of DNA crystal. Helix on the right
represents the structure that scientists think produces the Maltese cross X-ray
image.
Chargaff would later muse about the irony of these seemingly uninitiatedscientists making the discovery of the ages: “I seem to have missed the shiverof recognition of a historical moment; a change in the rhythm of theheartbeats of biology. As far I could make out, they wanted, unencumbered byany knowledge of the chemistry involved, to fit DNA into a helix. I do notremember whether I was actually shown their scale model of a polynucleotide
chain, but I do not believe so, since they still were unfamiliar with thechemical structures of the nucleotides.”33 Despite his contempt for thisambitious pair of know-nothings, or perhaps because of it, Chargaff passed on
to them the correspondences he had discovered.34
So by the time Watson returned from his visit to Franklin and Wilkins at
the end of January 1953, he and Crick knew several key facts about nucleic
acid. In addition to Chargaff’s rules, they knew from Franklin’s X-ray images
that DNA almost certainly did form a helix with a backbone made of sugars
linked to phosphates. From X-ray studies of the molecule, they also knew the
key dimensions of the B-form of the DNA molecule—20 angstroms acrossand 34 angstroms long for one full turn of the helix (an angstrom is the lengthof one hydrogen atom, about one ten-billionth of a meter). And they knewthat Franklin was convinced that the sugar-phosphate backbone would have tohave the phosphates on the outside.35
They also knew they had competition. Watson’s trip to London no doubt
had been partly motivated by his serendipitous acquisition of Linus Pauling’s
manuscript. Though Watson was enormously relieved that Pauling had
blundered, he suspected that it would not take long for Pauling to discover his
mistakes. Franklin also knew, immediately upon reading Pauling’s proposal,that it was wrong. Pauling had proposed a structure in which the sugar-phosphate strands of the helix ran through the center of the molecule. Thus,
his model required the bases to stick out horizontally from the rising helices.
This meant that the molecule would not define a smooth or definite edge, butinstead a jagged series of knobby protrusions. Yet Franklin knew that the Xray data showed that the molecule had a definite 20 angstrom diameter. Sucha precise measurement could never have been established if the molecule had
the kind of uneven edge Pauling envisioned.36
Pauling’s model also failed to account for Chargaff’s correspondences.
And it had density problems. With three helices running right through the
middle of the molecule, the density of the atoms in the center was too high to
be consistent with available data. X-ray and chemical studies had revealed thenumber of bases (about 10) present in the molecule per turn of a single helix.
A triple helix should, therefore, have a proportionately higher density of bases
per unit length than a double helix. When Watson discovered that density
measurements (bases per unit length of a fiber of DNA) agreed more closelywith the values calculated for a double helix than a triple helix, he and Cricknot only rejected Pauling’s triple-helix model, but all triple helices. DNA,
they were convinced, was much more likely to be some sort of double helix.37
They were much closer to a solution, but so too were the others in the race.
Watson and Crick sensed that time was short, and they had spacing problemsof their own.
Models and Molecules
To solve the puzzle, Watson began in haste to build a series of models. He
first tried to make a double helix with the helices again running up the centerof the molecule, ignoring Franklin’s earlier insistence about the need forkeeping the phosphates exposed on the outside of the molecule. Crick recallsthat they persisted in this approach because they thought the demands of
biology required it. If DNA was, indeed, the molecule of hereditary
information and the arrangement of bases conveyed genetic instructions, thenthe structure of DNA must allow some way of copying these instructions.
Putting the bases on the outside made it easier to envision how the bases were
copied during cell division. Additionally, placing them on the outside of themolecule eliminated the need to figure out how the bases fit together within
the strands. “As long as the bases were on the outside, we didn’t have toworry about how to pack them in,” said Crick.38
Unfortunately, an outside placement made it more difficult to explain
other facts. First, a jagged bases-on-the-outside model would not produce
consistent 20-angstrom-diameter X-ray measurements—a point Franklin had
made earlier. Also, positioning the bases on the outside of the sugar-phosphate backbone made it difficult to explain how DNA could form crystals
at all. Crystals are solidified chemical structures that form a highly regularstructure called a lattice as the result of a repeating chemical structure. DNA
crystals form when long strands of (A-form) DNA molecules alignthemselves alongside each other. To get a visual image of what a crystal of
DNA would look like, imagine a series of parallel lanes at a drag racing trackwith each lane of equal width. Now instead of those lanes being perfectly
straight, imagine them curving back and forth in parallel to each other all thewhile maintaining that even spacing.
The jagged edged molecule Watson envisioned in his bases-on-theoutside model was not conducive to the formation of regularly spaced,
parallel strands of DNA. The jagged edges might stick together, but only in an
irregular tongue-in-groove sort of way. Yet the ability to study DNA structurewith X rays is possible only because of the regular spacing DNA molecules
exhibit when aligned in parallel.
Placing the bases on the outside and the backbone on the inside also madeit more difficult to produce a structure that matched known measurements of
the length between the bases. X-ray measurements indicated that there areabout 3.4 angstroms of space between each base on the backbone. But
running the backbone up the middle of the molecule, as opposed to along theoutside edge, generates a helix with a steeper pitch (angle of ascent). Watsonsoon found that the pitch of his internal backbone model created too muchspace between the sugar groups (and thus the bases) to match the 3.4angstrom measurement derived from X-ray studies. As Watson worked withthe model, he also realized that the chemical properties of the backbone
limited how much it could be compressed or stretched to accommodate
definite spacing requirements. Further, as historian of science Robert Olby
recounts, the structure as a whole looked “awful.”39
A change of direction was clearly needed. Watson began to try models
with the backbones on the outside. Initially, things went no better. But then
two breakthroughs came.
“Aha” Times Two
The first breakthrough came as a flash of insight to Francis Crick. As Watson
began trying to fit the bases into the interior of the helices, leaving the
backbone on the outside of the model molecule, he still encountereddifficulties. Watson didn’t know, however, that how he oriented the two
sugar-phosphate backbones in relation to each other would matter. He knewthat there is a structural asymmetry in the construction of the backbone of the
DNA molecule. The phosphate groups attach to a different carbon molecule

on one side of the ribose sugar than they do on the other. On one side of thering structure of the sugar, the phosphate attaches to what is called its 5'
carbon (five prime carbon); on the other, the phosphate attaches to what iscalled its 3' carbon (see Fig. 3.7). Chemists know these differences well, andso did Watson.
But as Watson was constructing his models, he had assumed that the two
backbones should run in the same direction, parallel to each other with eachbackbone starting from the 5' end and running to the 3' end of the chain. So
try as he might, he could not get the bases to lock into place—to connect witheach other—in a stable structure across the molecule. Then a crucial insightcame.

Figure 3.7. The sugar phosphate backbone of the DNA showing how
phosphates are bonded to the 5' carbon of one sugar and the 3' carbon ofanother. The genetic text (sequence of bases) is transcribed in the 5' to 3'
direction of the DNA molecule.
Crick, who was studying the structure of proteins in his Ph.D.
dissertation, was familiar with helical structures in proteins. He knew thatsometimes sections of the alpha helices of a protein will run “antiparallel” toeach other. That is, one section of a helix in a protein will run up, then turnback the other direction and run down right alongside the first section of thehelix, making an upside-down “U-shape.” One day while reading a highlytechnical description of the X-ray diffraction pattern of the B-form of DNA ina Medical Research Council report summarizing Rosalind Franklin’s data,40
Crick realized that the pattern she was describing—called a “monoclinic C2
symmetry” pattern—was indicative of an antiparallel double helix structure.
Instead of the two strands running from 5' to 3' in the same direction, Crickrealized that Franklin’s data indicted that one helix was running up in the 5' to
3' direction and the other strand was running down in the 5' to 3' direction theother way. He also realized by studying the dimensions indicated by the X-ray
data that these antiparallel backbones were not separate molecules or separate
stretches of the same molecule bent back on itself as occurred in proteins.
Instead, these were two separate strands entwined around each other. They
were simply too close together to be anything else (see Fig. 3.8).

Figure 3.8. Antiparallel strands of DNA entwined around each other.
Represented in two dimensions (left) and three dimensions (right).
Crick’s insight was possible only because of his specialized expertise in
crystallography. At the time, it was nearly beyond Watson to understand
Crick’s argument, and he apparently took quite a long time to get it. But once
he did and once he started using antiparallel sugar-phosphate backbones, hismodel building would proceed in an entirely more satisfying and fruitful
direction—though he would first need to have a flash of insight of his own.
Eureka!
Watson resumed his model building in late February 1953 using cardboard
cutouts of his own construction as he waited for the metal pieces to comefrom the machine shop.41 The challenge he faced was to find a way to fit
these four irregularly shaped bases into the available space between the twotwisting sugar-phosphate backbones and to do so in a way that was consistent
with the chemical properties of each of the bases and the sugar molecules towhich the bases must attach. He had studied every scientific paper he could
about the dimensions and chemical proclivities of the bases. Watson learned
that the bases often form a weak kind of chemical bond with each other calleda hydrogen bond. Perhaps he could bridge the gap between the two backbones
with two bases connected by hydrogen bonds. This seemed plausible. But
there was a complicating factor. The bases weren’t the same size. Two of thebases, adenine and guanine, called the purines, exhibited a large double-ring
structure, while the two others, cytosine and thymine, called pyrimidines,
exhibited a smaller single-ring structure (see Fig. 3.9).
Watson tried to pair the two similar bases with each other. He found thathe could get them to join together by forming double hydrogen bonds.
Perhaps these like-with-like base pairs could bridge the gap between the two
backbones. Indeed, he could envision them forming rungs on the twistingladder.

Figure 3.9. The basic chemical structures of purines and pyrimidines. Noticethat pyrimidines have a single-ring structure and purines have a doublering
structure. The bases adenine and guanine are purines. Thymine and cytosine
are pyrimidines. Since complementary base pairing always involves one
pyrimidine and one purine, the rungs on the DNA ladder formed by thesepairs are closely equivalent in size and shape, whether they are adeninethymine pairs or guanine-cytosine pairs.
But there were problems. Watson recognized that if he used the like-withlike pairs, he would have a hard time producing a helix with a consistent
diameter. The pairs of large purine bases would cause the helix to bulge insome places, while the pairs of the smaller pyrimidines would cause it tonarrow in others. And there was another difficulty. Lab associate Jerry
Donohue, who knew a lot about the chemical structure and properties of thenitrogenous bases, insisted that Watson was mistaken.42 He was. Most of thenitrogenous bases come in either one of two variant forms, a common “keto”
form and a rare “enol” form. These two forms have slightly different
structural formulas, reflecting slight differences in the configurations of theirconstituent atoms. For three of the four bases, Watson had unknowingly used
the rare alternative form of the bases, instead of the common “keto” form thatis actually present in DNA. Deferring to Donohue’s expertise, Watson tried
again using the correct (keto) forms of the bases. But this made hydrogenbonding much more difficult for like-with-like pairings.43
That’s when Watson’s insight came. He remembers that it occurred themorning of February 28, 1953. Still waiting for the tin models to arrive fromthe Cavendish shop, he cleared the papers off the desktop and, constructing a
makeshift set of cardboard models, he began trying to fit the pieces together
this way and that. Initially, he reverted to his “like-with-like prejudices,” butvery quickly he realized this was a dead end. He began to try other pairing
schemes: “Suddenly I became aware that an adenine-thymine pair held
together by two hydrogen bonds was identical in shape to a guanine-cytosine
pair held together by at least two hydrogen bonds. All the hydrogen bonds
seemed to form naturally; no fudging was required to make the two types ofbase pairs identical in shape.”44
The AT pair formed a rung across the helix that was the exact length andshape of the GC pair. He placed these pairs inside the two backbones. Now
the twisting ladder maintained a consistent diameter. No bulging. No
pinching. Everything fit.
The Double Helix
Watson now saw the double helix rising before him and marveled at how“two irregular sequences of bases could be regularly packed in the center of ahelix.”45 This new structure also explained Chargaff’s rules. If A bonded only
to T and if C bonded only to G, then of course the amount of A would always
equal T and the amount of C would always equal G. It was also so easy to see
the two strands parting and new As, Ts, Gs, and Cs tumbling into place,seeking out hydrogen bonds with their natural opposites on the single strands.
Soon each single strand would be its own double strand and, presto, thewinding staircase structure would replicate itself. Crick arrived at the lab andquickly discovered that Watson’s revised structure explained several othersignificant facts of DNA chemistry as well.46
The new model was beautiful and fit the data exceptionally well. Cricklater observed that he and Watson “were looking for gold” and by “blundering
about” had found the gold they sought.47 At lunch, Crick was telling everyone
at the Eagle pub, around the corner from the Cavendish on Free School Lane

in Cambridge, that they had found the secret of life.48 On April 25, 1953, a
seemingly modest paper appeared in the journal Nature. The article was only
nine hundred words long, was signed by a pair of unknowns, J. D. Watson
and F. H. C. Crick, and featured the anodyne title “Molecular Structure of
Nucleic Acids: A Structure for Deoxyribose Nucleic Acid.” It revolutionizedbiology.

Figure 3.10. Watson and Crick presenting their model of the DNA double
helix. Courtesy of A. Barrington Brown/Photo Researchers, Inc.
The Watson-Crick model made it clear that DNA had an impressive
chemical and structural complexity. It was a very long molecule composed on
the outside of a regular arrangement of sugar and phosphate molecules. But
on the inside it could contain many potentially different arrangements of thefour bases. Thus, it had an impressive potential for variability and complexityof sequence as required by any potential carrier of hereditary information. As
Watson and Crick later explained: “The phosphate-sugar backbone of ourmodel is completely regular, but any sequence of the pairs of bases can fit
into the structure. It follows that in a long molecule, many different
permutations are possible, and it therefore seems likely that the precise
sequence of the bases is the code which carries the genetic information.”49

Thus, their paper not only described the structure of DNA; it also anticipatedwhat later discoveries would confirm: DNA was a repository of information.
Watson and Crick’s discovery would forever change our understanding of
the nature of life. At the close of the nineteenth century, most biologists
thought life consisted solely of matter and energy. But after Watson andCrick, biologists came to recognize the importance of a third fundamental
entity in living things: information. And this discovery would redefine, fromthat point forward, what theories about the origin of life would need to
explain.

4
Signature in the Cell

In Darwin’s time few, if any, biologists talked about biological or genetic
information, but today they routinely refer to DNA, RNA, and proteins as
carriers or repositories of information. Biologists tell us that DNA stores andtransmits “genetic information,” that it expresses a “genetic message,” that it
stores “assembly instructions,” a “genetic blueprint,” or “digital code.”1
Biology has entered its own information age, and scientists seeking to explain
the origin of life have taken note. Life does not consist of just matter andenergy, but also information. Since matter and energy were around long
before life, this third aspect of living systems has now taken center stage. At
some point in the history of the universe, biological information came into
existence. But how? Theories that claim to explain the origin of the first life
must answer this question.
But what exactly is information? What is biological information?
Beginning in the late 1940s, mathematicians and computer scientists began todefine, study, measure, and quantify information. But they made distinctions
between several distinct types or conceptions of information. What kind of
information does DNA have? What kind of information must origin-of-liferesearchers “explain the origin of”?
As we will see, it is important to answer these questions because DNA
contains a particular kind of information, one that only deepens the mysterysurrounding its origin.
Defining Information: Two Distinctions
Most of us use the term “information” to describe some piece of knowledge.
When we say so-and-so passed on some interesting information, we mean that
so-and-so told us something that we didn’t know before, but that we now
know, thanks to what we were told. In other words, information equals
knowledge. The first definition of information in Webster’s dictionary reflects
this idea: information is “the communication or reception of knowledge or
intelligence.” Because many of my students had this idea of information
firmly in mind, they were often confused at first when I talked about
information stored in a molecule. There is a sense in which it could be said

that DNA stores the “know-how” for building molecules in the cell. Yet sinceneither DNA nor the cellular machinery that receives its instruction set is a
conscious agent, equating biological information with knowledge in this way
didn’t seem to quite fit.
But our English dictionaries point to another common meaning of theterm that does apply to DNA. Webster’s, for instance, has a second definition
that defines information as “the attribute inherent in and communicated byalternative sequences or arrangements of something that produce specificeffects.” Information, according to this definition, equals an arrangement or
string of characters, specifically one that accomplishes a particular outcome
or performs a communication function. Thus, in common usage, we refer not
only to a sequence of English letters in a sentence, but also to a block ofbinary code in a software program as information. Information, in this sense,
does not require a conscious recipient of a message; it merely refers to asequence of characters that produces some specific effect. This definition
suggests a definite sense in which DNA contains information. DNA contains“alternative sequences” of nucleotide bases and can produce a specific effect.
Of course, neither DNA nor the cellular machinery that uses its information isconscious. But neither is a paragraph in a book or a section of software (or the
hardware in the computer that “reads” it). Yet clearly software contains a kindof information.
This seemed reasonably clear to me in 1985 after I first began to think
about the DNA enigma. But at the time something else puzzled me. I had
been working in a technical field in which the processing of information on
computers was part of our stock and trade. I was familiar with the science of
information storage, processing, and transmission called “information
theory.” Information theory was developed in the 1940s by a young MIT
engineer and mathematician named Claude Shannon. Shannon was studying
an obscure branch of algebra and, not surprisingly, few people were paying
any attention. He would later gain notoriety as the inventor of the rocketpowered Frisbee and for juggling four balls while cruising down the halls of
Bell Laboratories on a unicycle, but in 1937 he was putting the finishing
touches on a master’s thesis that, to some, may have seemed hopelessly dull.
Shannon had taken nineteenth-century mathematician George Boole’s systemof putting logical expressions in mathematical form and applied its categories
of “true” and “false” to switches found in electronic circuits.

Figure 4.1. Portrait of the U.S. mathematician and information theorist Claude
Shannon (1916–2001). Courtesy of the Estate of Francis Bello/Photo
Researchers, Inc.
“I’ve always pursued my interests without much regard to financial value
or value to the world,” Shannon confessed in 1983. “I’ve spent lots of time on
totally useless things.”2 His master’s thesis, however, wasn’t one of them.
Called “possibly the most important, and also the most famous, master’s
thesis of the century,”3 it eventually became the foundation for digital-circuit
and digital-computer theory. Nor was he finished laying these foundations. He
continued to develop his ideas and eventually published “The Mathematical
Theory of Communications.” Scientific American later called it “the Magna
Carta of the information age.”4Shannon’s theory of information provided a set of mathematical rules foranalyzing how symbols and characters are transmitted across communication
channels. After the conference in 1985 awakened my interest in the origin oflife, I began to read more about Shannon’s theory of information. I learned
that his mathematical theory of information applied to DNA, but there was acatch.
Shannon’s Information Theory
Shannon’s theory of information was based upon a fundamental intuition:
information and uncertainty are inversely related. The more informative astatement is, the more uncertainty it reduces.
Consider this illustration. I live in Seattle, where it rains a lot, especiallyin November. If someone were to tell me that it will rain in November, thatwould not be a very informative statement. I am virtually certain that it willrain then. Saying so does not reduce uncertainty. On the other hand, I know
very little about what the weather will be like in Seattle on May 18. I’m
uncertain about it. If a time-traveling weather forecaster with a flawless trackrecord were to tell me that on May 18 next year Seattle will have an
unseasonably cold day resulting in a light dusting of snow, that would be an
informative statement. It would tell me something I couldn’t have predictedbased upon what I know already—it would reduce my uncertainty about
Seattle weather on that day.
Claude Shannon wanted to develop a theory that could quantify theamount of information stored in or conveyed across a communication
channel. He did this first by linking the concepts of information anduncertainty and then by linking these concepts to measures of probability.
According to Shannon, the amount of information conveyed (and theamount of uncertainty reduced) in a series of symbols or characters isinversely proportional to the probability of a particular event, symbol, or
character occurring. Imagine rolling a six-sided die. Now think about flippinga coin. The die comes up on the number 6. The coin lands on tails. Before
rolling the die, there were six possible outcomes. Before flipping the coin,
there were two possible outcomes. The cast of the die thus eliminated more
uncertainty and, in Shannon’s theory, conveyed more information than thecoin toss. Notice here that the more improbable event (the die coming up 6)
conveys more information. By equating information with the reduction of
uncertainty, Shannon’s theory implied a mathematical relationship between
information and probability. Specifically, it showed that the amount of
information conveyed by an event is inversely proportional to the probabilityof its occurrence. The greater the number of possibilities, the greater the
improbability of any one being actualized, and thus the more information thatis transmitted when a particular possibility occurs.
Shannon’s theory also implied that information increases as a sequence ofcharacters grows, just as we might expect. The probability of getting heads ina single flip of a fair coin is 1 in 2. The probability of getting four heads in arow is 1/2 × 1/2 × 1/2 × 1/2, that is, (1/2)4 or 1/16. Thus, the probability of
attaining a specific sequence of heads and tails decreases as the number oftrials increases. And the quantity of information increases correspondingly.5
This makes sense. A paragraph contains more information than the individual
sentences of which it is composed; a sentence contains more information than
the individual words in the sentence. All other things being equal, shortsequences have less information than long sequences. Shannon’s theory
explained why in mathematical terms: improbabilities multiply as the number

of characters (and combination of possibilities) grows.
For Shannon, the important thing was that his theory provided a way ofmeasuring the amount of information in a system of symbols or characters.
And his equations for calculating the amount of information present in acommunication system could be readily applied to any sequence of symbols
or coding system that used elements that functioned like alphabetic
characters. Within any given alphabet of x possible characters (where eachcharacter has an equal chance of occurring), the probability of any one of thecharacters occurring is 1 chance in x. For instance, if a monkey could bangrandomly on a simplified typewriter possessing only keys for the 26 English
letters, and assuming he was a perfectly random little monkey, there would be
1 chance in 26 that he would hit any particular letter at any particular
moment.
The greater the number of characters in an alphabet (the greater the valueof x), the greater the amount of information conveyed by the occurrence of a
specific character in a sequence. In systems where the value of x is known, as
in a code or language, mathematicians can generate precise measures of
information using Shannon’s equations. The greater the number of possible
characters at each site and the longer the sequence of characters, the greater
the Shannon information associated with the sequence.
What Shannon’s Theory Can’t Say
But, as I said, there was a catch. Though Shannon’s theory and his equations
provided a powerful way to measure the amount of information stored in asystem or transmitted across a communication channel, it had important
limits. In particular, Shannon’s theory did not, and could not, distinguishmerely improbable sequences of symbols from those that conveyed a message
or “produced a specific effect”—as Webster’s second definition puts it. As
one of Shannon’s collaborators, Warren Weaver, explained in 1949, “Theword information in this theory is used in a special mathematical sense thatmust not be confused with its ordinary usage. In particular, information mustnot be confused with meaning.”6
Consider two sequences of characters:“Four score and seven years ago”
“nenen ytawoi jll sn mekhdx nnx”
Both of these sequences have an equal number of characters. Since both arecomposed of the same 26-letter English alphabet, the amount of uncertainty
eliminated by each letter (or space) is identical. The probability of producing

each of those two sequences at random is identical. Therefore, both sequences
have an equal amount of information as measured by Shannon’s theory. But
one of these sequences communicates something, while the other does not.
Why?
Clearly, the difference has something to do with the way the letters arearranged. In the first case, the letters are arranged in a precise way to takeadvantage of a preexistent convention or code—that of English vocabulary—in order to communicate something. When Abraham Lincoln wrote the firstwords of the Gettysburg address, he arranged the letters—f-o-u-r—s-c-o-r-e—a-n-d—s-e-v-e-n—y-e-a-r-s—a-g-o—in a specific sequence. When he didso he invoked concepts—the concept of “four,” the concept of “years,” and soon—that had long been associated with specified arrangements of sounds andcharacters among English speakers and writers. The specific arrangement hechose allowed those characters to perform a communication function. In thesecond sequence, however, the letters are not arranged in accord with any
established convention or code and, for that reason, are meaningless. Sinceboth sequences are composed of the same number of equally improbablecharacters, both sequences have a quantifiable amount of information as
calculated by Shannon’s theory. Nevertheless, the first of the two sequences
has something—a specificity of arrangement—that enables it “to produce a
specific effect” or to perform a function, whereas the second does not.
And, that’s the catch. Shannon’s theory cannot distinguish functional ormessage-bearing sequences from random or useless ones. It can only measure
the improbability of the sequence as a whole. It can quantify the amount offunctional or meaningful information that might be present in a given
sequence of symbols or characters, but it cannot determine whether thesequence in question “produces a specific effect.” For this reason, information
scientists often say that Shannon’s theory measures the “information-carrying
capacity,” as opposed to the functionally specified information or“information content,” of a sequence of characters or symbols. This generates
an interesting paradox. Long meaningless sequences of letters can have more
information than shorter meaningful sequences, as measured by Shannon’s
information theory.
All this suggested to me that there are important distinctions to be madewhen talking about information in DNA. In the first place, it’s important todistinguish information defined as “a piece of knowledge known by a person”
from information defined as “a sequence of characters or arrangements ofsomething that produce a specific effect.” Whereas the first of these two
definitions of information doesn’t apply to DNA, the second does. But it isalso necessary to distinguish Shannon information from information that
performs a function or conveys a meaning. We must distinguish sequences ofcharacters that are (a) merely improbable from sequences that are (b)improbable and also specifically arranged so as to perform a function. That is,
we must distinguish information-carrying capacity from functional
information.
So what kind of information does DNA possess, Shannon information orsome other? To answer this question we will need to look at what molecularbiologists have discovered since 1953 about the role of DNA within theminiature world of the cell.
Genetic Information: What Does It Do?
In the wake of Watson and Crick’s seminal 1953 paper, scientists soon
realized that DNA could store an immense amount of information. The
chemistry of the molecule allowed any one of the bases to attach to any one of
the sugar molecules in the backbone, allowing the kind of variable sequencingthat any carrier of genetic information must have. Further, the weak hydrogenbonds that held the two antiparallel strands together suggested a way themolecule might unzip—like two opposing strips of Velcro—to allow the
exposed sequence of bases to be copied. It seemed that DNA was ideal forstoring information-rich sequences of chemical characters. But how DNA
expressed this information and how it might be using that information
remained uncertain. Answers would soon come, however, thanks todevelopments in the field of protein chemistry.
The Puzzle of Proteins
Scientists today know that protein molecules perform most of the critical
functions in the cell. Proteins build cellular machines and structures, they
carry and deliver cellular materials, and they catalyze chemical reactions thatthe cell needs to stay alive. Proteins also process genetic information. To
accomplish this critical work, a typical cell uses thousands of different kindsof proteins. And each protein has a distinctive shape related to its function,
just as the different tools in a carpenter’s toolbox have different shapes related
to their functions.
By the 1890s, biochemists had begun to recognize the centrality ofproteins to the maintenance of life. They knew proteins were heavy (“highmolecular weight”) molecules that were involved in many of the chemical
reactions going on inside cells. During the first half of the twentieth century,
chemists were also able to determine that proteins were made of smallermolecules called amino acids. During this time, many scientists thought that
proteins were so important that they, rather than DNA molecules, were therepositories of genetic information. Even so, until the 1950s, scientists

repeatedly underestimated the complexity of proteins.
One reason for this mistaken view was a discovery made by adistinguished British scientist. William Astbury was an outstanding scientist
with impeccable credentials. Formerly chosen “head boy” at a distinguished
English boarding school, Astbury had studied physics at Cambridge during
and after World War I. He then worked with William Bragg, the pioneering
X-ray crystallographer whose son Lawrence later supervised Watson andCrick. Astbury had an infectious enthusiasm for laboratory work and a gift for
persuasion. But he also had a reputation for sometimes letting his enthusiasm
get the better of him and for jumping to premature conclusions.
Astbury was convinced that proteins held the key to understanding life.
And partly for that reason, he thought they should exhibit a simple, regularstructure that could be described by a mathematical equation or some generallaw. Perhaps his occupation biased him toward this view. As acrystallographer, he was used to studying highly regular and orderly
structures. For example, salt crystals, the first structures determined using Xray techniques, have a highly repetitive or regular structure of sodium andchlorine atoms arranged in a three-dimensional grid, a pattern in which one
type of atom always has six of the other types surrounding it (see Fig. 4.2).
Astbury was convinced that proteins—the secret of life—should exhibit asimilar regularity. During the 1930s he made a discovery that seemed toconfirm his expectation.
Astbury used X rays to determine the molecular structure of a fibrous
protein called keratin, the key structural protein in hair and skin.7 Astbury
discovered that keratin exhibits a simple, repetitive molecular structure, withthe same pattern of amino acids repeating over and over again—just like therepeating chemical elements in a crystal (or the repeating pattern of bases that
P. A. Levene had mistakenly proposed as a structure for DNA). Astbury
concluded that all proteins, including the mysterious globular proteins soimportant to life, would exhibit the same basic pattern he discovered in
keratin. Many of Astbury’s contemporaries shared the same view. In 1937, forexample, two leading American biochemists, Max Bergmann and Carl
Niemann, of the Rockefeller Institute, argued that the amino acids in all
proteins occurred in regular, mathematically expressible proportions.8

Figure 4.2. Three-dimensional structure of a salt crystal. Sodium atoms arerepresented by the light-colored balls; the chlorine atoms by the dark-coloredballs.
During the early 1950s, about the same time that Watson and Crick wereon the hunt to solve the structure of DNA, another Cambridge scientist madea discovery that would challenge the dominant view of proteins. While
working just a couple miles from Watson and Crick at the now famousLaboratory for Molecular Biology (or LMB), biochemist Fred Sanger
determined the structure of the protein molecule insulin. Sanger’s discovery
would later earn him the first of two Nobel prizes in chemistry. Sanger
showed that insulin consisted of irregular sequences of various amino acids,
rather like a string of differently colored beads arranged with no discernible orrepeating pattern.9 Subsequent work on other proteins would show the samething: the sequencing of amino acids is usually highly irregular and defies
description by any general rule.10
But old prejudices die hard. Many biologists at the time still expected
proteins, considered by many the fundamental unit of life, to exhibit
regularity, if not in the arrangement of their amino acids, then at least in theiroverall three-dimensional shapes or structures. Most thought the threedimensional structures of proteins would end up exhibiting some sort ofgeometric regularity. Some imagined that insulin and hemoglobin proteins,for example, would look like “bundles of parallel rods.”11 As Johns Hopkins
biophysicist George Rose recounts, “Protein structure was a scientific terra
incognita. With scant evidence to go on, biologists pictured proteins vaguely
as featureless ellipsoids: spheres, cigars, Kaiser rolls.”12Then came the publication of John Kendrew’s paper. Kendrew was theson of academics, an Oxford scientist and an art historian. He studied
chemistry at Cambridge and graduated in 1939 just as World War II was
beginning. After doing research on radar technology during the war, he took
up the study of molecular biology at the Medical Research Council
Laboratory in Cambridge in 1946. There Kendrew began to work closely withMax Perutz, the Austrian crystallographer who, along with Lawrence Bragg,
had officially supervised Watson and Crick. In 1958, Kendrew made his own
contribution to the molecular biological revolution when he published a paperon the three-dimensional structure of the protein myoglobin.
“The report in Nature,” historian Horace Judson recalls, “was marked by
a comical tone of surprise, of eyebrows raised at eccentricity.”13 Far from thesimple structure that biologists had imagined, Kendrew’s work revealed an
extraordinarily complex and irregular three-dimensional shape, a twisting,turning, tangled chain of amino acids. Whereas protein scientists had
anticipated that proteins would manifest the kind of regular order present incrystals (see Fig. 4.2), they found instead the complex three-dimensional
structure shown in Figure 4.3. As Kendrew put it, with characteristic British
understatement, “The big surprise was that it was so irregular.”14 In the
Nature paper, he wrote, “Perhaps the most remarkable features of themolecule are its complexity and its lack of symmetry. The arrangement seems
to be almost totally lacking in the kind of regularities which one instinctively
anticipates, and it is more complicated than has been predicted by any theoryof protein structure.”15

Figure 4.3. The complex three-dimensional structure of the protein
myoglobin.
But biochemists soon recognized that proteins exhibited anotherremarkable property. In addition to their complex shapes and irregulararrangements of amino acids, proteins also exhibit specificity. By specificity,
biologists mean that a molecule has some features that have to be what they
are, within fine tolerances, for the molecule to perform an important functionin the cell.
Proteins are specified in two ways. First, proteins display a specificity ofshape. The strangely irregular shapes of proteins that Kendrew and others
discovered turned out to be essential to the function of the proteins. In
particular, the three-dimensional shape of a protein gives it a hand-in-glove fit
with other equally specified and complex molecules or with simpler
substrates, enabling it to catalyze specific chemical reactions or to build
specific structures within the cell. Because of its three-dimensional
specificity, one protein cannot usually substitute for another. A topoisomerase
can no more perform the job of a polymerase than a hatchet can perform thefunction of soldering iron or a hammer the job of a wrench.
Figure 4.4 illustrates how three-dimensional specificity of fit determines
the function of a protein, in this case, an enzyme. Enzymes are proteins thatcatalyze specific chemical reactions. The figure shows an enzyme called abeta-galactosidase and a two-part sugar molecule (a disaccharide) called
lactose. Because the enzyme’s shape and dimensions exactly conform to theshape and dimensions of the disaccharide molecule, the sugar can nestle into
the pockets of the enzyme. Once it does, a chemically active part of the
enzyme, called an active site, causes a chemical reaction to occur. The
reaction breaks the chemical bonds holding the two parts of the sugar together
and liberates two individual molecules of glucose, each of which the cell canuse easily.
Consider another example of how the specific shape of proteins allows
them to perform specific functions. The eukaryotic cell has an uncanny wayof storing the information in DNA in a highly compact way. (Eukaryotes arecells that contain a nucleus and other membrane-bound organelles;
prokaryotic cells lack these features.) Strands of DNA are wrapped aroundspool-like structures called nucleosomes. These nucleosomes are made ofproteins called histones. And, again, it is the specific shape of the histone
proteins that enables them to do their job. Histones 3 and 4, for example, foldinto well-defined three-dimensional shapes with a precise distribution of
positive electrical charges around their exteriors. This precise shape andcharge distribution enables DNA strands to coil efficiently around thenucleosome spools and store an immense amount of information in a verysmall space.16 Thanks in part to nucleosome spooling, the information storage
density of DNA is many times that of our most advanced silicon chips.17

Figure 4.4. An enzyme (ß-D-galactosidase) breaking apart a two-part sugar
molecule (a disaccharide). Notice the tight three-dimensional specificity of fit
between the enzyme and the disaccharide at the active site where the reaction
takes place. Notice also that the active site is small in relation to the size ofthe enzyme as a whole.
To visualize how this works, imagine a large wooden spool with grooves
on the surface. Next, picture a helical cord made of two strands. Thenvisualize wrapping the cord around the spool so that it lies exactly in thehollowed-out grooves of the spool. Finally, imagine the grooves hollowed insuch a way that they exactly fit the shape of the coiled cord—thicker parts
nestling into deeper grooves, thinner parts into shallower ones. The
irregularities in the shape of the cord exactly match irregularities in thehollow grooves. In the case of nucleosomes, instead of grooves in the spool,
there is an uncanny distribution of positively charged regions on the surfaceof the histone proteins that exactly matches the negatively charged regions of
the double-stranded DNA that coils around it.18 (See Fig. 4.5.)
But proteins have a second type of specificity—one that helps to explain
the first. Proteins do not just display a specificity of shape; they also display a
specificity of arrangement. Whereas proteins are built from rather simple
amino-acid “building blocks,” their various functions depend crucially on thespecific arrangement of those building blocks. The specific sequence ofamino acids in a chain and the resulting chemical interactions between aminoacids largely determine the specific three-dimensional structure that the chain
as a whole will adopt. Those structures or shapes determine what function, if
any, the aminoacid chain can perform in the cell—whether as an enzyme,
structural component, or a machine for processing information.19

Figure 4.5. A highly specific pattern of matching positive and negative
charges allows information-rich DNA to wrap tightly around histone proteinsin a process known as nucleosome spooling. Chromosomes are made of DNA
spooled on histones.
When I was teaching college students, I used to illustrate this relationshipbetween amino-acid sequencing and protein structure and function with achildren’s toy called Snap-Lock Beads. Snap-Lock Beads are made of plastic
and come in many colors and shapes. Each bead has a hole on one end and aplug on the other that allows the beads to be snapped together to form a long
chain. In my illustration, the individual beads with their distinctive shapes
represent the twenty different protein-forming amino acids; the chain of beads
represents a potential protein; and the connection point between the beads
represents a chemical bond. As I snapped the different-shaped beads together,
I would explain to my students that proteins are formed from amino acids
linked together by a specific kind of chemical bond called a peptide bond. As
the amino acids “snap” together, the resulting chains are called polypeptides.
If one of these polypeptide chains folds into a specific shape that enables it toperform tasks within the cell, it is called a protein.
But that, it turns out, is a big “if.” Only if amino acids are arranged invery specific ways will the chain fold into useful shapes or conformations.
Individual amino acids have distinguishing features (called side chains) thatexert forces on each other. The resulting constellation of forces will cause thechain as a whole to twist and turn and, sometimes, fold into a stable structure.
Most amino-acid arrangements, however, produce chains that do not fold into
stable structures at all. Other arrangements generate stable structures, but do
not perform a function. Relatively few sequences will produce functional
structures. In each case, it is the arrangement of the amino acids that
determines the difference. Specificity of sequence distinguishes proteins fromordinary, useless polypeptides.
I would explain the importance of this specificity to my students byarranging and rearranging my Snap-Lock Beads. I would twist the chain toshow how one sequence might cause the chain to fold into a specific threedimensional shape. I would then rearrange the beads to show how a different
sequence and constellation of forces might cause the chain to collapse into a
limp or amorphous jumble. I emphasized that proteins have a type of“specificity”—called “sequence specificity”—that needed to be explained. A
system or sequence of characters manifests “sequence specificity” if thefunction of the system as a whole depends upon the specific arrangement of
the parts. Language has this property. Software has this property. And so too
do proteins.
The revolution in molecular biology produced some exciting andsurprising discoveries about proteins and raised an intriguing question. If thestructure of proteins depends upon the specific arrangement of its aminoacids, what determines the arrangement of the amino acids?
The Sequence Hypothesis
Discoveries in the 1950s showed that both DNA and proteins are long, linear
molecules composed of many irregularly arranged subunits of smallermolecules. Could these two otherwise very different kinds of molecules beconnected in some important, but as yet undiscovered way?
Following Sanger’s discovery, most molecular biologists assumed that
proteins could not assemble themselves from free-floating amino acids in thecell. Too many processes in the cell depend upon particular proteins having
just the right shape and sequence of amino acids to leave the assembly ofthese molecules to chance. Nor did it seem likely that some general chemical
law or uniform force of attraction could explain the assembly of theseirregular and oddly shaped structures. Instead, as the French molecularbiologist Jacques Monod would later recall, molecular biologists began tolook for some source of information or “specificity” within the cell that could
direct the construction of these highly specific and complex molecules.20
Watson and Crick’s seminal discovery suggested a source for that
information or “specificity.” Perhaps it lay along the spine of DNA’s sugarphosphate backbone.21 But what did the information in DNA have to do withthe specific arrangements of amino acids in proteins? In 1958, Francis Crick
proposed an answer in what came to be called the “sequence hypothesis.”22
The sequence hypothesis suggested that the nucleotide bases in DNA
functioned just like alphabetic letters in an English text or binary digits insoftware or a machine code. According to Crick’s hypothesis, it is the precise
arrangement or sequence of these bases that determines the arrangement ofamino acids—which, in turn, determines protein folding and structure. In
other words, the sequence specificity of amino acids in proteins derives froma prior specificity of arrangement in the nucleotide bases on the DNA
molecule.23
Crick’s sequence hypothesis was not the kind of hypothesis that could be
confirmed by a single experiment. Nor was it based upon a single discovery.
Rather, it capped a series of other discoveries and insights and would require
many more experiments and discoveries to confirm. It was also a naturalextension of what was already known, or at least suspected.
Since the early 1940s, following the publication of a research paper by
American geneticists George Beadle and Edward Tatum, many geneticists had
begun to suspect a link between genes and proteins even before it was clearthat genes were made of DNA. Beadle and Tatum had been studying fruit-fly
eyes and bread molds. They used X rays to induce mutations in thechromosomal material of these organisms. They discovered that thesemutations often had a discernable effect on their features. In the case of abread mold called Neurospora, Beadle and Tatum discovered that the mutant
forms of mold were missing an essential chemical compound—a compoundthat was produced with the help of a particular enzyme. Beadle and Tatum
proposed that the mutated gene was normally responsible for synthesizing the

enzyme in question, but that the mutations in the gene had destroyed its
ability to do so. They further suggested that it is the job of genes generally to
produce enzymes; in fact, they suggested that, as a rule, one gene produces
one enzyme.24
Crick’s sequence hypothesis was built on this presumed linkage between
genes and proteins and on other recent insights, including Avery’s
identification of DNA as the likely carrier of genetic information, Sanger’s
discovery of the irregularity and linearity of amino-acid sequences, andWatson and Crick’s own understanding of the chemical structure of DNA. If asequence of bases constitutes a gene, if genes direct protein synthesis, and if
specific sequences of amino acids constitute proteins, then perhaps the
specific sequencing of bases determines the specific sequencing of aminoacids. By 1958, this seemed to Crick to be far more than just a reasonablesupposition. The sequence hypothesis explained many of the central facts thenin currency among the elite circle of British, American, and French molecularbiologists trying to decipher exactly what DNA does.
Even so, confirming Crick’s sequence hypothesis would eventually
involve at least two additional kinds of experimental results. In a narrow
sense, confirmation of the sequence hypothesis meant establishing thatchanges in genes made a difference in the amino-acid sequence of proteins.25
By the early 1960s, scientists had developed many techniques for studying theeffects of changes in DNA sequences on proteins. These techniques enabledscientists to establish a definitive link between base sequences in DNA andthe sequences of amino acids. Experiments using such techniques eventually
revealed a set of correspondences between specific groups of bases andindividual amino acids. These correspondences came to be called the “geneticcode” (see Fig. 4.6).

Figure 4.6. The standard genetic code showing the specific amino acids thatDNA base triplets specify after they are transcribed and translated during gene
expression.
In a broader sense, however, the confirmation of the sequence hypothesis
would await the full explication of what is now called the gene-expression
system—the system by which proteins are constructed from the information
in DNA. It was one thing to know that there is a correlation between specificbases sequences in DNA and specific amino acids in proteins. It was quite
another to learn how that linkage is produced. Nevertheless, by the early
1960s, a series of experiments established how the information in DNA
produces proteins and, in the process, provided a deeper and morecomprehensive confirmation of Crick’s bold hypothesis.26
In the next chapter, I describe the intricate process of gene expression in
more detail. Here I simply give a brief sketch of what scientists discovered
about how the process works, since knowing that will enable me to answer
the question I raised about the nature of biological information at the
beginning of this chapter.
Gene expression begins as long chains of nucleotide bases are copied
during a process known as “transcription.” During this process, the genetic
assembly instructions stored on a strand of DNA are reproduced on anothermolecule called “messenger RNA” (or mRNA). The resulting single-stranded
copy or “transcript” contains a sequence of RNA bases precisely matching thesequence of bases on the original DNA strand.27 (RNA also uses chemicals

called bases to store genetic information, but it uses a slightly different
chemical alphabet than DNA. RNA substitutes a base called uracil for thebase thymine used in the DNA.)
After it is produced, the messenger-RNA molecule travels to theribosome, a molecular machine that helps translate the mRNA assembly
instructions. These instructions consist of a series of three-letter genetic“words” called “codons.” Each codon consists of three bases and directs thecell to attach a specific amino acid to a growing chain of other amino acids.
For example, the mRNA word UUA directs the ribosome to attach the aminoacid leucine, whereas AGA specifies the amino acid arginine. Other codons
direct the ribosome to start or stop building proteins. This translation process
occurs with the aid of specific adapter molecules (called transfer RNAs, ortRNAs) and specific enzymes (called aminoacyl-tRNA synthetases).28 (SeeFig. 4.7. To view an animation of this process of gene expression, see thisbook’s Web site at www.signatureinthecell.com.)
By the way, the elucidation of the gene-expression system shed further
light on the experiment with the dead mice discussed in the last chapter.
Oswald Avery correctly determined it was DNA, not bacteria living or dead,that caused the mice to die. But when scientists established that DNA directs
protein synthesis, Avery’s results were viewed in a new light. DNA caused themice to die, all right, but it did so by directing the production of proteins thatwere toxic to the mice (though, obviously, not to the bacteria from which the
DNA was derived).

Figure 4.7. A simplified schematic of gene expression showing bothtranscription and translation. (The enzymes called tRNA synthetases are notpictured, although they are essential to the process.) Courtesy of I. L. Cohen
of New Research Publications.
In any case, with the explication of gene expression complete, Francis
Crick’s sequence hypothesis was formally confirmed. But what kind of
information does DNA contain? The discovery that DNA directs protein
synthesis enabled molecular biologists working in the two decades after theformulation of Shannon’s theory to answer this question.
Shannon Information or Shannon Plus?
A parable may help to illustrate a crucial distinction and help define some key
terms we will need throughout the rest of this book. Mr. Smith has promised
to pick up Mr. Jones at Reagan National Airport in Washington when hereturns from Chicago, but he has asked Jones to call just after he arrives.
When he steps off the plane, Jones reaches into his pocket for Smith’s number
in order to make the call. He finds that he has lost the slip of paper with thenumber on it. Nevertheless, Jones doesn’t panic. He knows that all longdistance phone numbers have ten characters and are constructed from thesame ten digits. He writes down the digits 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 on anotherpiece of paper. He gives a sigh of relief. Now he has the raw materials, thebuilding blocks, to generate Smith’s ten-digit phone number.
He quickly arranges the digits on his list at random into a sequence of ten
characters. Now he has some information, some Shannon information. Being
mathematically inclined, he quickly calculates how much information he has
using Shannon’s familiar formula. He’s relieved and impressed. His new tendigit phone number has a lot of information—33.2 bits to be exact. He calls
the number. Unfortunately, he has a problem. The sequence of characters is
not arranged “to produce a specific effect.” Instead of reaching Smith, he getsa recording from the phone company: “The number you have dialed is not inservice…”
After a while Mr. Smith gets worried. He hasn’t received the call from
Jones that he was expecting. But then he remembers that he has some
information that can help him reach Jones. He has Jones’s cell phone number.
It also has ten digits. These digits are arranged in a definite sequence: 202–
555–0973. He enters them in his own cell phone and presses the call button.
Jones answers and tells Smith of his arrival and location. They connect, andthere is a happy ending.
In our illustration, both Smith and Jones have an equally improbablesequence of ten characters. The chance of getting either sequence at random isthe same: 1/10 × 1/10 × 1/10 × 1/10 × 1/10 × 1/10 × 1/10 × 1/10 × 1/10 ×
1/10, or 1 chance in 1010. Both sequences, therefore, have informationcarrying capacity, or Shannon information, and both have an equal amount of
it as measured by Shannon’s theory. Clearly, however, there is an importantdifference between the two sequences. Smith’s number is arranged in aparticular way so as to produce a specific effect, namely, ringing Jones’s cell
phone, whereas Jones’s number is not. Thus, Smith’s number contains
specified information or functional information, whereas Jones’s does not;Smith’s number has information content, whereas Jones’s number has only
information-carrying capacity (or Shannon information).
Both Smith’s and Jones’s sequences are also complex. Complex sequences
exhibit an irregular, nonrepeating arrangement that defies expression by ageneral law or computer algorithm (an algorithm is a set of instructions foraccomplishing a specific task or mathematical operation). The opposite of acomplex sequence is a highly ordered sequence like ABCABCABCABC, inwhich the characters or constituents repeat over and over due to someunderlying rule, algorithm, or general law.
Information theorists say that repetitive sequences are compressible,
whereas complex sequences are not. To be compressible means a sequencecan be expressed in a shorter form or generated by a shorter number ofcharacters. For example, a computer programmer could write two commands
that would generate a 300-character sequence of repeating ABCs simply by
writing the commands “write ABC” and then “repeat 100 times.” In this case,a simple rule or algorithm could generate this exact sequence without eachindividual character having to be specified. Complex sequences, however,cannot be compressed to, or expressed by, a shorter sequence or set of coding
instructions. (Or rather, to be more precise, the complexity of a sequence
reflects the extent to which it cannot be compressed.)
Information scientists typically equate “complexity” with “improbability,”
whereas they regard repetitive or redundant sequences as highly probable.
This makes sense. If you know that there is a reason that the same thing will
happen over and over again, you are not surprised when that thing happens;nor will you regard it as improbable when it does. Based upon what we know
about the way the solar system works, it is very probable that the sun will rise
tomorrow. If you know that a computer is executing a command to “repeat”—
to generate the same sequence over and over again—then it is highly probablethat the sequence will arise. Since information and improbability are inversely
related, high-probability repeating sequences like
ABCABCABCABCABCABC have very little information (either carrying
capacity or content). And this makes sense too. Once you have seen the first
triad of ABCs, the rest are “redundant”; they convey nothing new. They aren’tinformative. Such sequences aren’t complex either. Why? A short algorithm
or set of commands could easily generate a long sequence of repeating ABCs,
making the sequence compressible.
In our parable, both Smith and Jones have ten-digit numbers that arecomplex; neither sequence can be compressed (or expressed) using a simpler
rule. But Smith’s sequence was specifically arranged to perform a function,
whereas Jones’s was not. For this reason, Smith’s sequence exhibits what has
been called specified complexity, while Jones’s exhibits mere complexity. The
term specified complexity is, therefore, a synonym for specified information
or information content. (See Fig. 4.8.)

Figure 4.8. Examples of order, complexity, and specified complexity.
So what kind of information does DNA contain, Shannon information or
specified information? Mere complexity or specified complexity? The answer
is—both.
First, DNA certainly does have a quantifiable amount of informationcarrying capacity as measured by Shannon’s theory. Since DNA contains theassembly instructions for building proteins, the gene-expression system of thecell functions as a communication channel.29 Further, the nucleotide bases
function as alphabetic characters within that system. This enables scientists tocalculate the information-carrying capacity of DNA using Shannon’s
equations. Since, at any given site along the DNA backbone, any one of four
nucleotide bases may occur with equal ease,30 the probability of theoccurrence of a specific nucleotide at that site equals 1/4. For the occurrenceof two particular nucleotide bases, the odds are 1/4 × 1/4. For three, 1/4 × 1/4
× 1/4, or 1/64, or (1/4),3 and so on.31 The information-carrying capacity of asequence of a specific length n can then be calculated using Shannon’sfamiliar expression (I =–log2p) once one computes a probability value (p) for
the occurrence of a particular sequence n nucleotides long where p = (1/4)n.
The p value thus yields a corresponding measure of information-carrying
capacity or syntactic information for a sequence of n nucleotide bases.
Just as mathematicians and engineers can apply Shannon’s theory toanalyze a written text, a cryptographic transmission, or a section of software,mathematical biologists can apply the theory to analyze the informationcarrying capacity of a DNA, RNA, or protein molecule. That is what Charles
Thaxton meant when he told me in Dallas in 1985 that the treatment of DNA
and English text is “mathematically identical.” Both systems of symbols or
characters can be analyzed the same way. As I write this sentence, theplacement of each additional letter eliminates twenty-five other possible
letters and a corresponding amount of uncertainty. It, therefore, increases theinformation of the sentence by a quantifiable amount as measured by
Shannon’s theory. Similarly, at each site along the DNA molecule any one of
the four bases is possible. Thus, the placement or presence of any one of thebases eliminates uncertainty and conveys a quantifiable amount of
information according to Shannon’s theory.
Is this significant? In several ways, it is. It is certainly remarkable thatDNA can carry or encode information using chemical subunits that function
as alphabetic characters. It is also remarkable that DNA forms part of acommunication channel that can be analyzed so readily using themathematical tools of Shannon’s information theory. Further, scientists haveapplied Shannon’s information theory to generate quantitative measures of theinformation-carrying capacity (or brute complexity) of DNA sequences andtheir corresponding proteins. These analyses have shown that these molecules
are highly complex, and quantifiably so. DNA, RNA, and proteins have atremendous capacity, at least, to store and transmit information.
Nevertheless, the ease with which information theory applies to molecularbiology has also created some confusion. The sequences of nucleotide bases
in DNA and the sequences of amino acids in proteins are highly improbableand, therefore, have large information-carrying capacities. Knowing this,some scientists have mistakenly described DNA and proteins as if they
contained only Shannon information or possessed mere information-carrying
capacity.
Yet, like meaningful sentences, lines of computer code, or the phone
number that Smith used to ring Jones’s cell phone, the arrangements of bases
in DNA and amino acids in proteins are also specified with respect tofunction. This judgment is a direct consequence of the experimental
verification of Francis Crick’s sequence hypothesis. Indeed, since theconfirmation of the sequence hypothesis in the early 1960s, biologists have
known that the ability of the cell to build functional proteins depends upon the
precise sequential arrangement of the bases in DNA. Thus, molecularbiologists beginning with Francis Crick have equated biological information
not only with improbability (or complexity), but also with “specificity,”
where “specificity” or “specified” has meant “necessary to function.”32
Thus, in addition to a quantifiable amount of Shannon information (orcomplexity), DNA also contains information in the sense of Webster’s second
definition: it contains “alternative sequences or arrangements of somethingthat produce a specific effect.” Although DNA does not convey informationthat is received, understood, or used by a conscious mind, it does haveinformation that is received and used by the cell’s machinery to build thestructures critical to the maintenance of life. DNA displays a property—functional specificity—that transcends the merely mathematical formalism of
Shannon’s theory.
Is this significant? In fact, it is profoundly mysterious. Apart from themolecules comprising the gene-expression system and machinery of the cell,
sequences or structures exhibiting such specified complexity or specified
information are not found anywhere in the natural—that is, the nonhuman—world. Sequences and structures exhibiting either redundant order or merecomplexity are common in the chemical substrate of nature. But structures
exhibiting specified complexity are completely unknown there apart from
DNA, RNA, and proteins. As the origin-of-life biochemist Leslie Orgelobserves: “Living organisms are distinguished by their specified complexity.
Crystals…fail to qualify as living because they lack complexity; mixtures ofrandom polymers fail to qualify because they lack specificity.”33
Nevertheless, human artifacts and technology—paintings, signs, written text,
spoken language, ancient hieroglyphics, integrated circuits, machine codes,
computer hardware and software—exhibit specified complexity; among
those, software and its encoded sequences of digital characters function in a
way that most closely parallels the base sequences in DNA.
Thus, oddly, at nearly the same time that computer scientists werebeginning to develop machine languages, molecular biologists werediscovering that living cells had been using something akin to machine code34
or software35 all along. To quote the information scientist Hubert Yockey
again, “The genetic code is constructed to confront and solve the problems ofcommunication and recording by the same principles found…in modern
communication and computer codes.”36 Like software, the coding regions of
DNA direct operations within a complex material system via highly variable
and improbable, yet also precisely specified, sequences of chemical
characters. How did these digitally encoded and specifically sequenced

instructions in DNA arise? And how did they arise within a channel fortransmitting information?
Explaining the “Origin of”
As we saw in Chapter 2, early theories of the origin of life did not need toaddress, nor did they anticipate, this problem. Since scientists did not know
about the information-bearing properties of DNA, or how the cell uses thatfunctionally specified information to build proteins, they did not worry about
explaining these features of life. But after 1953 the landscape changed
irrevocably. Any idea of a repetitive, crystalline order at the foundation of life
gave way to a picture of life in which information-rich macromolecules direct
the metabolism and machinery of living things. Origin-of-life researchers
have accordingly turned their attention to this crucially specified form of
information, the origin of which constitutes the central aspect of the DNA
enigma. But before examining how scientists have attempted to explain thismystery, it is important to examine another feature of the informational
system at work within the cell, since there is another facet of the DNA enigma
that scientists investigating the origin of life must now explain.

5
The Molecular Labyrinth

A military surgeon at a primitive field hospital is treating a battalion
commander crucial to the success of a coming campaign. The surgeon needs
to remove shrapnel lodged in the soldier’s internal organs, but to do so
effectively, the surgeon needs some advanced software and imaging
technology unavailable at the field hospital. He radios his superior and tells
him the problem. The supervisor in turn relates the situation to a bureaucrat ata major hospital in Washington, D.C. Unfortunately, the bureaucrat is Mr.
Jones from the previous chapter.
Jones quickly goes to work procuring the necessary software for runningthe diagnostic imaging technology. With it, he boards a military plane andflies to the remote location. He hands the surgeon a high-density storage
device and raises a hand to forestall expressions of gratitude. “Don’t mention
it,” he says. “Solving problems is what I do.”And yet the problem remains. It’s not the software. The information in thefile is error-free and contains precisely the program and instructions needed to
conduct the diagnostic procedure. The problem is the surgeon does not have
either the computer or the operating system that would allow him to use thesoftware. These are built into the diagnostic equipment—the imaging
machine—that the surgeon was missing in the first place. The surgeon has asoftware file that he cannot access, translate, or use. The information isuseless to him. The soldier dies. The war drags on.
This story illustrates what in the realm of human technology andexperience is obvious: encoded information is worthless without a system—
whether mind or machine—that can use, read, translate, or otherwise processthat information. What does this tell us about the DNA enigma?
As scientists began to discover more about how the cell uses theinformation in DNA to build proteins, they realized that DNA is only part of acomplex system for expressing and processing information. They realized thatwithout this whole system, DNA by itself could do nothing.
In the previous chapter, we saw that the cell uses the information stored in
DNA to build proteins. Establishing this enabled us to answer a question
about the kind of information that DNA contains, namely, functionallyspecified information. Nevertheless, the previous chapter did not say muchabout how the cell processes the information in DNA or how scientists cameto learn about it. This is worth looking at more closely because it turns outthat the cell’s information-processing system is itself one of the key featuresof life that any theory of the origin of life must explain.
Unlike the unfortunate surgeon in our opening story, the cell does have asystem for processing stored information. As for how it works: the key insightcame when Francis Crick realized that the cell must be using a code totranslate information from one form to another and that this code wasmediated by an interdependent system of molecules. This insight raised anage-old question: the question of the chicken and the egg.
Deducing a Code
By the late 1950s the relationship between DNA and protein was coming into
focus. By then leading molecular biologists understood that the threedimensional specificity of proteins depended upon the one-dimensional
specificity of their amino-acid sequences. They also suspected that specificarrangements of amino acids in protein chains derived in turn from specificsequences of nucleotide bases on the DNA molecule. Yet the question
remained: How does the sequence of bases on the DNA direct theconstruction of protein molecules? How do specific sequences in a fourcharacter alphabet generate specific sequences in a twenty-characteralphabet?
Francis Crick again anticipated the answer: the cell is using some kind ofa code. Crick first began to suspect this as he reflected on a proposal by
George Gamow, a Russian-born theoretical physicist and cosmologist who
had, in the postwar years, turned some of his prodigious intellectual powers toreflect on new discoveries in molecular biology.
Gamow had immigrated to the United States in the 1930s to take an
appointment at George Washington University after working at the famed
Theoretical Physics Institute in Copenhagen.1 In 1953 and 1954, he proposeda model to explain how the specific sequences in DNA generate the specificsequences in proteins.2 According to Gamow’s “direct template model,” as it
was called, protein assembly occurred directly on the DNA strand. Gamow
proposed that proteins formed as amino acids attached directly to the DNA
molecule at regularly spaced intervals. Gamow thought the amino acids could
nestle into diamond-shaped cavities in DNA that formed in the space between
the two backbones (see Fig. 5.1). In this model, a group of four nucleotide
bases from two parallel strands of DNA made a specifically shaped hollow
into which one and only one amino acid could fit.3 As each group of
nucleotides acquired an amino-acid partner, the amino acids would also linkwith each other to form a chain. According to Gamow, the nearly identicalspacing of amino acids in protein and of bases in DNA enabled the direct“matching” of amino acids to nucleotide groups on the DNA template. This
matching occurred because of the supposed fit between the cavities producedby the bases and the shape of the side chain of the amino acids and thechemical affinity between the bases and amino acids.

Figure 5.1. A simplified version of George Gamow’s own drawing depicting
his direct template model of protein synthesis. The diamonds represent places
where he thought various twenty amino acids would bond to the DNA double
helix. The circles represent one of the four nucleotide bases. Gamow thoughtthat the amino acids would nestle into the grooves between the nucleotide
bases and that the structure of the DNA molecule would somehow determine
where individual amino acids would bond, thereby directly determining
protein sequencing.
Francis Crick first recognized the futility of this scheme. In a famous
communication to members of the “RNA Tie Club,” Crick explained that
there was nothing about either the chemical properties or shapes of the bases
to ensure that one and only one amino acid would fit into, or attach to, thecavities created by a group of bases.4 In the first place, many of the aminoacids were difficult to differentiate structurally, because they had similar side
chains. Second, the bases themselves did not necessarily create shapes thateither matched these shapes or, still less, differentiated one from another. As
Crick put it, “Where are the nobby hydrophobic surfaces to distinguish valinefrom leucine from isoleucine? Where are the charged groups, in specificpositions, to go with the acidic and basic amino acids?”5 As Judson explains,“Crick was a protein crystallographer, and knew of no reason to think thatGamow’s holes in the helix could provide the variety or precision of shapes
necessary to differentiate a score or more of rather similar objects.”6
Yet in the absence of such spatial matching and the formation ofcorresponding chemical attachments, there could be no reliable transmissionof information. For the direct template model to explain the irregularity andspecificity of the amino-acid sequences in proteins, individual bases (orgroups of bases) needed to manifest discriminating spatial geometries.

Figuratively speaking, DNA bases not only needed to “zig” in a way thatmatched the specific “zags” of each amino acid, but DNA needed to do so at
irregular intervals in order to produce the irregular sequencing of amino acids
that characterizes proteins. Yet as Crick realized, both the individual bases
themselves and their various combinations lacked distinguishing physicalfeatures that could account for the specificity of amino-acid sequencing.
Further, the geometry of the DNA molecule as a whole, at the level of its
gross morphology, presents a highly repetitive series of major and minor
grooves (see Fig. 5.2). Therefore, it could not function as a direct template forprotein synthesis. As Crick explained, “What the DNA structure does show…
is a specific pattern of hydrogen bonds, and very little else.”7

Figure 5.2. The large-scale form of the DNA helix showing its major andminor grooves.
If the chemical features and shapes of the DNA bases do not directly
account for the specific sequencing of proteins, what does? Crick remainedadamant that the specific arrangement of the nucleotide bases, not anything
about their physical or chemical features per se, dictated amino acidsequencing.8
Crick’s insight had profound implications. If a single protein could notcopy the information in DNA directly, as the direct template model suggested,
then, as Jacques Monod would later explain, “you absolutely needed a code.”9
And so Crick postulated a third factor consistent with his original sequence
hypothesis. He proposed the existence of a genetic code—a means oftranslating information from one chemical domain into another.
To envision what Crick had in mind, imagine having a human alphabetthat uses four and only four distinct shapes that combine in various specificways to form not just words, but words that correspond to individual letters ina larger alphabet roughly the size of the English alphabet; this larger alphabet
then uses its letters (each of which is one of those words composed of the four
shapes) to build sentences. Of course, we actually have a symbol system that
does much the same thing. The binary code that computer programs use has atranslation key that enables programmers to produce English text fromsequences of binary digits. Each letter in the English alphabet is represented

by a unique combination of two character types, 0’s and 1’s. For example, in
ASCII code the letter A is represented by the sequence 100 0001, the letter B
by the sequence 100 0010, and so on. Each of the letters of the twenty-sixletter English alphabet has a corresponding representation in the two-digit
numeric alphabet of this binary system (see Fig. 5.3).10 Crick realized that if
his sequence hypothesis was true, then there must be some similar translation
system in the cell—one that determined how sequences written in the fourcharacter alphabet of DNA are converted into sequences that use a twentyletter amino-acid alphabet. The DNA detective was now in the business ofcode breaking as well.

Figure 5.3. Part of the ASCII code.
Yet in a physical system, as opposed to a social or linguistic one, a code
must have a physical expression. Crick postulated the existence of a third
molecule, an adapter molecule functioning as a translating device that could
recognize and convert the information in the sequential arrangements of thebases into specific amino-acid sequences.11 More specifically, he proposed
the existence of twenty separate adapter molecules corresponding to each of
the twenty protein-forming amino acids. Each adapter would, by the familiar
mechanism of complementary base pairing, bind to a section of DNA text at
one end and to a specific amino acid at the other. Crick also proposed theexistence of specific enzymes (one for each of the twenty adapter–amino acidpairs) to connect the specific amino acids and their corresponding adapters.12
The set of correspondences between sections of genetic text, on the one hand,and a specific amino acid, on the other, constituted a genetic code (see Fig.
4.6).
Though these correspondences were mediated physically by adapter
molecules and enzymes, this complex system, as conceived by Crick, would

be governed as much by the functional requirements of information transfer
as by rules of chemical affinity—as much by a set of chemically arbitrary
conventions as by the necessary relations of physical-chemical law. Indeed, as
Crick imagined this system, nothing about the physical or chemical featuresof the nucleotides or amino acids directly dictated any particular set of
assignments between amino acids and bases in the DNA text. The code could
not be deduced from the chemical properties of amino acids and nucleotide
bases. It had to be cracked. Just as a specific letter of the English language
can be represented by any combination of binary digits, so too could a given
amino acid correspond to any combination of nucleotide bases. The
assignments are, in both cases, arbitrary. For this reason, the progressive
elimination of the many chemically possible types of codes would eventually
prove a laborious task. Yet precisely this feature of chemical
underdetermination would also later prove the key to understanding DNA’s
information-carrying capacity. As Judson explains, “Crick freed the nucleic
acid template of its most stringent physical limitation, allowing it to bethought about formally”13 as an information carrier rather than merely achemical substance.
Crick’s proposal was striking in its shear theoretical audacity.
Biochemistry had not a shred of direct evidence for the existence of adapter
molecules or their corresponding enzymes. Crick simply deduced the need fora code by thinking about what would be needed to make the cell’scommunication system work. Only a code could facilitate the translation of
the information from DNA’s four-character base sequences into the twentycharacter “language” of proteins. His adapter hypothesis followed logically,
and intuitively, from an understanding of the functional requirements of
information transfer and the limited informational capacity of the chemical
constituents of the relevant molecules themselves.
Simple regular geometries plus rules of chemical affinity did not andcould not generate the specific complexity present in functional proteins. But
complex combinations of a few physically simple bases functioning as
characters or symbols could specify a vast array of possible structures, just as
only two symbols, when variously arranged, can specify many characters bymeans of the ASCII code. Yet if the sequential arrangement—and nothingelse—about the bases dictated protein structure, then the cell needed a meansof translating and expressing the information stored on DNA. The cell neededa code, albeit one physically instantiated as part of an integrated system fortranslating and expressing genetic information. And so Crick conceived the“adapter hypothesis”—described by Judson as “the postulation, fromtheoretical necessity, of a new biochemical entity.”14
What Crick would postulate on the grounds of functional necessity tooknearly five years of intensive research and many transatlantic communications
and conferences to verify and elucidate. But during the late 1950s and early
1960s, a series of experiments enabled scientists to show how the information
in DNA directs protein synthesis. First, Paul Zamecnik, a researcher at
Massachusetts General Hospital, discovered that proteins were produced incellular structures called ribosomes, located in the outer cytoplasm “far” fromthe nucleus where DNA resides.15 This discovery suggested the need formechanisms to transcribe, transport, and translate the information in DNA sothat amino-acid chains could be constructed at these sites. Because Zamecnik
had discovered that ribosomes contained no DNA, Francis Crick, along with
two other molecular biologists, François Jacob and Sydney Brenner,suggested that another type of molecule, a copy of the original DNA gene,actually directed the synthesis of proteins at the ribosomes. They proposedthat this “messenger” molecule was made of RNA, or ribonucleic acid. Soon
Brenner and others found this messenger RNA operating as he, Crick, andJacob had anticipated.16
About the same time, Severo Ochoa, a Spanish-born physician working at
the New York University Medical School, and Marshall Nirenburg, an
American at the National Institutes of Health in Maryland, performedexperiments that enabled scientists to decipher the genetic code.17 Ochoa
identified an enzyme that enabled him to synthesize RNA molecules.
Nirenburg used Ochoa’s technique to make a synthetic form of messenger
RNA that he then used to direct protein synthesis.18 Together their work
showed that groups of three nucleotides (called codons) on the mRNA specify
the addition of one of the twenty protein-forming amino acids during theprocess of protein synthesis. Other scientists discovered that the cell uses a setof adapter molecules to help convert the information on mRNA into proteinsjust as Crick expected.19 Indeed, Crick’s adapter molecules and theircorresponding enzymes functioned much as he had originally envisioned,
albeit as part of a far more complex process than even he had foreseen.
“CAD-CAM” and Protein Synthesis
In April 2008, a film called Expelled: No Intelligence Allowed, starring theHollywood comic actor Ben Stein, was released in theaters all over the UnitedStates. Part of the film was about the current scientific controversy over theorigin of life. When the producers came to our offices to plan interviews, they
told us they wanted to find a way to represent what DNA does visually, sothat a general audience could follow the scientific discussion they planned toincorporate into the film. They commissioned a visually stunning threedimensional animation of DNA and the inner workings of the cell andretained a team of molecular biologists to work closely with the animators.
When the film opened in theaters, audiences were impressed by the beauty of
the animation and the intricacy of the processes that it depicted. The
producers have kindly made some of their animation, including some that
didn’t make it into the film, available to me at www.signatureinthecell.com.
This animation beautifully depicts the process by which the information in
DNA directs the synthesis of proteins. I recommend viewing it in tandem withthe discussion that follows.
Though the animation in Expelled provided a visual representation of how
the cell uses information to direct its manufacturing operations, the film
producers thought it might also help audiences understand the process if they
could see footage of an analogous manufacturing procedure used in manyindustries. “Computer-assisted design and manufacturing,” or “CAD-CAM”
for short, uses digital information to manufacture various machines andproducts, from airplanes to automobiles to garage doors.20
At the Boeing plant in Seattle, engineers use CAD-CAM to direct the
production of many key parts of airplanes.21 A CAD program is used todesign some part of an airplane, such as a wing. Engineers provide
specifications that enable the CAD program to produce a three-dimensional
drawing, a visual display of the specific part. The engineers can examine thisvisual image to check that they have achieved their design objectives. The
CAD program also stores the information for producing this visual display inbinary code. In the CAD-CAM process, the digital information stored in the
CAD program is then transferred to another computer program, called an NC(“numerical code”) interface. This program then translates the instructions
from the CAD program into a machine code. This machine code then directs
the manufacturing machinery—a robotic rivet arm, for example—to make theparts of the airplane.
In the film, the producers showed a manufacturing plant operating with
CAD-CAM technology to illustrate how the cell uses digital code to
manufacture its proteins and protein machines. In fact, the similarities
between the two processes are striking. Like a production facility at Boeing orFord, the cell uses digitally encoded information to direct the manufacture of
the parts of its machines. In addition, the process of gene expression involves
the conversion of information from one digital format to another before theinformation is used in manufacturing. In CAD-CAM, the original digitally
encoded information in the CAD program is translated into another machinecode by the NC interface and then used to direct the manufacture of airplaneparts. In the gene-expression system, the original digital information in DNA
is converted into an RNA format, which then directs the construction ofproteins.

The system is a marvel of sophistication and merits a closer look. Whatfollows is a short tour of the molecular labyrinth, the cell’s sophisticatedinformation-processing system. This will help to clarify what origin-of-liferesearchers have to explain. For those without a background in biology, don’t
worry if some of the details get past you. The details of the process are
fascinating, but the big picture that emerges from this overview is the key.
The cell’s information-processing system is strikingly similar to CAD-CAM
technology, though it differs from CAD-CAM in at least one important
respect: the cell’s information-processing system not only produces machines,
it also reproduces itself.
Gene Expression
Molecular biologists describe the process of protein synthesis, or “geneexpression,” as a two-stage process of information transfer involving manysmaller discrete steps and many molecular machines. This process proceeds
as long chains of nucleotide triplets (the genetic message) are first copied
during a process known as “transcription” and then transported (by themolecular messenger mRNA) to a complex organelle called a ribosome. At
the ribosome site, the genetic message is then “translated” with the aid of asuite of adapter molecules called transfer RNAs to produce growing aminoacid chains—chains that fold into the functional proteins the cell needs tosurvive. Let’s look at each of the two stages of gene expression in turn.
Transcription
The first stage in the process of protein synthesis is called transcription.
During transcription, a copy, or transcript, of the DNA text is made by a large
protein complex, known as RNA polymerase, that moves down the DNA
chain and “reads” the original DNA text. As RNA polymerase proceeds, it
makes an identical copy of the DNA transcript in an RNA format. (Like DNA,RNA contains four chemical bases, called nucleotide bases. These bases are
the same as those in DNA with one exception: RNA uses a base called uracil
instead of thymine.) The resulting single-stranded RNA copy, or transcript,
then moves from the chromosomes to the ribosome in the outer cytoplasm tobegin translation, the next step in the processing of genetic information.22(See Fig. 5.4.)
Transcription can be thus described in a few simple sentences. Yet any
such description conceals an impressive complexity. In the first place, RNApolymerase is an extraordinarily complex protein machine of great specificity.
The RNA polymerases present in the simplest bacteria (Mycoplasma)

comprise several separate protein subunits with (collectively) thousands ofspecifically sequenced amino acids.23
RNA polymerase performs several discrete functions in the process oftranscription. First, it recognizes (and binds to) specific regions of the DNA
that mark the beginning of genes. Second, it unwinds (or helps unwind) the
DNA text, exposing the strand that will serve as the template for making the
RNA copy. Third, it sequesters and positions RNA bases (A, U, G, C) with
their complementary partners on the DNA template (T, A, C, G, respectively).
Fourth, it polymerizes or links together the separate RNA nucleotides, thus
forming a long message-bearing ribbon of mRNA.24 As molecular biologist
Stephen Wolfe explains: “The structure of the RNA polymerases reflects thecomplexity of their activities in RNA transcription. The enzymes have sitesthat recognize promoters, react with initiation, elongation and termination
factors, recognize DNA bases for correct pairing, bind and hydrolyze RNA
nucleotides, form phospho-diester linkages, terminate transcription andperhaps unwind and rewind DNA.”25

Figure 5.4. The process of transcription. The top view shows a view fromoutside the RNA polymerase. The bottom view is a close-up showing (insimplified form) what is happening inside the RNA polymerase.

Yet for all its complexity and specificity, RNA polymerase alone does not
ensure accurate transcription. The process involves several other complex andfunctionally integrated parts and steps. For example, for RNA polymerase toaccess the genetic text, the DNA double helix must unwind and expose thenucleotide bases. Further, to initiate transcription, the RNA polymerase mustbind to the correct part of the DNA sequence in order to begin transcribing at
the beginning of a genetic message, rather than in the middle or at the end.
For its part, the DNA text provides a promoter sequence or signal upstream of
the actual coding sequence to facilitate recognition of the correct location by
the RNA polymerase. Yet the RNA polymerase cannot, on its own, find thissite with any reliability. In prokaryotes (cells without nuclei), a protein known
as sigma factor combines with the core RNA polymerase enzyme (itself acomposite of enzymes) to form a larger “holoenzyme.” The addition of thissigma-factor protein increases the accuracy of RNA polymerase–DNA
binding by roughly a million times, making recognition of promoter
sequences and thus accurate transcription possible.26
Transcription is a highly regulated process. By binding to specific sites on
the DNA, various proteins will either inhibit or promote the transcription ofparticular genes in response to the varying needs of the cell. For example,
when bacteria have no lactose available to metabolize, the protein “lactose
repressor” binds near the gene that produces proteins for consuming or
metabolizing lactose. This prevents RNA polymerase from transcribing thegene and producing an unnecessary protein. When lactose enters the cell,
however, a chemical derivative of lactose binds to the lactose repressor,
producing a change in the repressor protein that causes the repressor to fall off
the gene, allowing the gene to be transcribed and the protein for metabolizing
lactose to be synthesized.27 By regulating transcription, repressor andactivator proteins ensure that the cell maintains appropriate levels of proteins.
Thus, even in prokaryotic organisms, many separate proteins are necessary tofacilitate—and regulate—transcription.

Figure 5.5. Parts of the RNA polymerase holoenzyme (sigma factor not
pictured).
In eukaryotic cells (cells with nuclei), the process of transcription isconsiderably more complex.28 Here recognition is promoted by a massive
complex of several necessary initiation factors, enzymes that, like a jigsaw
puzzle, fit together with each other, with the promoter sequence on the DNA
molecule, and with the RNA polymerase (see Fig. 5.5). After transcription,
the mRNA transcript must be heavily edited by still other proteins before
transport to the ribosome site for translation. (In prokaryotes, this editing
process takes place to a much lesser, but still necessary degree; see Fig.
5.6).29
As it turns out, the original DNA text in eukaryotic organisms has longsections of text called “introns” that do not (typically) encode proteins.
Although these introns were once thought to be nonfunctional “junk DNA,”
they are now known to play many important functional roles in the cell.30 (Idiscuss some of these newly discovered functions of introns and other typesof nonprotein coding DNA in Chapter 18 and the Epilogue.) In any case, theinitial transcript, being a copy of the DNA, also contains sections of text that
do not encode proteins, but that are interspersed with coding text. To excise
these noncoding regions before translation, the cell must edit the initial
mRNA transcript so that only coding regions remain. This process requires
the existence of other specific enzymes—exonucleases, endonucleases, andsplicesomes, for example—that can correctly identify and excise thenonprotein coding text from the initial RNA transcript and then splice
together the resulting fragments of coding text in the correct order.31

Figure 5.6. The initiation phase of transcription showing a promoter sequence(TATA), the various transcription factors that help position the RNA
polymerase correctly along the DNA and the RNA polymerase enzyme.
Thus, in both prokaryotes and eukaryotes, transcription constitutes acomplex, functionally integrated process involving several specialized andindividually necessary proteins. Yet production of each of these separate
proteins is itself dependent on the very process of transcription that they makepossible. To build RNA polymerase, for example, the cell must transcribe thegenetic texts with the instructions for building RNA polymerase. Yet totranscribe this information requires RNA polymerase. The informationnecessary to manufacture RNA polymerase and all the other associatedenzymes and protein cofactors of transcription is stored on the DNA template.
But expressing that information on the DNA template for building theproteins of the transcription system requires most of the proteins of thetranscription system.
Translation

The next step in gene expression, called translation, exhibits even greater
integrated complexity. Whereas transcription makes a single-stranded copy—a transcript—of DNA in an RNA format, translation uses that information tobuild a protein. Since many biologists think of protein molecules themselves
as information-rich molecules constructed from a twenty-character aminoacid alphabet, they think of the process of protein synthesis as a process oftranslating information from the four-character alphabets of DNA and RNA
into the twenty-character amino-acid alphabet; hence the name “translation.”
Even in the simplest prokaryotic cells, the process of translation utilizes
many dozens of separate proteins or protein machines, each one of which is
produced during translation.32 After the messenger RNA reaches the outer
cytoplasm, it arrives at the site of a large chemical-processing unit known as aribosome, the site of protein synthesis. In prokaryotes, the ribosome alone
contains fifty separate proteins and three long RNA molecules combined in
two distinct but associated subunits.33
The process of translation begins as the ribosome subunits dissociate andthe messenger RNA (mRNA) binds to the smaller of the two subunits (seeFig. 5.7). Auxiliary proteins known as initiation factors catalyze thisdisassociation and temporarily stabilize the second subunit in its disassociatedstate. At the same time, a group of three RNA bases on a transfer-RNA(tRNA) molecule binds to the first triplet of RNA bases on the mRNA
molecule as it docks in the ribosome. The groups of three bases on mRNA are
called codons or triplets. The groups of three bases to which they bind on thetRNA are called anticodons. The sequence AUG constitutes the “initiatorcodon” at the head of the mRNA transcript.34
After the initiator codon (AUG) on the mRNA transcript binds to theanticodon triplet on the corresponding tRNA, then the second and larger
subunit of the ribosome rejoins the first, forming a large complex ofmolecules including both ribosomal subunits, the mRNA, and a tRNA
molecule carrying its corresponding amino acid. The protein chain can now
begin to form. An additional amino acid–tRNA combination (known as anaminoacyl-tRNA molecule) binds to a second and adjacent active site on theribosome, bringing its amino acid into close proximity to the first. A protein
within the ribosome known as a peptidyl transferase then catalyzes apolymerization (linking) reaction involving the two (tRNA-borne) aminoacids. In the process, the first amino acid detaches from its tRNA and attaches
to the second amino acid, forming a short dipeptide chain. The ribosome thenejects the first and empty molecule and moves along to “read” the next tripletof bases on the mRNA. Another tRNA–amino acid carrier pairs with the next
mRNA codon, bringing a new amino acid into close proximity with thegrowing chain, and the process repeats itself until the signal for termination isreached on the mRNA. Then a protein termination factor, rather than anaminoacyl tRNA, binds to the second ribosome site and catalyzes hydrolysis(dissolution) of the bond holding the peptide chain to the tRNA at the firstribosome site. The newly assembled protein then detaches.35

Figure 5.7. The translation of messenger RNA at the ribosome resulting in a
growing amino acid chain.
At each step in the translation process, specialized proteins perform
crucial functions. For example, the initial coupling of specific amino acids totheir specific tRNA molecules (Crick’s adapters) depends upon the catalytic
action of twenty specific enzymes, one for each tRNA–amino acid pair. The
integrity of the genetic code depends upon the specific properties of these
enzymes, known as aminoacyl-tRNA synthetases.36
These synthetases are necessary because, as Francis Crick anticipated,
there is nothing about the chemical properties of the bases in DNA (or thosein mRNA) that favors forming a chemical bond with any specific amino acidover another. In fact, the cloverleaf-shaped tRNA molecule attaches to themRNA transcript on one end and carries a specific amino acid on the other.
Figure 5.8 shows that the amino acid and the codon-anticodon pairs are at
opposite ends of the tRNA molecule. This distance ensures that neither thecodons on mRNA nor the anticodons on tRNA interact chemically with theamino acids. As Crick anticipated, direct chemical interactions between bases(codons) and amino acids do not determine the assignments that constitute thegenetic code.

Figure 5.8. The transfer-RNA molecule showing the anticodon on one end of
the molecule and the amino acid attachment site on the other.
Instead, these associations are mediated indirectly by the enzymatic actionof the aminoacyl-tRNA synthetases. The synthetases have several active sitesthat enable them to: (1) recognize a specific amino acid, (2) recognize a
specific corresponding tRNA (with a specific anticodon), (3) react the aminoacid with ATP (adenosine triphosphate) to form an AMP (adenosine
monophosphate) derivative, and then, finally, (4) link the specific tRNA
molecule in question to its corresponding amino acid. Current researchsuggests that the synthetases recognize particular three-dimensional orchemical features (such as methylated bases) of the tRNA molecule. In virtueof the specificity of the features they must recognize, individual synthetases
have highly distinctive shapes that derive from specifically arranged aminoacid sequences. In other words, the synthetases are themselves marvels of
specificity.37
For their part, ribosomes must also perform many functions. These
include: (1) enhancing the accuracy of codon-anticodon pairing between themRNA transcript and the aminoacyl-tRNAs, (2) polymerizing (via peptidyltransferase) the growing peptide chain, (3) acting as energy transducers
converting chemical energy into the mechanical energy during translocationof amino acids from tRNA carriers, (4) protecting the growing protein fromattack by proteases (protein-degrading enzymes) possibly by forming a longprotective tunnel, and (5) assisting in the hydrolysis (dissolution) of the aminoacid–tRNA bond during termination. Further, several separate protein factors
and cofactors facilitate various specialized chemical transformations during
the three discrete steps of translation: initiation, elongation, and termination.
In eukaryotes, initiating translation alone requires a dozen separate proteincofactors. In prokaryotes, for each of the three steps of translation, three
specialized protein cofactors perform specific (and in several cases necessary)functions. Thus, here, as in the transcription system, origin-of-life researchers
find themselves confronted by a chicken-egg problem.
Making Copies
Besides transcribing and translating, the cell’s information-processing system
also replicates DNA. This happens whenever cells divide and copy
themselves. As with the processes of transcription and translation, the process
of DNA replication depends on many separate protein catalysts to unwind,stabilize, copy, edit, and rewind the original DNA message. In prokaryotic
cells, DNA replication involves more than thirty specialized proteins toperform tasks necessary for building and accurately copying the genetic
molecule. These specialized proteins include DNA polymerases, primases,
helicases, topoisomerases, DNA-binding proteins, DNA ligases, and editing
enzymes.38 DNA needs these proteins to copy the genetic information
contained in DNA. But the proteins that copy the genetic information in DNA
are themselves built from that information. This again poses what is, at thevery least, a curiosity: the production of proteins requires DNA, but the
production of DNA requires proteins.
To complicate matters further, proteins must catalyze formation of thebasic building blocks of cellular life such as sugars, lipids, glycolipids,
nucleotides, and ATP (adenosine triphosphate, the main energy molecule ofthe cell). Yet each of these materials is also constructed with the help ofspecific enzymes. For example, each of the systems involved in theprocessing of genetic information requires energy at many discrete steps. In
the cell, ATP (adenosine triphosphate) or a similar molecule (GTP, guanosine
triphosphate) supplies this energy whenever one of its three phosphates arecleaved during a reaction known as “hydrolysis.” The cell manufactures ATPfrom glucose by a process known as glycolysis. Yet glycolysis involves ten
discrete steps each catalyzed by a specific protein. These proteins (e.g.,hexokinase, aldolase, enolase, pyruvate kinase) are, in turn, produced fromgenetic information on DNA via the processes of transcription and translation.
Thus, the information-processing system of the cell requires ATP, but ATP
production (via glycolysis) requires the cell’s information-processing system,
again forming a “closed loop.” Indeed, it even takes ATP to make ATP during
glycolysis.39 Figure 5.9 shows some of the functionally interdependent
relationships among the systems of proteins that comprise the cell’s
information-processing system.

Figure 5.9. Schematic of the functional integration of the prokaryotic
information-processing system listing the number of proteins involved in eachprocess.
Integrated Complexity and the Origin of Life
Following the elucidation of the structure and function of DNA during the1950s and early 1960s, a radically new conception of life began to emerge.
Not only did molecular biologists discover that DNA carried information;
they soon began to suspect that living organisms must contain systems forprocessing genetic information. Just as the digital information stored on a discis useless without a device for reading the disc, so too is the information on
DNA useless without the cell’s information-processing system. As Richard
Lewontin notes, “No living molecule [i.e., biomolecule] is self-reproducing.
Only whole cells may contain all the necessary machinery for selfreproduction…. Not only is DNA incapable of making copies of itself, aided
or unaided, but it is incapable of ‘making’ anything else…. The proteins of
the cell are made from other proteins, and without that protein-forming
machinery nothing can be made.”40
Crick was right: the cell contains not just molecular repositories ofgenetic information, but a code for translating the information in the DNA
molecule (and its RNA transcript) into the construction of a protein. But thisrequires some physical medium of information transfer. After Crick andothers realized that this transfer is not achieved via the direct attraction ofamino acids to individual nucleotide bases or groups of bases—as Gamow
had proposed—it became evident that the transcription and translation ofgenetic information is mediated by a complex information-processing systemcomposed of many types of nucleic acids (such as mRNAs and tRNAs) andmany specific enzymes.
These and other developments in molecular biology since the 1960s haveshown that the information-processing system of the cell depends on a“tightly integrated” system of components—indeed, a system of systems.
Both the transcription and translation systems depend upon numerous
proteins, many of which are jointly necessary for protein synthesis to occur atall. Yet all of these proteins are made by this very process. Proteins involvedin transcription such as RNA polymerases, for example, are built from
instructions carried on an RNA transcript. Translation of the RNA transcript
depends upon other specialized enzymes such as synthetases, yet the
information to build these enzymes is translated during the translation processthat synthetases themselves facilitate.
Biochemist David Goodsell describes the problem, “The key molecular
process that makes modern life possible is protein synthesis, since proteins
are used in nearly every aspect of living. The synthesis of proteins requires atightly integrated sequence of reactions, most of which are themselves
performed by proteins.”41 Or as Jacques Monod noted in 1971: “The code ismeaningless unless translated. The modern cell’s translating machinery
consists of at least fifty macromolecular components which are themselves
coded in DNA: the code cannot be translated otherwise than by products oftranslation.”42 (Scientists now know that translation actually requires morethan a hundred proteins.)43
The integrated complexity of the cell’s information-processing system has
prompted some profound reflection. As Lewontin asks, “What makes theproteins that are necessary to make the protein?”44 As David Goodsell puts it,
this “is one of the unanswered riddles of biochemistry: which came first,
proteins or protein synthesis? If proteins are needed to make proteins, how did
the whole thing get started?”45 The end result of protein synthesis is required
before it can begin.
The interdependence of proteins and nucleic acids raises many obvious“chicken and egg” dilemmas—dilemmas that origin-of-life theorists before
the 1960s neither anticipated nor addressed. The cell needs proteins to process
and express the information in DNA in order to build proteins. But theconstruction of DNA molecules (during the process of DNA replication) also
requires proteins. So which came first, the chicken (nucleic acids) or the egg(proteins)? If proteins must have arisen first, then how did they do so, since
all extant cells construct proteins from the assembly instructions in DNA.
How did either arise without the other?
As the late British philosopher Sir Karl Popper mused, “What makes theorigin of life and the genetic code a disturbing riddle is this: the code cannot
be translated except by using certain products of its translation. This
constitutes a really baffling circle: a vicious circle it seems, for any attempt toform a model, or a theory, of the genesis of the genetic code.”46 Even thesimplest living things operate as complex systems of multiple interdependentparts. Yet how this feature of life could have arisen is, as Jacques Monod put
it, “exceedingly difficult to imagine.”47
The Question Redefined
The picture of the cell provided by modern molecular biology has led
scientists to redefine the question of the origin of life. The discovery of life’s
information-processing systems, with their elaborate functional integration ofproteins and nucleic acids, has made it clear that scientists investigating theorigin of life must now explain the origin of at least three key features of life.
First, they must explain the origin of the system for storing and encoding
digital information in the cell, DNA’s capacity to store digitally encoded
information. Second, they must explain the origin of the large amount ofspecified complexity or functionally specified information in DNA. Third,
they must explain the origin of the integrated complexity—the functional
interdependence of parts—of the cell’s information-processing system.
Later chapters in the book (8 through 16) describe the various attempts
scientists have made to explain the origin of the information and informationprocessing system that the cell requires to maintain itself. As we will see,
there have been three basic approaches for explaining the informational
properties of life. The first relies heavily on random molecular interactions(chance). The second explains by reference to lawlike forces (necessity). The
third approach combines chance and necessity. But before I investigated these
possible explanations in detail I studied more about what scientists in the pastthought about questions of origins and about how scientists in the present
investigate them. I did this in part because I wanted to know if there was afourth option—the design hypothesis—that could be legitimately considered
as a possible scientific explanation for the DNA enigma.

6
The Origin of Science and the Possibility of Design

At the beginning of my graduate studies, I did not know the extent of thecell’s complexity and, therefore, did not fully appreciate the challenge facing
origin-of-life research. But as I was learning more about various origin-of-lifescenarios, I also began to learn everything I could about molecular biology
and the extraordinary discoveries that had taken place in that field during thepreceding thirty-five years. Fortunately, the Cambridge system allowed
graduate students to attend lectures in various disciplines, and our supervisors
in the history and philosophy of science particularly encouraged us to attend
lectures in scientific fields relevant to our topics. I found the challenge of
retooling in molecular biology exciting and was further aided in my quest by
a judicious choice of new friends, several of whom were molecular biologists
working at the Medical Research Council Laboratory, including two
American postdoctoral researchers who shared my interest in the origin of life
and who more or less willingly indulged my persistent inquiries about the
inner workings of the cell.
As I learned more about the chemistry of DNA, the sequence hypothesis,
the gene-expression system, and what molecular biology generally had to say
about the origin-of-life problem, I also learned more about how Watson andCrick had made their famous discovery. One day, Maurice Wilkins, thecrystallographer who had received the Nobel Prize along with Watson andCrick for his work on the double helix, came to lecture in our department. In
his talk, Wilkins told about his sometimes tense working relationship with
Rosalind Franklin, the importance of her work, his encounters with Watson
and Crick, and the key scientific insights that led to their eventual discovery.
He was personally modest about the part he played in this historicalachievement and clearly energized by the opportunity to relive the excitementof the discovery, although he did comment, rather plaintively, that if only he
had had a bit more time, he might himself have solved the structure first.
Afterward, several scholars questioned Wilkins about the ethics of Watson
and Crick’s use of what they had learned from Wilkins about Franklin’s X-ray
images in order to determine the structure of DNA. Wilkins’s response wasinteresting. He acknowledged that Franklin, who died before the Nobel Prize
was awarded, had not received her share of credit for the important discovery,but he defended his rivals from Cambridge for their willingness and ability tosynthesize evidence from diverse sources and disciplines in order to put all
the pieces of a puzzle together into a coherent new picture of reality.
I had a chance to talk with Wilkins afterward and his defense of Watson
and Crick left me with a key insight about how science works. Many people
think that scientists spend all their time doing experiments in the laboratory inorder to test hypotheses. The discovery of the structure of DNA showed thatscience certainly involves careful laboratory work. Rosalind Franklin’s
painstaking collection and analysis of X-ray data played an indispensable rolein the discovery of the double helix, one for which she doubtless did notreceive sufficient credit. But the discovery of the double helix showed thatscience also depends upon the synthesis of diverse lines of evidence in orderto make sense of the big picture. In this, Watson and Crick excelled. Seen inthis light, there was more method—more scientific method—to their maddash to collect and synthesize all available data than some of their critics haveallowed.
Though Watson and Crick were relatively unknown and certainly
undercredentialed, they had solved one of the great scientific mysteries of theages. Moreover, they achieved this feat not by working their way up throughthe establishment, which typically involves publishing a series of narrowly
focused technical papers based on their own experimental research, but by
explaining an array of preexisting evidence in a new and more coherent way.
Could a similar approach help to crack the other mystery surrounding DNA,the mystery of how the digital information in the molecule had originated in
the first place?
As I addressed these questions I found Watson and Crick’s example
instructive and encouraging. I was not an experimentalist, but a former
applied scientist and philosopher of science. In my investigations of the DNA
enigma, I began to marshal every relevant intellectual resource and insight—
scientific, historical, mathematical, and philosophical—that I could. Watson
and Crick showed that science involves such interdisciplinary activity. Many
scientists working on the origin of life are particularly aware of this. As
Freeman Dyson, a Princeton physicist who has written extensively on theorigin of life, explains, “The origin of life is one of the few scientificproblems which is broad enough to make use of ideas from almost allscientific disciplines.”1
Unfortunately, this understanding of how science works has not
penetrated public understanding. Nor do scientists themselves always
acknowledge it. Years later, as those of us in the intelligent design (ID)research community began to advance what we regarded as a new synthetic
understanding of the origin and nature of life, we were repeatedly maligned
for not “doing science.” In 2004–5, as the program I led at Discovery Institutesuddenly found itself at the center of a frenzy of hostile media coverage,
reporters kept repeating the same criticism, namely, “ID advocates aren’t
really scientists, because they don’t do any experiments of their own.”
Reporters would then demand to see our laboratory, as if doing experiments
was the only kind of activity that scientists pursued.
At the time, I knew there were scientists in labs around the world who
supported the theory of intelligent design. Some of these scientists had done
experiments testing different aspects of the theory, sometimes with ourfinancial support. Beyond that, we actually had started our own laboratory,
though we had chosen to keep it out of public view initially to protect somescientists and sensitive projects from premature attempts to discredit them.
Later when we publicly unveiled a lab, called Biologic Institute, even our
critics acknowledged that our scientists were addressing significant scientificquestions, though many still attempted to stigmatize Biologic’s experimental
research as “religiously motivated.”2 In any case, because of my roleoverseeing ID research at Discovery Institute, I knew firsthand that thiscritique of the theory of intelligent design was not accurate: many ID
scientists do in fact work in labs and conduct experiments.
But as Watson and Crick’s discovery showed, even if advocates of ID
weren’t doing experiments in labs—as I personally was not—it didn’t followthat we weren’t “doing science.” To say otherwise betrayed a blinkered viewof the scientific enterprise. Watson and Crick performed many experiments
during their long careers. But the work for which they are best known came as
the result of building models based on data they acquired almost exclusively
from other sources—from scientific journals, other scientists, and other labs.
Many of the great discoveries in science were achieved not just by
experimentalists who produced new factual knowledge, but by theoreticians
who taught us to think differently about what we already know. Examples ofthis kind of scientific work leaped to mind: Copernicus’s De Revolutionibus
orbium coelestrium, Newton’s Principia, and the papers Einstein produced in
his annus mirabilis, his miracle year of 1905. While working as a patent clerk
without access to any experimental apparatus, Einstein rethought the wholeframework of modern physics and, in the process, explained many previously
confounding factual anomalies.3
Charles Darwin also did little experimental science. He did make several
descriptive studies of barnacles and worms and some experimental studies
about how species spread through seed dispersal and other processes. Yet his
masterpiece, On the Origin of Species by Means of Natural Selection,
contains neither a single mathematical equation nor any report of original
experimental research. Yet he formulated a great scientific theory. He did thisby drawing together disparate lines of observational evidence and presenting

an argument for a novel interpretation of that evidence. Of course Darwin
made some of the observations that supported his theory himself, but even if
he had not done so, this would have hardly detracted from his theory.
Darwin’s method of investigation typified that of many other historicalscientists who functioned more like detectives solving a mystery by collecting
clues and developing a case than like stereotypical experimental scientists
who test hypotheses under carefully controlled laboratory conditions.
But I had other reasons for liking the story of Watson and Crick, andparticularly the story of their encounter with Erwin Chargaff, in front ofwhom they had grievously embarrassed themselves in their headlong attempt
to acquire information about the chemical structure of the mystery molecule.
During my years in Cambridge and even after, I had a number of similar
experiences—although I can’t say I wasn’t warned.
One day I attended a spellbinding lecture in my department by a visiting
scholar who was perhaps the world’s leading authority on Immanuel Kant.
During the ensuing discussion, I asked him if he would direct me to a goodsource whereby I could learn more about a particular aspect of Kant’s
philosophy that he had just discussed. He answered me graciously enough,but I had a vague sense from others in the room that I may have created an
awkward moment. As I was leaving the lecture hall, the head of mydepartment, Michael Hoskin, a kindly Cambridge don of the old school,
gently pulled me aside. “Meyer,” he intoned in his high Oxbridge accent, “Iknow that in the States you’ve learned that the only stupid question is the one
you don’t ask. But it’s different here. Everyone here is bluffing, and if you’re
to succeed, you must learn to bluff too. So never admit you haven’t readsomething. And in future, if you have a question that reveals ignorance,
please, come ask me privately.”
Professor Hoskin’s advice was no doubt good, but I was temperamentallydisinclined to follow it. As I met Cambridge scientists, philosophers, andfellow graduate students over high-table dinners or in common-room
discussions, I often shared my interest in the DNA enigma. When I met
someone with relevant expertise, I would probe for insights. Often, I learned
things as a result, but sometimes I felt the sting of embarrassment at having
asked one too many questions.
Foolishly perhaps, I also sometimes let it slip that I was interested in thequestion of design and, specifically, in the question of whether a rigorous
scientific argument for design could be reformulated based upon the
discovery of the information-bearing properties of DNA. It did not take melong, naïve American though I was, to understand that this was not always awelcome turn in the conversation. Sometimes fellow graduate students would
warn me about not venturing into such a dangerous topic, though more often I
received blank and impassive stares. Cambridge was, after all, Darwin’s almamater, and the design hypothesis had not been seriously debated in biology
for almost a hundred and fifty years.
Oddly, I did find willingness to discuss these topics among some of themore prominent scientists whom I had the good fortune to meet. The year
before coming to Cambridge, I had the opportunity to interview the Nobel
Prize–winning physicist Eugene Wigner at a conference at Yale. I asked him
directly about the origin-of-life problem, and he explained why he thought theodds were overwhelmingly against any process of undirected chemical
evolution producing life. Wigner further expressed openness to, evensympathy for, the idea that the information present in DNA pointed to an
intelligent source.
Upon arriving in Cambridge I had a few similar experiences, but again
only with very prominent scientists, those who, perhaps, felt secure enough intheir reputations to entertain heterodox ideas openly. Once, before attending ahigh-table dinner at Churchill College as the guest of Professor Hoskin, a
charming Hungarian man broke with Cambridge etiquette and sat downcrossed-legged in front of me on the carpet in the senior common room. He
introduced himself merely as Herman and began asking me questions about
my research interests. As I explained them, he expressed his fascination with
my ideas about DNA and shared some of his own questions about the originof-life problem. Later, as we resumed our conversation after a dinner in whichanti-American invective was flowing as freely as the port and madeira,Professor Hoskin circled around to make sure that we had made proper
introductions.
“Oh, Bondi,” he said, “I’m delighted to see that you’ve met young
Meyer.” I gulped as I suddenly realized who it was that I had been regaling
with my various musings about the origin of life. “Herman” was Sir Professor
Herman Bondi, the mathematician and cosmologist who had, with Sir Fred
Hoyle, formulated the steady-state theory of the universe and who was also
famous for his work on general relativity. Beyond that, I knew that Bondi wasa well-known secular humanist. He had every philosophical inclination todismiss my ideas out of hand. Yet there he sat, cross-legged, in front of a
graduate student, openly discussing a radical proposal for solving the problemof the origin of life. I’ll never forget him.
Later Bondi’s collaborator, Fred Hoyle, also came to Cambridge todiscuss his views on the origin of life. This was a lecture I couldn’t miss.
Hoyle was famous for his correct prediction of the resonance levels of the
carbon atom and for his pioneering work on the fine-tuning problem inphysics and cosmology. But he hadn’t come to Cambridge to discuss any ofthis. He was here to explain why he had come to reject chemical evolutionary
theory. Afterward I asked him directly about whether he thought the
information stored in DNA might point to an intelligent source. His eyesbrightened, and he motioned me to continue walking with him after his
lecture. “That would certainly make life a lot easier to explain,” he said.
Modern Cambridge, Ancient Cambridge
Apart from a few such encounters, contemporary Cambridge did not share myinterest in design. Nevertheless, it didn’t take me long to find that ancient
Cambridge did. Cambridge was world-famous as a city where the history ofscience was not just studied, but made. I soon discovered that those who had
established the scientific enterprise and, for several hundred years, made its
history did not share the disdain for the design hypothesis that I had
encountered. The founders of the scientific revolution (ca. 1300–1700) were
often deeply religious men who expressed a profound appreciation for thedesign of life and the universe. Moreover, for these scientists, the concept ofdesign was not just a pious sentiment. For them it was an indispensable
assumption upon which the whole of the scientific enterprise rested.
As many historians of science have noted, the founders of modern scienceneeded to assume that if they studied nature carefully, it would reveal its
secrets. Their confidence in this assumption was grounded in the Greek andJudeo-Christian idea that the universe is an orderly system—a cosmos, not achaos. As the British philosopher Alfred North Whitehead explained, “Therecan be no living science unless there is a widespread instinctive conviction in
the existence of an Order of Things. And, in particular, of an Order of
Nature.” Whitehead argued that confidence in this proposition was especiallyinspired by the “medieval insistence upon the rationality of God.”4
Other historians of science have amplified Whitehead’s observation. They
have insisted that modern science was specifically inspired by the convictionthat the universe is the product of a rational mind who designed the universe
to be understood and the human mind to understand it. As sociologist ofscience Steve Fuller notes, Western science is grounded in “the belief that thenatural order is the product of a single intelligence from which our own
intelligence descends.”5 This foundational assumption gave rise to the ideathat nature was “intelligible,” that it had been designed in accord withdiscernable laws that could be understood by those who subjected nature tocareful scrutiny. Or as the astronomer Johannes Kepler said, scientists havethe job of “thinking God’s thoughts after him.”6
Though Greek, Jewish, and Christian philosophers agreed about therationality of nature, they did not necessarily agree about how to discover it.
Many historians of science have noted that Greek ideas about nature tended toinduce a sterile armchair philosophizing unconstrained by actual observations.
For science to advance, it needed to develop a more empirical approach. Thisbegan to occur during the scientific revolution after medieval philosophers
and the early modern scientists made a decisive break with one aspect of
Greek thinking.
Although the Greek philosophers thought that nature reflected anunderlying order, they nevertheless believed that this order issued not from adesigning mind, but from an underlying and self-evident logical principle. For
this reason, many assumed that they could deduce how nature ought to behavefrom first principles without actually observing nature. In astronomy, forexample, the Greeks (Aristotle and Ptolemy) assumed that planets must movein circular orbits. Why? Because according to the Greek cosmology, theplanets moved in the “quintessential” realm of the crystalline spheres, aheavenly realm in which only perfection was possible. Since, they deduced,
the most perfect form of motion was circular, the planets must move incircular orbits. What could be more logical?
The idea of design helped liberate Western science from such fact-freereasoning. Like the Greek philosophers, the early modern scientists thoughtthat nature exhibited an underlying order. Nevertheless, they thought thisnatural order had been impressed on nature by a designing mind, in particular,
the mind of the Judeo-Christian God. For this reason they thought that theorder in nature was the product not of logical necessity, but of rational
deliberation and choice. Because nature had been designed by the same mind
that had designed the human mind, the early scientists assumed that nature
was intelligible, that it could be understood by the human intellect. But
because the order in nature was also contingent on the will of its designer,
they realized they had to observe nature in order to get it to reveal its secrets.
Just as there are many ways to paint a picture or design a clock ororganize the books in a library, there were many ways to design and organize
a universe. Because it had been chosen by a rational mind, the order in nature
could have been otherwise. Thus, the natural philosophers could not merely
deduce the order of nature from logical first principles; they needed toobserve nature carefully and systematically. As the chemist Robert Boyle
explained, the job of the natural philosopher (the scientist) was not to ask
what God must have done, but (as far as possible) to inquire into what God
actually did. Boyle argued that God’s absolute freedom as designer andcreator requires of us an empirical and observational, not a deductive,
approach to the natural order: look—observe—to find out.7
Thus, the assumption that a rational mind had designed the universe gave
rise to two ideas—intelligibility and contingency—which, in turn, provided a
powerful impetus to study nature and to feel confident that such study would
yield understanding. As the Oxford physicist and historian of science Peter
Hodgson observes: “According to Judeo-Christian beliefs the world is the freecreation of God from nothing. The structure of the world cannot therefore bededuced from first principles; we have to look at it, to make observations andexperiments to find out how God made it. This reinforces the Aristotelianprinciple that all knowledge comes through the senses, but requires that it besituated within a wider set of beliefs concerning the nature of the world that is
implicit in the doctrine of creation.”8 Hodgson notes that early scientists
assumed that the world was both rational—because it was created by a Mind
—and contingent—because that Mind had acted freely. These assumptions
led to “a fresh style of scientific thinking,” one that “was made possible by
the Judeo-Christian vision of the world.”9Everywhere I went in the city of Cambridge, I encountered evidence of
this long dominant viewpoint. Each day as I walked to my department on Free
School Lane, I passed by the entrance to the old Cavendish Laboratory inwhich thirty-odd years before Francis Crick and James Watson realized that
their model of DNA was so beautiful it had to be right.10 On the archway
across the great wooden door of the Cavendish was a Latin inscription that
reads, Magna opera Domini. Exquista in omnes voluntates ejus. The
inscription had been placed there at the insistence of the physicist James
Clark Maxwell, the first Cavendish professor in 1871. The inscription quotesa Psalm that reads, “Great are the works of the Lord, sought out by all who
take pleasure therein.” The inscription summarized Maxwell’s inspiration for
scientific study: the thought that works of nature reflect the work of adesigning mind. In this belief he had been joined by many of the leading
scientists of Western civilization for over four hundred years—Copernicus,
Kepler, Ray, Linnaeus, Curvier, Agassiz, Boyle, Newton, Kelvin, Faraday,
Rutherford—on and on the list could go.
As I studied the history of science, I soon discovered, however, that manyof these scientists did not just assume or assert by faith that the universe had
been designed; they also argued for this hypothesis based on discoveries intheir disciplines. Johannes Kepler perceived intelligent design in themathematical precision of planetary motion and the three laws he discoveredthat describe that motion. Other scientists perceived design in many of thestructures or features of the natural world upon which the laws of nature
operated. Louis Agassiz, the leading American naturalist of the nineteenth
century, for whom the Agassiz Chair is named at Harvard, believed that thepatterns of appearance in the fossil record pointed unmistakably to design.11
Carl Linnaeus argued for design based upon the ease with which plants andanimals fell into an orderly groups-within-groups system of classification.12
Robert Boyle insisted that the intricate clocklike regularity of many physicalmechanisms suggested the activity of “a most intelligent and designing
agent.”13
Newton, in particular, was noteworthy in this regard. As I discussed inChapter 1, he made specific design arguments based upon discoveries inphysics, biology, and astronomy. He argued for the intelligent design of theeye in his classic work, Opticks. He also argued for the intelligent design ofthe planetary system in his masterpiece, The Mathematical Principles of
Natural Philosophy (often cited in brief by part of its Latin title, thePrincipia).14 Writing in the General Scholium (introduction) to the Principia,
Newton suggested that the stability of the planetary system depended not only
upon the regular action of universal gravitation, but also upon the precise
initial positioning of the planets and comets in relation to the sun. As he
explained: “Though these bodies may, indeed, persevere in their orbits by themere laws of gravity, yet they could by no means have, at first, derived theregular position of the orbits themselves from those laws…. [Thus] this mostbeautiful system of the sun, planets, and comets could only proceed from thecounsel and dominion of an intelligent and powerful being.”15
So central was the idea of design to Newton’s scientific perspective that I
was repeatedly cautioned not to miss it as I researched Newton’s ideas about
gravity in the Principia. In one tutorial, one of my supervisors, a staunchatheist, reminded me not to read modern secular biases into my interpretationof Newton’s work. “If you miss Newton’s theism,” he exhorted, “you’ve
missed everything.”
What I learned about Newton would come in handy years later. In 1999, I
was asked to testify at a hearing of the U.S. Commission on Civil Rights
about the alleged viewpoint discrimination in the teaching of biologicalorigins. My opposing expert witness at this hearing was Eugenie Scott, ananthropologist who heads the National Center for Science Education. Scott
had distinguished herself as an ardent critic of the scientific legitimacy ofintelligent design and has consistently argued that the very idea of intelligent
design violates the rules of science. According to Scott, these rules preclude
invoking any nonnaturalistic or nonmaterialistic causes (such as the action ofa designing intelligence) to explain features of the natural world.
At the hearing, one of the commissioners asked me an interestingquestion. He wondered, given Newton’s views on intelligent design in physics
and Dr. Scott’s widely accepted definition of science, whether Newtonian
physics would qualify as “scientific” in our present educational climate.
Eugenie Scott responded by insisting that the commissioner had
misunderstood the context in which Newton discussed intelligent design. She
explained that, for Newton, intelligent design was merely a theological belief—one that he took pains to keep separate from his science so as to avoid
subjecting it to empirical test. Here’s what she said according to the transcriptof the hearing: “Newton made a very clean distinction—as a very religious
scientist—he made a very clear distinction about how science should work.

Newton’s view was that we should understand the natural world solely byusing natural processes. And he said this for religious reasons because hedidn’t want God’s existence or God’s transcendence, shall we say, to be testedby the base methods of science.” Scott then proceeded to reassert thatinvoking intelligent design would violate the central rule of science, namely,
as she put it, that “we explain the natural world restricting ourselves only tonatural cause.”16
As it happened, I had just finished an essay on the history of scientificdesign arguments and had quoted Newton’s argument in the Principia—an
indisputably scientific treatise—at length. The essay was at that moment
sitting in my briefcase. As the transcript of the hearing makes clear, I
immediately challenged Scott’s portrayal of Newton. “The historical point on
Newton, I’m afraid, is just simply incorrect. If one opens the General
Scholium, the introduction to the Principia, arguably the greatest book ofscience ever written, one finds an exquisite design argument by Newton inwhich he makes clear that the arrangements of the planets can only be
explained not, he says, by natural law, but only by the contrivance of a mostwise artificer. He’s very explicit about this. This is in the introduction to his
magnum opus.” Several of the commissioners appeared intrigued, so I pressed
on, noting that Newton was not alone in his formulation of such design
arguments. I continued, “You find these kinds of design arguments all
throughout the scientific revolution, from Boyle, from Kepler, from others.”17
The Abolition of Design
Based on my research (and from noticing many of the landmarks at
Cambridge), I had learned that the historical record on this point was
irrefutable. I began to wonder: How could the act of invoking something sofoundational to the history of science as the idea of design now completely
violate the rules of science itself, as I had repeatedly heard many scientists
assert? If belief in intelligent design first inspired modern scientificinvestigation, how could mere openness to the design hypothesis now act as a“science stopper” and threaten to put an end to productive scientific researchaltogether, as some scientists feared?
Clearly, the idea of intelligent design had played a formative role in thefoundation of modern science. Many great scientists had proposed specificdesign hypotheses. This seemed to suggest to me that intelligent design could
function as a possible scientific hypothesis. But many contemporary scientists
rejected this idea out of hand. Why?
The Nature of Historical Scientific ReasoningI remembered that my Dallas mentor, Charles Thaxton, thought that many
scientists today rejected design out of hand because they failed to recognizethat there were different types of scientific inquiry, specifically, that there wasa distinctive kind of scientific inquiry concerned with investigating andexplaining the past. His distinction between origins and operations sciencesuggested a reason to consider an “intelligent cause” as a possible scientificexplanation for the origin of life. Thaxton thought that since the origin of life
was a unique historical event, and since the singular actions of intelligent
agents often produce unique events, the act of a designing agent mightprovide an appropriate kind of scientific explanation for this event. This madesense when I first encountered Thaxton’s distinction. It seemed to me thatinvoking an “intelligent cause” as the explanation for a past event might be aperfectly reasonable thing to do for the simple reason that the activity of an
intelligent agent might have actually caused the event in question.
It now occurred to me that the great scientists who had proposed design
hypotheses during the scientific revolution had typically done so to explain
the origin of some system or feature of the world—the eye, solar system, theuniverse, the “correlation of parts” in biological systems.18 So perhaps
Thaxton was right. Perhaps the design hypothesis could be formulated as arigorous scientific explanation for events such as the origin of life. But todecide that question I would need to know how scientists investigated the pastand how they formulated and justified their theories. And so the focus of myinvestigation shifted from an examination of the features of life that needed to
be explained (see Chapters 3 through 5) to a study of how historical scientists
go about explaining the origin of particular features and events in the remote
past (see Chapter 7).

7
Of Clues to Causes

When I left Dallas in 1986 I was seized by questions that had emerged in mydiscussions with Charles Thaxton. Could the inference to design from DNA
be formulated as a rigorous scientific argument? Is there a distinctive
scientific method for studying the remote past? When I left for Cambridge, I
was intrigued by Thaxton’s ideas, but not fully convinced. For one thing, I
didn’t find his account of the distinction between the two types of science
fully satisfying. The distinction seemed plausible, but needed further
clarification and justification.
Even so, I soon discovered that other scientists had made comparable
distinctions, though usually using different terminology. Stephen Jay Gould,
the Harvard paleontologist and historian of science, insisted that the“historical sciences” such as geology, evolutionary biology, and paleontology
used different methods than did “experimental sciences” such as chemistry
and physics. Interestingly, he also argued that understanding how historicalsciences differed from experimental sciences helped to legitimate
evolutionary theory in the face of challenges to its scientific rigor by those
who questioned its testability. Gould argued that historical scientific theories
were testable, but not necessarily by experiments under controlled laboratory
conditions. Instead, he emphasized that historical scientists tested their
theories by evaluating their explanatory power.1
Could the same thing be true of the design hypothesis? Perhaps a theoryof intelligent design could be formulated as a historical scientific theory about
what had happened in the past. If so, perhaps such a theory could be tested byassessing its explanatory power rather than its ability to generate predictions
in a controlled laboratory setting.
In my second year at Cambridge, after I had acquired a better
understanding of what origin-of-life researchers needed to explain, I began toinvestigate the questions that first emerged in my discussions with Thaxton. Is
there a distinctive mode of historical scientific investigation? If so, how doscientists reason and make inferences about the past? Are these inferences
testable? If so, how? And in the back of my mind another question continued
to linger: If there is a historical way of scientific reasoning, could suchreasoning be used to make a rigorous scientific case for intelligent design?

As I began to study how scientists investigate the past, I examined theworks of the nineteenth-century scientists and philosophers of science who
first developed and refined these methods.2 This eventually led me to themasters: to Charles Darwin and his mentor, the geologist Charles Lyell, and toa few lesser known luminaries who, along with Darwin and Lyell, pioneered amethod of scientific investigation designed to illuminate the history of life,
the earth, and the cosmos.
The Science of Past Causes
Visit the Royal Tyrrell Museum in Alberta, Canada, and you will find a
beautiful reconstruction of the Cambrian seafloor with its stunningassemblage of ancient organisms. Or read the fourth chapter of Cambridge
paleobiologist Simon Conway Morris’s book on the Burgess Shale and you
will be taken on a vivid guided tour of an ancient marine environment
teaming with exotic life-forms.3 How do scientists come to know about suchancient environments? What methods of reasoning do they use to investigate
what happened on the earth so long ago?
I soon discovered that I wasn’t the first person to ask such questions.
During the 1830s and 1840s, William Whewell, a distinguished scientist andphilosopher and the master of Trinity College, Cambridge, published two
books about the nature of science that addressed this issue.4 The publicationof Whewell’s work coincided with a surge of interest in natural history inVictorian England. By the 1830s, Charles Lyell had published his seminal
Principles of Geology, the first scientific descriptions of dinosaur fossils had
been made, and new evolutionary ideas about the history of life were
circulating in elite scientific societies.
In his volumes History of the Inductive Sciences (1837) and The
Philosophy of the Inductive Sciences (1840), Whewell distinguished sciencessuch as physics and chemistry from what he called palaetiology—literally, the“study of past causes.” And he argued that “palaetiological,” or historical,
sciences could be distinguished from nonhistorical sciences in three ways.5
First, the palaetiological, or historical, sciences have a distinctive objective: todetermine ancient conditions or past causes, as opposed to establishing
universal laws by which nature generally operates.6
Second, such sciences explain present events (“manifest effects”) by
reference to past (causal) events, rather than by reference to general laws
(though Whewell acknowledged that laws often play a supporting role in such
explanations).7 Historical scientists cite the occurrence of an event or series of
events in the past as the explanation for some observable phenomenon in thepresent.8 For example, a geologist might explain the origin of the Alps as theresult of a series of geological events involving the collision of specifictectonic plates, the overthrusting of sedimentary layers, and then thesubsequent folding and faulting of those layers. As science historian Jonathan
Hodge explains, Whewell realized that historical sciences do not study“forces that are permanent causes of motion, such as gravitational attraction,”but “causes that have worked their effects in temporal succession.”9
Third, in their attempt to reconstruct “ancient conditions,” Whewellargued that historical scientists also utilized a distinctive mode of reasoning.10
Using knowledge of cause-and-effect relationships, historical scientists“calculate backwards” and “infer” past conditions and causes from “manifest
effects.”11 As Gould later put it, historical scientists proceed by “inferring
history from its results.”12 For example, in order to reconstruct the Cambrianenvironment, paleontologists such as Gould and Conway Morris madeinferences about the past based on present-day fossils and other clues.13 They
inferred a past environment and set of conditions as the cause of the evidence
they found. Like other historical scientists, they reasoned from clues back tocauses.14
Abductive Reasoning
This type of reasoning is called abductive reasoning or abduction. It was firstdescribed by the American philosopher and logician Charles Sanders Peirce.
He noted that, unlike inductive reasoning, in which a universal law or
principle is established from repeated observations of the same phenomena,
and unlike deductive reasoning, in which a particular fact is deduced byapplying a general law to another particular fact or case, abductive reasoning
infers unseen facts, events, or causes in the past from clues or facts in thepresent.15
As Peirce himself showed, there is a problem with abductive reasoning.16
Consider the following syllogism:
If it rains, the streets will get wet.
The streets are wet.
Therefore, it rained.
In this syllogism, a past condition (it was raining) is inferred from a present
clue (the streets are wet). Nevertheless, this syllogism commits a logicalfallacy known as affirming the consequent. Given that the street is wet (andwithout additional evidence to decide the matter), one can only conclude thatperhaps it rained. Why? Because there are other possible ways the street may

have gotten wet. A street-cleaning machine, an uncapped fire hydrant, or rainmight have caused the street to get wet. It can be difficult to infer the pastfrom the present because there are often many possible causes of a given
effect. When this is the case, abductive inferences yield plausible, but notcertain, conclusions.
For Peirce, this raised an important question: How is it that, despite thelogical problem of affirming the consequent, we nevertheless frequently makeconclusive inferences about the past? He noted, for example, that no one
doubts the existence of Napoleon. Yet we use abductive reasoning to infer
Napoleon’s existence. That is, we infer his past existence not by travelingback in time and observing him directly, but by inferring his existence fromour study of present effects, namely, artifacts and records. But despite ourdependence on abductive reasoning to make this inference, no sane andeducated person would doubt that Napoleon Bonaparte actually lived. How
could this be if the problem of affirming the consequent bedevils our attempts
to reason abductively? Peirce’s answer was revealing: “Though we have notseen the man [Napoleon], yet we cannot explain what we have seen without
the hypothesis of his existence.”17 Peirce suggested that a particular abductive
hypothesis can be firmly established if it can be shown that it represents thebest or only explanation of the “manifest effects” in question.
As Peirce noted, the problem with abductive reasoning is that there isoften more than one cause that can explain the same effect. To address thisproblem in geology, the late-nineteenth-century geologist Thomas
Chamberlain delineated a method of reasoning he called the “method ofmultiple working hypotheses.”18 Geologists and other historical scientists use
this method when there is more than one possible cause or hypothesis toexplain the same evidence. In such cases, historical scientists carefully weigh
the relevant evidence and what they know about various possible causes todetermine which best explains it. Contemporary philosophers of science callthis the method of “inference to the best explanation.” That is, when trying to
explain the origin of an event or structure from the past, historical scientists
compare various hypotheses to see which would, if true, best explain it. They
then provisionally affirm the hypothesis that best explains the data as the most
likely to be true.
Peter Lipton and Inference to the Best Explanation
In the spring of 1990, as I was pressing to finish my Ph.D. dissertation, I had
the good fortune to meet an American philosopher of science who helped meunderstand abductive reasoning. Peter Lipton had just been hired away from
Williams College in Massachusetts by the Cambridge Department of History

and Philosophy of Science. After assuming the position of lecturer (theBritish equivalent of assistant professor), he eventually rose to become
professor and chair of the department before his premature death in 2007 at
the age of fifty-three. While Lipton was visiting Cambridge in the spring of
1990, one of the Cambridge faculty members, seeing that we shared somecommon interests, introduced us. Lipton had just finished writing a major
treatise called Inference to the Best Explanation, which later would become
something of a minor classic in contemporary philosophy of science. Given
what I had learned about how historical scientists needed to weigh competing
hypotheses, I was immediately interested in Lipton’s ideas. He kindly sent me
a pre-publication copy of his manuscript.
Lipton had made a systematic study and defense of a way of reasoningthat he called “inference to the best explanation” (IBE). He argued that thisway of reasoning was used commonly in science and ordinary life. For
example, he noted, that “faced with tracks in the snow of a peculiar shape, I
infer that a person with snowshoes has recently passed this way. There areother possibilities, but I make this inference because it provides the best
explanation of what I see.”19 From the examples he used to illustrate“inference to the best explanation,” it was clear that this method of reasoning
was especially useful to scientists who were trying to provide causal
explanations of events or circumstantial evidence.
I was impressed by Lipton’s work. He wrote clearly, using practicalscientific case studies to illustrate his ideas about this scientific method. His
work employed none of the jargon or penchant for abstraction that sometimes
gave philosophers of science a bad name with working scientists. Instead, heobviously respected the practice of science and wanted to give an accurate
account of how scientists actually formulate and justify their hypotheses andinferences.

Figure 7.1. Peter Lipton. Printed by permission from Howard Guest.
Interestingly, in his Oxford doctoral dissertation, Lipton initially hadargued against inference to the best explanation as an accurate understandingof scientific reasoning. He did this, he said, because he didn’t have time as adoctoral student to mount a sufficiently comprehensive case for it. Instead, hedecided to make the best case against it, so that he would fully understand theobjections to it as a way of preparing himself to write a book defending it. His
story revealed a person capable of weighing the merits of competingarguments before deciding a question. Lipton practiced what he preached.
According to Lipton, “beginning with the evidence available to us” wegenerally “infer what would, if true, best explain that evidence.”20 Thisechoed what Peirce had said about how an abductive inference can bestrengthened by showing that it alone can explain the evidence. But Lipton
made the connection between inference and explanation more explicit andshowed how considerations of explanatory power often influenced
assessments of competing inferences or hypotheses. Peirce and Gould hadconvinced me that historical scientists used a distinctive kind of inference—in
which past causes are inferred from present effects. Whewell and otherphilosophers of science showed that historical scientists formulated
distinctive types of explanations—ones in which past events or causes are
invoked to explain particular facts. Lipton now showed how the two
intellectual activities of inferring and explaining are connected.21
He showed that scientists often evaluate competing inferences or
hypotheses by comparing their explanatory power. That is, assessments of aninference’s explanatory power determine how much stock we place in it. Inmaking this argument, Lipton challenged a popular conception of science,
namely, that scientists test their theories only by evaluating the accuracy ofthe predictions that their theories make about future events.22 Lipton
demonstrated that the ability to explain known facts often mattered as muchor more than predictive success in the evaluation of an inference or
hypothesis.23
But there was a problem with this kind of reasoning, one that Lipton andmany philosophers of science had noted. Many regarded “inference to thebest explanation” as little more than a slogan, because no one could say
exactly what made an explanation best. “Sure,” many argued, “we often inferhypotheses that, if true, best explain the evidence, but what does it mean to
explain something and to explain it best?”24
As a first step toward answering the question, “What makes an
explanation best?” Lipton gave a general account of what a good explanation
should look like. He noted that scientists sometimes explain general classes ofphenomena (such as condensation) and sometimes a particular fact or event(such as the extinction of the dinosaurs). According to Lipton, good
explanations—whether of particular one-time events or general phenomena—
are typically causal.
In the case of general phenomena such as condensation, good
explanations typically cite causal mechanisms. For example, a physicist
would explain condensation by citing a general mechanism of heat exchange.
(When warm water vapor contacts a cold solid, the colder solid draws heatout of the gas, causing the water to change from a gaseous to a liquid state.)
In the case of particular events, Lipton showed that a good explanation
cites a prior cause, typically an event that is part of the “causal history” of theevent in question. Further, Lipton showed that to identify the cause of anevent, scientists must identify something within the causal history of the eventthat accounts for a crucial difference—the difference between what did occur
and what we might have otherwise expected. Good explanations answer
questions of the form, “Why this rather than that?” A good (or best)
explanation cites an event that makes a “causal difference” in outcome.25
Here’s an example of what Lipton had in mind. Imagine that apaleontologist wants to explain why the dinosaurs went extinct in the
Cretaceous period. He realizes that many events led up to the extinction of thedinosaurs, from the big bang to the formation of continents to the origin of thedinosaurs themselves. All these events are part of the “causal history” ofdinosaur extinction. But of these events, the one that best explains theextinction is the event—the cause—that accounts for the difference between
what happened and what we might have otherwise expected to happen—in
this case, the difference between the extinction of the dinosaurs and thedinosaurs continuing to roam the earth. The big bang is part of the causal
history of extinction of the dinosaurs, but it doesn’t explain the extinction.
Why? It does not account for the difference in outcome. Whether thedinosaurs had continued to live or became extinct, the big bang would still
have been part of either causal history.
As it happens, paleontologists have proposed a theory that cites a causal
difference. As an explanation for a variety of evidence, they have inferred thata massive meteorite hit the earth at the end of the Cretaceous period, causing
an environmental catastrophe. They postulate that the meteorite impact
generated dust and debris that blocked sunlight and cooled the earth,eventually killing the cold-blooded dinosaurs by destroying their food supply.
This explanation illustrates Lipton’s conception of a sound causal
explanation. It explains one event (the extinction of the dinosaurs) by citing
another prior event (the meteorite impact and subsequent environmental
catastrophe) as the cause. It also accounts for the difference between what
happened (the extinction of the dinosaurs) and what otherwise might havebeen expected to happen (the dinosaurs continuing to thrive). Indeed, had themeteorite not hit the earth, the dinosaurs would, presumably, have continued
to live beyond the Cretaceous period. Thus, the meteorite impact could havemade a causal difference. The impact hypothesis also could explain manyother evidences—such as the iridium levels in rocks from the period and thepresence of an impact crater in the Yucatan Peninsula—and so it has broad
explanatory power. None of these facts alone prove the hypothesis is correct,
but they provide support for it and put it in contention as a possible best
explanation.
In Inference to the Best Explanation, Lipton went on to develop an evenmore comprehensive account of causal explanation in order to enhance his
characterization of how the method of inference to the best explanation
works. In the process, he also provided a general, if partial, answer to thequestion, “What makes an explanation best?” Because of his more general
focus on how IBE works, however, Lipton did not address the specificquestions about the nature of historical scientific reasoning that mostinterested me. Nevertheless, his general description of how scientists evaluate
inferences by assessing their explanatory power aptly characterized how
historical scientists evaluate their theories. Indeed, many of his examples
involved scientists trying to explain particular events by reference to pastcauses.
Moreover, his method directly addressed the main problem that often
arose in historical scientific investigations. Lipton noted that sometimes morethan one possible explanation might satisfy his general criteria of “best” byciting a past cause that could conceivably make a causal difference. For
example, paleontologists have proposed other possible causes for theextinction of the dinosaurs—such as a sharp increase in volcanic activity—that could explain why the dinosaurs went extinct rather than continuing toexist. Lipton noted that in such cases, where more than one appropriately
causal explanation is available, scientists use a comparative method of
evaluation and a process of elimination to evaluate competing possible causal
hypotheses.26
At the same time, Lipton did not attempt to establish criteria for
determining which past cause among a group of possible causes constituted
the best explanation in such cases. Fortunately, however, I discovered that thefounders of several historical sciences had developed their own practical
criteria for determining what made an explanation of a past event “best.”
These criteria supplemented Lipton’s work in a way that made them
practically relevant to scientists investigating the past and helped to answer
the question, “What makes an explanation of a past event ‘best’?”
Causes Now in Operation
Historical scientists have developed two key criteria for deciding whichcause, among a group of competing possible causes, provides the best
explanation for some relevant body of evidence. The most important of thesecriteria is called “causal adequacy.” This criterion requires that historicalscientists, as a condition of a successful explanation, identify causes that are
known to have the power to produce the kind of effect, feature, or event inneed of explanation. In making these determinations, historical scientists
evaluate hypotheses against their present knowledge of cause and effect.
Causes that are known to produce the effect in question are judged to be better
candidates than those that are not.
For instance, a volcanic eruption provides a better explanation for an ash
layer in the earth than an earthquake, because eruptions have been observed to
produce ash layers, whereas earthquakes have not. A receding lakeshore
offers a better explanation of ripple marks in sedimentary rock than does avolcanic eruption, because evaporating lakes have been observed to produce
ripple marks, whereas volcanic eruptions have not.
One of the first scientists to develop this principle was the geologist
Charles Lyell, who exerted tremendous influence on the development of
nineteenth-century historical science—and on Charles Darwin, in particular.
Darwin read Lyell’s magnum opus, The Principles of Geology, on the voyageof the Beagle and employed its principles of reasoning in On the Origin ofSpecies. The subtitle of Lyell’s Principles summarized the geologist’s central
methodological principle: Being an Attempt to Explain the Former Changesof the Earth’s Surface, by Reference to Causes Now in Operation. Lyellargued that when historical scientists are seeking to explain events in the past,
they should not invoke unknown or exotic causes, the effects of which we do
not know; instead, they should cite causes that are known from our uniform
experience to have the power to produce the effect in question.27 Historicalscientists should cite “causes now in operation,” or presently acting causes.
This was the idea behind his uniformitarian principle and the dictum, “Thepresent is the key to the past.” According to Lyell, our present experience ofcause and effect should guide our reasoning about the causes of past events.
Or as Whewell explained, “Our knowledge respecting [past] causes…must be
arrived at by ascertaining what the causes of change in such matters cando.”28Darwin himself adopted this methodological principle. His term for apresently acting cause was a vera causa, that is, a true, known, or actual
cause.29 Darwin thought that when explaining past events, scientists shouldseek to identify established causes—causes known to produce the effect inquestion. Darwin appealed to this principle to argue that presently observed
microevolutionary processes of change could be used to explain the origin of
new forms of life in the past.30 Since the observed process of natural selection
can produce a small amount of change in a short time, Darwin argued that itwas capable of producing a large amount of change over a long period oftime.31 In that sense, natural selection was “causally adequate.”
In the late 1950s and early 1960s, a leading philosopher of science fromthe University of California at Berkeley named Michael Scriven made adetailed study of how historical scientists reconstruct the past. He argued that
historical scientists use a distinctive method of reasoning that he called“retrospective causal analysis.”32 In his description of this method ofreasoning, he echoed the importance of the causal-adequacy criterion affirmed
by Darwin and Lyell. According to Scriven, in order to establish the cause ofa past event, historical scientists must first show that any candidate cause “hason other occasions clearly demonstrated its capacity to produce an effect of
the sort here under study.”33 As Scriven put it, historical scientists must showthat “independent evidence supports the claim that it [the cause] can produce
this effect.”34
And Then There Was One
Both philosophers of science and leading historical scientists haveemphasized causal adequacy as the main criterion by which competing
hypotheses are adjudicated. But philosophers of science also have noted thatassessments of explanatory power lead to sound inferences only when it can

be shown that there is one known cause for the effect or evidence in question.
Michael Scriven, for example, points out that historical scientists can makeinferences about the past with confidence when they encounter evidence or acondition for which there is only one known cause.35 When historicalscientists know that there is only one known cause of a given effect, they caninfer the cause from the occurrence of its effect. When scientists can infer auniquely plausible cause, they can avoid the fallacy of affirming theconsequent—the error of ignoring other possible causes with the power toproduce the same effect.36
In his book Reconstructing the Past, Elliott Sober shows that the tasks ofinferring the past may be easy or difficult (or even impossible) depending
upon whether there are many causes that produce the effect in question or justone. He suggests that the severity of the problem confronting historicalscientists will depend on whether the processes linking the present and past
are “information preserving” or “information destroying.”37 To Sober, an
information-preserving process is one that maps a present state to a single
past state, whereas an information-destroying process is one that maps apresent state to a multiplicity of possible past states. He illustrates these twogeneral cases with the following idealized diagram: Sober uses a simple
mechanical system to illustrate what makes reconstructing the past either easy
or difficult. He notes that if someone places a metal ball on the rim of aconcave metal bowl and releases it, it may traverse a complex path before
coming to rest at the bottom of the bowl. Observing the ball in its final resting
position (at equilibrium) will reveal little about the ball’s initial position, since
the ball could have been released from many places on the rim and still ended
up at the bottom of the bowl. Imagine, however, that a ball is rolled down an
indented track with high boundaries on either side and a large hole at thebottom, so that the ball will always come to rest in the hole just below theplace where it is released. In this case, the final resting place of the ball willtell far more about the initial position of the ball. Indeed, the final state willcorrespond to just a single initial state, making it easy to reconstruct the ball’s
initial position from the observation of its final position.38

Figure 7.2. Schematic of the logical problem of retrodiction. Whether
reconstructing the past is easy or difficult depends upon whether there is asingle cause or condition that gives rise to a given present state or whether
there are many possible past causes or conditions that give rise to a given
present state. The diagram on the left portrays an information-destroying
situation, in which many past causes (or conditions) correspond to a given
present state. The diagram on the right portrays an information-preserving
situation, in which only one past cause (or condition) corresponds to a present
state. Adapted from Sober, Reconstructing the Past, 4.
Sober’s illustration suggests that if scientists can discover an effect for
which there is only one plausible cause, they can infer the presence or actionof that cause in the past with great confidence.39 But if they can’t, establishinga past cause is much more difficult. Two logical syllogisms illustrate the nubof the issue. First form:
If X, then Y.
Y occurred.
Therefore X.
Arguments of this form commit the fallacy of affirming the consequent.
Nevertheless, such arguments can be restated in a logically compelling form if
it can be shown that Y has only one known cause (namely, X) or that X is anecessary condition of Y:
X is necessary to the occurrence of Y.
Y exists.
Therefore, X existed.
Thus, this form of argument is a logically valid form of historical inference inwhich a past cause or event can be established from the effect alone.40 For
instance, the archaeologist who knows that scribes are the only known causeof linguistic inscriptions will, upon discovering tablets containing ancient
writing, infer scribal activity. The historical geologist who knows that a
volcanic eruption is the only known cause of widespread deposits of volcanic
ash will infer that a past eruption caused the ash layer discovered today. As
British philosopher Walter Bryce Gallie noted, where a particular past cause is
known to be necessary to produce a subsequent effect, the occurrence of “theeffect is taken as sufficient to establish the occurrence of the cause.”41

Another Case
But what do historical scientists do in the more difficult case where there ismore than one possible cause—more than one causally adequate explanation—of the evidence in question? Scientists will look for additional evidence that
can help discriminate the explanatory power of the remaining explanations.
Ideally in such situations, scientists try to discover an additional fact or groupof facts for which there is only one adequate causal explanation. In otherwords, if scientists investigating the past can’t find a single fact that can be
explained uniquely well (by reference to the only cause known of that type offact), they try to find a wider group of facts for which only one adequate
explanation exists.42
In many such cases, of course, the investigators will have to work their
way back to the actual cause one painstaking step at a time. For instance, bothwind shear and compressor-blade failure could explain an airline crash, but
the forensic investigator will want to know which one did or if the true causelies elsewhere. If both causes can explain the brute fact of the crash, theinvestigator will need to seek additional evidence about some specific aspectof the crash that only one of the candidate explanations can explain—maybe
some evidence from the flight recorder or at the crash scene. Ideally, theinvestigator will find an additional piece of evidence or suite of evidences forwhich there is only one known cause, allowing him or her to distinguishbetween competing explanations and to eliminate every explanation but thecorrect one.
Here’s an idealized illustration that I developed with some colleagues toshow how this process works.43 Suppose I wake up from a nap and see thatthe driveway at my house is glistening with water. The car in the driveway isalso wet. From these two facts, or pieces of evidence, what can I conclude?
There are actually several “causally adequate” explanations. It might haverained while I was napping, or the automatic sprinklers may have come on, or
someone may have sprayed the car and driveway with the hose. With only thedata that the driveway and car are wet, all these explanations are credible
possibilities. Each cause cited is known to be capable of producing the effectin question.
How do I decide which of these “causally adequate” explanations best
explains the data? Clearly, I must look for more evidence. As I do, I discoverthat the lawn and the street are perfectly dry and there isn’t a cloud in the sky.
What should I think now? Although the sprinkler theory and the rainstorm
theory are still possible, these explanations are now much less likely in thelight of the new evidence. Rainstorms produce wet driveways, but usually not
wet driveways surrounded by dry yards, streets, and houses. And cloudlessskies aren’t particularly good candidates for producing rain. Thus, althoughthe rainstorm and sprinkler hypotheses provide causally adequate
explanations of the first group of facts, they fail to do so for the second.
But now, suppose I look a little harder and see a bucket with soapy waterand a sponge sitting behind the car. With this additional piece of data, the best
explanation for the whole group of observations becomes obvious: someone
probably washed the car and sprayed the driveway with the hose. The carwash hypothesis now provides the only “causally adequate” explanation of
the whole ensemble of facts. It does this by providing a (merely) adequate
explanation of some facts (the wet car and the wet driveway) and a uniquely
adequate explanation of others (the bucket of soapy water).
This homespun example shows how we often use the method of inference
to the best explanation to determine past causes of events when more than one
cause can explain the same evidence. In my research, I came across many
examples of historical scientists using precisely this strategy.
Here’s a famous example from cosmology. During the late 1940s, twotheories—the big bang and the steady state—could explain the main facts ofcosmology. According to the big-bang theory, the universe had a definite
beginning a finite time ago and has been expanding outward ever since as aresult of its original explosive beginning. According to the steady-state
theory, the universe is expanding, but it has existed eternally in a state ofconstant density. Space has been expanding eternally, and as it does, newmatter is continuously created, thus maintaining the average density of matterin the universe. Thus, both theories could explain the evidence of an
expanding universe, such as the “red shift” of light coming from distantreceding galaxies.
Faced with this situation, cosmologists sought to make additional
observations to determine which theory had superior explanatory power.
When cosmic background radiation was discovered in 1965, it allowed
cosmologists to decide this question. The big-bang theory postulated an early
universe in which all matter was densely compacted, whereas the steady-state
theory did not. If matter had existed in an early high-density state, it
subsequently would have produced a ubiquitous background radiation thatwould have filled the universe as it expanded. Thus, the discovery of the
background radiation helped to establish the superior explanatory power ofthe big-bang theory. In conjunction with other discriminating pieces ofevidence, it also resulted in the rejection of the steady-state model. Despite an
initial parity in explanatory power, the big bang ultimately provided a better,more causally adequate explanation of the full range of relevant evidence than
did the steady state.
These illustrations show that the process of determining the best
explanation often involves generating a list of possible hypotheses, comparing
their known (or theoretically plausible) causal powers against the relevantevidence, looking for additional facts if necessary, and then, like a detective,
progressively eliminating potential but inadequate explanations until, finally,
one causally adequate explanation for the ensemble of relevant evidence
remains. As Scriven explained, such “retrospective causal analysis” “proceedsby the elimination of possible causes.”44
Causal Existence Criterion
There is another way of thinking of the problem of reconstructing the pastwhen there are several possible causes of the same effect. If there is more than
one causally adequate explanation of the same evidence, then scientists needto establish which of the adequate causes was actually present andresponsible for the event in question. For that reason, some philosophers ofscience have argued that “best” explanations of past events must meet two
criteria: causal adequacy and causal existence. To meet this second conditionof a “best” explanation, a scientist must show that a proposed cause is not
only capable of producing the event in question, but that the cause waspresent—that it existed at the right time and place—so as to have had theopportunity to produce the event in question. As Michael Scriven insisted,historical scientists not only have to show that a postulated cause could haveproduced the effect in need of explanation; they need “to show…that thiscause was present.”45
Scriven notes that, for any given effect or phenomenon, there might be “alist of possible causes,” each of which is known to be capable of producing
the effect or event in question.46 Citing one of the possible causes on such alist, however, would not necessarily explain the event, even if the cause is
known to have the power to produce the same kind of event on otheroccasions. Why? Because the event cited as the cause (as opposed to anotherpossible cause) may not have actually occurred. Thus, he states, “We do not
explain [a particular event]…merely by mentioning some possible cause.”47
An example will help to illustrate. In order to explain the extinction of the
Irish elk, a biologist must do more than just mention events that could havecaused the extinction (such as an invasion of predators or the pollution of theelk’s habitat). After all, these possible causal events might not have occurred.
And obviously, if these events didn’t happen, they cannot have caused theextinction and won’t provide the best explanation of it. Instead, a biologist
must also show that one of these events actually occurred, that predators
actually invaded the elk’s environment or that an increase in pollution ruined
the elk’s habitat. Scientists need to establish the past existence of the cause (inproximity to the effect) as well as the cause’s adequacy.
So how do historical scientists establish that a given cause existed in thepast? How can scientists meet this condition of causal existence? They have
two ways of doing this, both of which I have already described. First,
historical scientists can show that a presently acting cause must have beenpresent in the past because the proposed candidate is the only known cause of
the effect in question. If there is only one possible cause of a salient piece ofevidence, then clearly the presence of that evidence establishes the past
existence of its cause. Second, historical scientists can establish the existenceof a cause by examining a wider class of facts to show that only one of thepossible causes explains the whole ensemble (i.e., the main fact that needs
explanation as well as other associated facts). If there is only one cause
capable of producing all the relevant facts, then, again, that cause must havebeen present in the past in order to produce them.
This latter strategy was used in the wet-driveway illustration. Since there
were many possible causes by which the driveway could have gotten wet,determining which cause was actually responsible for the wet driveway
required comparing the explanatory power of the various hypotheses against agroup of associated facts. This evaluation established that only one of theotherwise adequate causes of the wet driveway also provided an adequate
explanation of all the other relevant facts. Thus, it also established thatsomeone had washed the car and that the causal factors implied by that
hypothesis—a spraying hose and sloshing bucket—had been present in thepast. Thus, the car-wash hypothesis met the causal existence criterion (and,having already met the causal-adequacy criterion, also qualified as the best
explanation).48
Clearly, the way historical scientists go about meeting the causalexistence criterion involves further assessments of causal adequacy, so inpractice the two criteria are closely related. Meeting one criterion (causal
existence) depends upon how well a hypothesis meets the other (casualadequacy)—either with respect to a single salient fact or a wider ensemble ofassociated facts. For this reason, many historical scientists may not make any
explicit mention (or even be aware) of seeking to meet a separate causalexistence criterion. Indeed, they need not do so, because they can ensure that
this criterion gets met just by identifying a uniquely adequate causal
explanation, either with respect to a single salient fact or with respect to awider ensemble. If a single fact does not sufficiently discriminate theexplanatory power of competing hypotheses, then historical scientists
intuitively seek additional facts until they eliminate all but the best (mostcausally adequate) explanation. In so doing, they meet the causal-existence
criterion and identify the best explanation at the same time.
The Nature of Historical Science

Even so, Michael Scriven describes the historical method of “retrospective
causal analysis” as a simple three-part method of evaluating proposed causal
explanations. Many historical scientists find it helpful to think of it this way.
According to Scriven, in order to establish a causal claim, the historicalscientist needs: (1) “evidence that his candidate [cause] was present” and (2)evidence that “it has on other occasions clearly demonstrated its capacity to
produce an effect of the sort here under study.” Additionally, the historicalscientist needs to establish that there is (3) an “absence of evidence (despite athorough search)…of…other possible causes.”49
Many fields that seek to explain events by reference to past causes—suchas forensic science, evolutionary biology, paleontology, geology, archeology,
and cosmology—use this kind of “retrospective causal analysis.”50 Indeed,
many scholars think that Charles Darwin structured his case in the Origin toshow that natural selection met both the causal-adequacy and causal-existence
conditions of a best explanation51 and, in so doing, that he was able to test his
theory as well.
According to Darwin, his theory of “universal common descent” could betested not by using it to predict future outcomes under controlled
experimental conditions, but by showing that it could explain already known
facts in a better, a more causally adequate way than rival hypotheses. As he
explained in a letter to Asa Gray: “I…test this hypothesis [universal common
descent] by comparison with as many general and pretty well-establishedpropositions [facts] as I can find…. And it seems to me that, supposing thatsuch a hypothesis were to explain such general propositions, we ought, in
accordance with the common way of following all sciences, to admit it tillsome better hypothesis be found out.”52
Taking Stock
And so my study of how historical scientists reconstruct past causes led me toconclude that Charles Thaxton had been on to something. There does, indeed,seem to be a distinctive method of historical scientific reasoning andinvestigation. Historical scientists have a distinctive objective (to identify thecauses of past events); they formulate distinctive types of explanation (inwhich they cite past events as causes); and they make inferences with adistinctive (abductive) logical form.
But I concluded that Thaxton was mistaken about something as well. The
historical sciences were not quite as unique as he thought. Historical scientists
use a common method of evaluating their theories in which they evaluate therelative explanatory power of an inference to determine its strength,plausibility, or likelihood. This meant that historical scientific theories, likeother scientific theories, were testable, albeit by reference to distinctively
historical criteria for determining when an explanation qualifies as “best.”
Historical scientists test their theories against the explanatory power of theircompetitors and against our knowledge of the “causal adequacy” or “causal
powers” of various entities. They make these assessments based upon ourpresent knowledge of established cause-and-effect relationships. The criteriaof causal adequacy and to a lesser extent causal existence, developed by Lyell
and used by Darwin, constitute critical experience-based tests of historicalscientific theories, tests that can be used to discriminate the explanatory
power (and merit) of competing hypotheses.
Clearly, this method of testing scientific ideas is different from that usedby experimental scientists, who test their theories by making predictions
about what will happen under controlled laboratory conditions. Even so,
historical scientists are not the only scientists to use it. Arguably, Watson andCrick used this method to test their ideas about the structure of DNA against
competing models. And many scientists—theoretical physicists, biochemists,
psychologists, astronomers, pathologists, medical diagnosticians—as well
historians, detectives, and thinking people everywhere use this method ofreasoning every day to make sense of their experiences.
In any case, whether the historical sciences use an absolutely distinctive
scientific method now seemed less important to me than understanding
exactly how conclusions about past events and causes could be rigorously
established. And my study of how historical scientists formulated theirinferences and arguments gave me a clear understanding of this. Historicalscientists evaluate the strength of competing abductive inferences bycomparing their explanatory power. In the best of cases, they will infer aclearly best explanation—one that cites the “only known cause” of the effector effects in question.
Intelligent Design as a Possible Scientific Explanation?
What did all this have to do with the DNA enigma? Quite simply this. I
wondered if a case for an intelligent cause could be formulated and justified
in the same way that historical scientists justify any other causal claim aboutan event in the past. What I learned about how historical scientists determine
the causes of past events gave me another reason to think that intelligent
design was at least a possible (historical) scientific explanation for the originof biological information. As conceived by advocates of the theory, theactivity a conscious designing agent in the past constituted a causal event (orseries of such events). Moreover, an “intelligent cause” might well have madea causal difference in outcome. The activity of a designing intelligence mightaccount, for example, for the difference between a chaotic prebiotic
environment in which simple chemical constituents interacted with each otherin accord with natural entropic (randomness-producing) processes and an
environment in which systems with highly specific arrangements of chemicals
arose that stored information for building proteins and protein machines.
Intelligent design could have made a causal difference between continuing
chemical chaos and the origin of information-rich molecules.
Moreover, my study of historical scientific reasoning also suggested to me
that it was at least possible to formulate a rigorous case for intelligent design
as an inference to the best explanation, specifically, as the best explanation forthe origin of biological information. Not only was it was possible to conceiveof the purposeful act (or repeated action) of an intelligent agent as a causal
event; the action of a conscious and intelligent agent clearly represented botha known (presently acting) and adequate cause for the origin of information.
Uniform and repeated experience affirms that intelligent agents produce
information-rich systems, whether software programs, ancient inscriptions, orShakespearean sonnets. Minds are clearly capable of generating specified
information.
Conclusion: The Causes Now in Operation
When I first noticed the subtitle of Lyell’s book, “By Reference to Causes
Now in Operation,” a light came on for me. I immediately asked myself:
What causes now in operation produce digital code or specified information?
Is there a known cause—a vera causa—of the origin of such information?
What does our uniform experience tell us? As I thought about this further, it
occurred to me that by Lyell and Darwin’s own rule of reasoning and by theirtest of a sound scientific explanation, intelligent design must qualify as, atleast, a possible scientific explanation for the origin of biological information.
Why? Because we have independent evidence—“uniform experience”—that
intelligent agents are capable of producing specified information. Intelligent
activity is known to produce the effect in question. The “creation of new
information is habitually associated with conscious activity.”53
But is intelligent design the “only known cause” of the origin of specified
information? I was now more than intrigued with this possibility, especiallygiven the implications for the status of the design argument in biology. If
intelligent design turned out to be the only known or adequate cause of theorigin of specified information, then the past action of a designing
intelligence could be established on the basis of the strongest and mostlogically compelling form of historical inference—an inference from theeffect in question (specified information) to a single necessary cause of thateffect (intelligent activity). Moreover, if intelligent design were shown to be
the only known cause, if it were shown to be a uniquely adequate cause, thenintelligent design would also automatically meet the causal-existence criterionof a best explanation as well.
Is intelligent design in fact the best explanation for the origin of life? I
knew that the central question facing scientists trying to explain the origin of
the first life was precisely: How did the sequence-specific digital informationnecessary to building the first cell arise?54 At the same time, I knew thatorigin-of-life researchers had proposed many explanations for the origin of
biological information, including some new and potentially promising
approaches. Though I had studied both Oparin’s classical theory of chemical
evolution and several contemporary alternatives, much of my research had
focused on methodological issues—on how historical scientists reason aboutpast events and causes. These studies convinced me that intelligent design
was a possible—a causally adequate—explanation for the origin of biologicalinformation. But to determine whether intelligent design was the best—the
only causally adequate explanation—I would need to know more about otherscientific possibilities. I would need to follow Scriven’s directive to “make athorough search” for and evaluation of other possible causes. Over the next
several years, as I assumed my duties as an assistant professor, I set out to doexactly that.

8
Chance Elimination and Pattern Recognition

My work in England on historical scientific reasoning convinced me that an
intelligent cause could function as a possible scientific explanation for theorigin of biological information. But I wondered if it could provide a better
explanation than the alternatives. Could it be inferred as the best explanation
for the origin of the specified information in the cell? To answer this question,
I would need to undertake a rigorous examination of the various alternatives.
By the time I finished my Ph.D. studies, in 1990, I was familiar with mostof the main theories then current for explaining the origin of life. These
theories exemplified a few basic strategies of explanation. Some reliedheavily on chance—that is, on random processes or events. Some invokedlawlike processes—deterministic chemical reactions or forces of attraction.
Other models combined these two approaches. In this respect, research on theorigin of life followed a well-established pattern in the sciences, one
explicitly recommended by the leading scientists of our time.
During the period of explosive discovery from 1953 through 1965 now
known as the molecular biological revolution, one of Francis Crick’s
colleagues was the French biologist Jacques Monod. In 1971 he wrote an
influential book called Chance and Necessity, extolling the powers of chancevariation and lawlike processes of necessity in the history of life.1 As an
example of a chance process, Monod noted how random mutations in thegenetic makeup of organisms can explain how variations in populations arise
during the process of biological evolution. In Monod’s shorthand, “chance”
referred to events or processes that produce a range of possible outcomes,
each with some probability of occurring. The term “necessity,” on the other
hand, referred to processes or forces that produce a specific outcome withperfect regularity, so that the outcome is “necessary” or inevitable once someprior conditions have been established. Since, for example, Newton’s law ofgravity stipulates that “all unsuspended bodies fall,” scientists say that any
unsuspended body will “of necessity” fall to the earth. If enough heat is
applied under a pan of water, the water will eventually—of necessity—boil.
In Chance and Necessity, Monod did not try to explain the origin of
biological information or the cell’s system for processing information.
Instead, he presupposed this system and then argued, following Darwin, thatthe subsequent history of life could be explained solely by reference to thetwin factors of chance and necessity. For scientists reluctant to consider
intelligent design as an explanation for the origin of life, Monod’s work
codified a normative set of guidelines for approaching this and every otherscientific problem. His message was clear: scientists can, and should, explain
all phenomena by reference to chance, necessity, or the combination of thetwo. And, indeed, most scientists have sought to explain the origin of life
using one of these approaches.
Following Monod’s order of presentation and the rough sequence inwhich ideas about the origin of biological information appeared after 1953, I
began to examine scenarios that relied primarily on chance. Could the random
interaction of the molecular building blocks of life explain the mysterious
origin of the biological information needed to produce life? Various scientists
have proposed this idea, but many origin-of-life researchers have expressed
reservations about it, because they thought that it amounted to little more thansaying, “We don’t know what caused life to arise.” And, indeed, as I
scrutinized the chance-based explanations for life’s origin, I often found themdisappointing. Specific chance-based proposals were few and far between.
And where present, they were hopelessly vague. Upon reading them, myreaction was often the same: “That’s it?”This situation posed a dilemma for me in my investigation of the DNA
enigma. Though chance was assumed to have played a significant role in theorigin of life, few scientists could say exactly what that meant or offer any
rigorous criteria for evaluating such explanations. Is “chance” a cause that canbe cited to explain something? Is chance even a “something” that can cause
anything? Chance-based explanations were, as a rule, so thin on detail that it
was difficult to assess them.
What Is Chance?
My concerns about chance as an explanation boiled down to a few basic
issues. What does it mean to say that something happened by chance? Whenis it reasonable to invoke chance in an explanation? And what justifies
excluding such an explanation from consideration? Considering a couple ofdifferent situations clarified my thinking about these questions.
In the Pacific Northwest, a now infamous bridge called the Tacoma
Narrows (nicknamed Galloping Gertie) collapsed into the Puget Sound in1940 after the bridge began to undulate more and more violently during a
stiff, but not unusually high, wind. Now imagine that the engineersinvestigating the collapse had come back and, without citing any otherfactors, told the political authorities that the bridge had collapsed “bychance.” Would the authorities have found that explanation satisfying or
informative?
Consider a second scenario. A man named Slick has just won at a Las
Vegas roulette table where the odds of winning are 1 in 38. Although Slick’s
name does not exactly inspire confidence, the croupier at the game table
certifies that he did not cheat and the roulette wheel is operating properly. The
victory was legitimate. Slick is paid in accord with his wager. Why did hewin? What explains the ball falling into the pocket corresponding to Slick’s
bet? The croupier spun the wheel and the ball fell—by chance—into thepocket that Slick had chosen.
A bridge collapsed “by chance.” A gambler won at the roulette table “bychance.” Which of these two explanations seems more satisfactory or
reasonable? Clearly the second, and not the first. But why?
In the second case, it seems legitimate to attribute the event to chance,
because there is a known process, namely, the spinning roulette wheel with its
38 compartments, which can generate the event of interest—the ball landing
on a red 16—with some regular or predictable frequency. To say that thisevent occurred by chance does not mean that chance caused something tohappen, but rather that, given the construction of the roulette wheel, there is adefinite probability—a 1 in 38 chance, to be precise—that the ball will fall in
the red 16 pocket. Absent indicators of other influences at work, we say thatthe ball fell in the red 16 pocket “by chance” to indicate that we think thisevent occurred as one of the normal possible outcomes of a regularunderlying process.
The case of the investigating engineers, however, is different. They did
not specify an underlying process capable of producing bridge failure as apossible outcome. They cited chance purely to conceal what they did notknow about the cause of the bridge failure. Any official receiving this answer
would have correctly regarded it as an admission of ignorance, a fancy way ofsaying, “We don’t know what happened” or “We can’t explain it.”
There’s a second aspect to this, however. When scientists say thatsomething happened by chance, they do not usually mean to deny thatsomething caused the event in question. (Some interpretations of quantum
physics would stand as an exception.) Instead, they usually mean that theevent in question occurred because of a combination of factors so complex
that it would have been impossible to determine the exact ones responsible forwhat happened or to have predicted it. Imagine that I roll a die and it turns up
a five. That outcome was caused or determined by a number of factors—theexact force applied to the die, the orientation of the die as it left my hand, theangle at which it first hit the table, and so on. Saying that the five turned up“by chance” does not deny that the event was determined or caused byphysical factors, but instead that the exact combination of physical factors

was so complex that we cannot know them exhaustively and, therefore, could
not have predicted the precise outcome.
Thus, when scientists say that something happened by chance, they do not
usually mean that some entity called “chance” caused something to happen.
Instead, they mean that there is a process in play that produces a range ofoutcomes each of which has a chance or probability of occurring, including
the outcome in question.
Chance as Negation
But there is usually more to the notion of chance than that. When scientists
attribute an event to chance, they also usually mean there is no good reason tothink the event happened either by design or because of a known physicalprocess that must, of necessity, generate only one possible outcome.
Imagine that a statistician is trying to determine whether a coin is fair byobserving the distribution of heads and tails that result from flipping it. The“chance” hypothesis in this case is that the coin is fair. The alternative
hypothesis is that the coin is biased. If the distribution of heads and tails
comes up roughly even, the statistician will conclude that the coin is fair andelect the “chance hypothesis.” In saying this, the statistician is not saying that“chance” caused the 50–50 distribution or even that she knows what caused
any particular outcome. Instead, the affirmation of the chance hypothesis
mainly serves to negate the alternative hypothesis of bias (in this case, aloaded coin produced by design). The essentially negative character of thechance hypothesis is suggested by its other common name: the null
hypothesis (i.e., the hypothesis to be nullified or refuted by alternative
hypotheses of design or lawlike necessity).2
Such negations, implicit or explicit, are part of what give substantive
chance hypotheses content. To say that the roulette ball fell in the red 16
pocket by chance is also to say that the ball was not placed there intentionallyand that there was nothing about the construction of the roulette wheel thatforced the ball to land in the red 16 of necessity. On the other hand, theengineers who appeal to chance because they don’t know anything about thecause of a bridge collapse don’t make such a negation. Because they know
nothing about the cause, they are unable to rule out alternatives, whether foulplay (design) or some underlying structural failure induced by environmental
conditions acting on a structural defect (necessity). Vacuous appeals to chanceneither affirm a cause nor negate one. But substantive appeals to chance
specify the operation of a relevant outcome-producing process, and they also
implicitly or explicitly negate (or nullify) other possible types of hypotheses.

William Dembski, R. A. Fisher, and Chance Elimination
Of course, just because a chance hypothesis avoids being vacuous by making
definite claims does not necessarily mean that its claims are true. Successful
appeals to chance must be justified, including their implicit negations ofdesign and necessity. So how do statisticians and scientists test substantive(rather than vacuous) chance hypotheses? How do they decide when to accept
and when to reject chance as an explanation?
About the time I was asking myself these questions, I met William
Dembski, a mathematician who later would become one of the best-known
proponents of intelligent design. In the summer of 1992, I returned toCambridge to work on a research project with Dembski and anothercolleague, Paul Nelson, who was then working toward a Ph.D. in thephilosophy of biology at the University of Chicago.
During the preceding year Nelson and I had been approached by aresearch institute about the feasibility of developing a rigorous theory ofintelligent design. The institute wanted to support a small interdisciplinary
team and invited a proposal. Nelson and I thought it would be desirable tofind someone with a background in probability and information theory toround out our team. A week before the proposal was due Nelson stumbled
across two articles by a young mathematician with a recently minted Ph.D.
from the University of Chicago. The articles were impressive. One broke
important new ground in understanding pattern recognition. The otherdescribed how probability theory could be used to assess chance hypotheses.
At the end of one of the articles was a biosketch describing Dembski’s
considerable qualifications. In one line it noted that Dembski wanted to use
his expertise in probability theory to investigate the feasibility of the design
hypothesis. We had found our man, or so we thought.

Figure 8.1. William Dembski. Printed by permission from William Dembski.
Unfortunately, despite concerted effort, Nelson could not locate Dembski,
who was traveling in Germany. Then, the day before the deadline, I received acall from a friend in Texas who wanted to tell me about someone he thought I
should know. Sure enough, he wanted to tell me about Bill Dembski. He
described Dembski as unusually brilliant, yet unassuming and pleasantly
eccentric. He also confirmed that Dembski definitely shared our interests.
Upon hearing of our dilemma, he encouraged me to “pencil him in” on the
proposal and make explanations later. We did. After receiving word that our
proposal had been accepted—contingent on the willingness of all
investigators to participate—Nelson and I finally located Dembski and told
him what we had done. He was surprised, but delighted by our audacity.
My first meetings with Dembski in Cambridge confirmed my friend’s
assessment. Dembski had an extraordinary facility with logical andmathematical formalism and a desire to use these tools to address realscientific questions. He also exhibited some rather endearing eccentricities.
He had prepacked a supply of health food for the summer in order to avoid
the heavy fare in the Cambridge colleges. He also had an odd penchant foranswering questions with illustrations involving coins and dice without
explaining what it was he was using these examples to illustrate. Bysummer’s end, I had enticed him to join me for “beans and chips” in college
and to begin his answers with straightforward assertions before bringing out
the dice and coins to illustrate his point. He, in turn, introduced me to a wayof assessing chance as an explanation for the origin of life.
During the summer, Dembski, Nelson, and I talked frequently about the
problem of assessing chance as an explanation. Dembski introduced us to thework of the pioneering statistician Ronald A. Fisher. During the 1920s, Fisher
had developed a method of statistical hypothesis testing that brought greater
rigor to discussions of chance.3 On the one hand, Fisher’s method
acknowledged that some phenomena could be reasonably explained by, or atleast attributed to, random processes or chance events. On the other hand,
Fisher’s work established a clear method for deciding when chance alone
could be eliminated from consideration as the best explanation. I was
intrigued.
Here’s an example that shows how Fisher’s method of chance elimination
works. Apparently encouraged by his success at the casino, Slick returns thenext day to try his hand at the roulette wheel again. The croupier smiles tohimself. This is how casinos make their money back from the previous day’s
big winners—overconfidence. But then a most unusual morning unfolds.
Over the next few hours, a preternaturally confident Slick bets only on the red
16. He wins not just once at the roulette wheel, but 100 times in a row(fortunately for the casino, he keeps betting only small amounts, thus notbreaking the bank).
The croupier knows, based upon the construction of the wheel, that theprobability of the ball falling into a given pocket is exactly 1 in 38. Thus, theprobability of the ball falling into a given pocket 100 times consecutively is
extremely small, 1 chance in 38100, or 1 chance in 10158. In this situation, thecasino rightly suspects (and long before the 100th spin) that something morethan chance is at work. Either the roulette wheel has a mechanical defect thatcauses the ball to catch and stop in the same place every time or someone ischeating in some way that has not yet been detected. Either way, given theirknowledge of the odds in roulette and the construction of the wheel, thecasino’s managers will conclude that Lady Luck is not the culprit. But why?
On average the ball should land in the red 16 pocket once every 38 spins.
(This is the expected waiting time between successes).4 Dumb luck mightallow it to land there two or three times in 100 spins, but since it has landed in
the winning pocket in every one of the last 100 spins, this will excite concern.
This event cannot reasonably be attributed to the normal function of theroulette wheel, because the frequency of the occurrence of the event inquestion has deviated too much from the frequency that the house rightly
expects. Thus, “chance”—even chance understood in the more substantive
way defined above—no longer seems an appropriate explanation.
According to Fisher, chance hypotheses can be eliminated precisely whena series of events occurs that deviates too greatly from an expected statistical
distribution of events based on what we know about the processes thatgenerate those events or what we know from sampling about how frequently
those events typically occur. Fisher developed a statistical method for
identifying these variations from expectation. That method depends cruciallyon what he calls a “rejection region.”5
Fisher knew that any truly random process would produce a range of
events with some predictable distribution. In the case of roulette, gamblers
will win the game about once every 38 times. A few will win less frequently
than that; some will win more frequently. A very few will win either quite
frequently or rarely. These frequencies can be plotted on a curve that shows
the frequency of winning on one axis (the x axis) and the number of players
who achieve a given frequency on the other (the y axis). The resulting
distribution or pattern is the familiar “bell curve” known to students ofstatistics everywhere (see Fig. 8.2). Events that conform to our expectations
about what should happen fall near the center of the bell curve. Events thatdeviate from expectation fall on the sides of the curve or on the tails. Events
that deviate dramatically from statistical expectation—like Slick winning atroulette 100 times in a row—will fall way out on the tails of the curve, often
so far out that we have no other examples of such events occurring, at least
not by chance alone.
And this was Fisher’s insight. He argued that statisticians could “catch” or“identify” events that resulted from factors other than chance byprespecifying a rejection region. A rejection region is a place on a bell curveor some similar statistical plot that defines a set of outcomes that deviate
dramatically from our statistical expectation—so dramatically, in fact, thatstatisticians think they can reasonably conclude that these events did notoccur from the normal outworking of the statistical process in play. For
example, a statistician working for a casino may specify as a rejection region
any statistically significant set of outcomes in which a roulette player wins
more than 50 percent of the time. When an event occurs that falls within that
rejection region—like Slick winning 100 times in a row—the statistician will
reject the chance hypothesis. Further, by specifying in advance where thoserejection regions lie, Fisher thought statisticians could guard against reading
patterns or significance into a statistical distribution that wasn’t there.
Prespecifying rejection regions could prevent “cherry-picking” the data.6

Figure 8.2. A bell-curve distribution with a prespecified rejection region
shaded.
Fisher’s method made intuitive sense to me, and I wondered if it could
shed light on the question of whether random processes were responsible forthe origin of biological information. But Dembski had other questions on his
mind. He was wondering why Fisher’s method worked in light of a puzzlingfeature of probabilistic reasoning. As a probability theorist, Dembski knewthat intuitively we tend to reject chance as an explanation for extremely
improbable events. Fisher’s method of chance elimination illustrated this.
Clearly, the improbability of Slick’s winning every time contributes to ourskepticism about the chance hypothesis. But there was something puzzling
about this intuitive conviction as well—something that Dembski pointed out
to me. Extremely improbable events—events that don’t necessarily indicate
anything other than chance—happen all the time. Indeed, many extremely
improbable events don’t seem to justify skepticism about chance at all.
Dembski illustrated this by asking me to imagine flipping a coin 100
times and then writing down the exact sequence of heads and tails that turned
up. He pointed out that if I was bored enough to do this, I would actuallyparticipate in an incredibly unlikely event. The precise sequence that occurred
would have a probability of 1 chance in 2100 (or approximately 1 in 1030). Yet
the improbability of this event did not mean that something other than chance
was responsible. After all, some sequence of that improbability had tohappen. Why not this one?
This same puzzle is evident in my example of Slick at the roulette wheel.
When Slick won roulette 100 times in a row, he participated in an incredibly
improbable event. The odds of his winning 100 times in a row wereincredibly low, 1 chance in 10158. (There are only 1080 elementary particles in
the observable universe.) Now imagine that Slick has not won every time, butinstead only two or three times out of 100, more in keeping with the expected
frequency for winning by chance. Imagine also that each time the ball falls ina pocket, Slick records the outcome in hopes of finding a pattern to guide his
future betting. After 100 spins, Slick has compiled a series of 100 outcomes—
red 10, black 17, red 28, black 5, and so on.
What are the odds of this exact series of 100 outcomes occurring? They
are exactly the same as the odds of Slick winning 100 times in a row. Since
the probability of the ball falling in any one of the pockets on any given spinis the same each time (1 in 38), any specific combination of 100 outcomes
will be the same, 1 chance in 38100, or 1 chance in 10158. Even in defeat, Slickwill witness an incredibly improbable event.
The Improbability Paradox
Here we have a paradox. On the one hand, the improbability of Slick’s
winning 100 times in a row seemed to be an important factor in determiningthat something other than chance was at work. After all, had Slick won only
once or twice in a row—something much less improbable—no one would
have suspected anything. On the other hand, it was easy to identify anotherevent just as improbable as his winning 100 times in a row that would not
trigger the same recognition.
The essence of this paradox is that two equally improbable events
nevertheless seem as though they should be explained in two different ways.
This paradox led Dembski to think that assessments of probability alone could
not be the whole reason for rejecting chance. It also led him to ask a series ofimportant questions.
What, in addition to improbability, justifies excluding chance as an
explanation for an improbable event? What was Fisher’s method identifying,other than just an improbable event, that made it a reliable method for
eliminating chance—for catching “something else going on”? Clearly, Slick’s
winning 100 times in a row aroused justifiable suspicion of cheating, whereashis winning only twice out of 100 did not. What was his method identifying in
one case, but not in the other?
As Dembski thought about this, he realized that Fisher was introducing
another factor into his analysis. Fisher’s method was not just analyzing an
improbable event (or sequence of outcomes); it was looking for a significant
pattern within that larger event (or sequence). Fisher’s method compared thefrequency of occurrence of certain kinds of outcomes within a larger event toa statistical norm. For example, it compared the frequency of Slick’s winningbets to the statistical norm for roulette players. Dembski realized that bydefining certain kinds of outcomes, the frequency of winning, for example, as
significant, Fisher had introduced an extra-probabilistic factor into hisanalysis. In other words, Fisher’s method required statisticians to deem
certain kinds of events as significant. But Dembski realized that “a significant
kind of event” is the same thing as a pattern.
Events at the roulette wheel again prove instructive. Consider two kindsof outcomes—two patterns—that would lead statisticians to reject a chance
hypothesis. First, while testing a new roulette table, the croupier at the gaming
house finds that the ball repeatedly falls in the same pocket. Yet on the chance
hypothesis that the wheel is working properly and there has been no outside
interference, the odds of the ball repeatedly falling in exactly the same pocket—say, 100 times in a row—are very low. The croupier suspects thatsomething beyond chance is in play, but again, not just because theprobability of the observed outcome is incredibly small. Clearly, in this case,a pattern has emerged. The same physical event keeps recurring, though not
one that has any effect on a game, since the roulette wheel is being tested onlyat the moment. What should the croupier conclude? The recurrence of theexact same physical outcome suggests to him some physical defect with thewheel. He investigates. Sure enough, there is an inordinately high lip on thebackside of the red 16 pocket. Each time the ball rolls over the red 16 itcollides with the lip and falls—by necessity, not chance—into the same
pocket.
Now consider a second case in which a different kind of pattern triggers
the rejection of the chance hypothesis. In this case, Slick repeatedly changes
his bet, but nevertheless always wins. The exact sequence of bets manifests
no obvious physical pattern (such as the ball repeatedly falling in the red 16).
Instead, a different kind of pattern emerges. In this case, a seemingly random
sequence of different physical outcomes produces a significant outcome in thegame. A specific sequence of physical events combines to achieve asignificant objective—Slick winning a lot of money. Unlike the previous case,where the pattern was evident simply by observing the event itself, the patternin this case corresponds to an improbable series of events that has
significance beyond the physical boundaries of the roulette wheel, asignificance based on other things the croupier or statistician knows (i.e., theobject and rules of the game of roulette).
In this case, the statistician or croupier will again reject the chance
hypothesis (that Slick won fairly and the wheel is working properly) and for agood reason. Once again an outcome has occurred that would be—if nothing
but chance were at work—incredibly improbable. But beyond that thestatistician also recognizes in that outcome a pattern of events that advances agoal or performs a function, namely, winning the game and money. Rejection
regions often implicitly identify patterns of events that advance some goal or
perform some function. Thus, the rejection region that a statistician uses toeliminate chance in this case will not only correspond to an improbablesequence of events, but also to an improbable sequence of victories in thegame. It identifies a frequency of winning that deviates dramatically fromexpectation. And since winning is a significant objective that agents routinely
seek to achieve, a highly improbable string of winning bets suggests theactivity of agents. It suggests cheating, not chance. Conversely, the gaming
commission might suspect that the casino was cheating if the roulette wheel
inflicted a disproportionately high rate of losses on customers, a rateconsistently and significantly higher than the roulette wheel would generate if
it were governed purely by chance.7
Thus, Dembski realized that when we use a statistical plot to track thefrequency of some event occurring, we are implicitly looking for a kind ofpattern, a pattern of events that suggests something other than chance alone. A
pattern of events may present itself as interesting because some physical stateof affairs occurs repeatedly. Or a pattern of events may appear interesting
because it has some independent functional significance apart from thephysical features of the events. Rejection regions sometimes identify
repeating patterns; other times they correspond to what I call “functionallysignificant patterns.” Either way, Dembski realized that implicit in Fisher’s
method of defining rejection regions around “a frequency of occurrence thatdeviates dramatically from expectation” was the identification of specifickinds of events— events that manifested significant or salient patterns.
The role of pattern recognition is even easier to see in cases where we
reject the chance hypothesis without the use of a formal statistical method
such as Fisher’s. Consider another example that illustrates the importance ofpattern recognition to the elimination of chance.
During my first year as a college professor, I was assigned to help teach ateam-taught course on the history of Western philosophy. During a grading
session with the other professors, my senior colleagues read samples ofvarious student papers aloud to showcase both model papers and typicalstudent errors. As I listened, I had a sudden recognition. “Hey,” I said, “I’veread that paper.” One of the other profs chuckled and said, “I know, they allsound the same after a while.” But I persisted. “No,” I said, “I mean, I’ve readthat exact paper, really.” As my colleague continued to read portions of thisstudent essay on Plato, I rifled through my stack. About halfway down thepile, I saw the line he was reading and I finished his sentence for him. He
looked up at me and read some more. I finished his sentence again. An
antiphonal reading ensued. Groans arose from the teaching assistants and theother professors on the team. The two papers were exactly the same. The
students were eventually confronted. After some emotion and vague denials,
one of the two students confessed. It was exactly what it seemed—plagiarism.
After meeting Dembski, I realized that my close encounter with a student
plagiarist aptly illustrated his ideas about why we eliminate chance
hypotheses—in this case, the hypothesis that the two students had justhappened to write the same three typed pages of the same English charactersin the same sequence without one having copied the other or both having
copied a common source.
Why did my colleagues and I eliminate the chance hypothesis in thiscase? Clearly, the odds did have something to do with it. A typical three-page
double-spaced college essay has about 1,000 precisely sequenced English
words, about 6,000 characters and spaces. The probability of one student
generating the exact same sequence of 1,000 words independently, even if
writing on the same topic, was almost incalculably small. Our awareness of
the incredible improbability of this event occurring by chance contributed toour judgment that plagiarism was involved. After all, if the students had justreplicated two or three short words or a short phrase, we would have had
lesser grounds for suspicion, no doubt because the probability of generating a
few short repeats was much, much higher.
Even so, my discussions with Dembski made me aware that the odds
weren’t the whole of the matter in such cases. The odds of getting any
specific sequence of 6,000 characters is roughly the same as the odds ofgetting a sequence of 6,000 characters that exactly matches the sequence of
6,000 characters in another text. So what was the difference? In one case an
improbable event matched another, and in the other case the event stood alone—that is, no such match occurred.
And that was Dembski’s key insight. It wasn’t the improbability of anevent alone that justified the elimination of chance. It was the match between
one event and a pattern that we recognize from another. In detecting
plagiarism, I had recognized not only an event I knew intuitively to be
incredibly improbable (a specific sequence of English words and characters),
but a pattern in that event that I knew from a completely separate event,
namely, my reading of another student’s essay. It was the match, theconvergence of an improbable event with another pattern of letters and words
that made me aware of plagiarism. Pattern recognition helped trigger therejection of chance.
Dembski later developed his ideas about pattern recognition in moredetail. He argued that some kinds of patterns we recognize can, in conjunction
with an improbable event, reliably indicate the activity of a designing
intelligence. Others types of patterns—highly repetitive patterns of the same
physical outcome—such as a coin turning up heads 100 times in a row might
indicate the presence of some physical cause responsible for producing thatoutcome by necessity. In still other cases, we fabricate or imagine patterns
rather than recognize them, and when this occurs the patterns may indicate
nothing at all. (In Chapter 16, I discuss how Dembski developed a test to
identify and exclude such phony patterns from consideration.) In any case, I
came to realize, based on my discussions with Dembski, that patternrecognition played a key role in the rejection of chance.
I also realized that this made sense in light of my own earlier
philosophical reflections about what scientists mean by “chance.” If a
substantive chance hypothesis necessarily negates or nullifies explanations
involving physical-chemical necessity and design, then the presence of apattern necessarily negates chance. The chance hypothesis in effect says,“There is nothing going on in this event to indicate any regular or discernablecausal factors.” Since patterns signal the presence of deeper causal factors or
regularities at work, the presence of patterns negates chance. Because patterns
negate the null hypothesis (that “nothing is going on”) and the null hypothesisis the chance hypothesis, patterns negate the chance hypothesis. Patterns
negate the negation—the negation entailed in a substantive chance
hypothesis.
Ex Post Facto Pattern Recognition and Singular EventsAs Dembski was thinking about the role that pattern recognition plays inchance elimination—and the way rejection regions define significant patterns—he had another key insight. He realized that it wasn’t necessary to
anticipate or prespecify a pattern (or rejection region) in order to justify
rejecting chance, as Fisher’s method required. Dembski regarded Fisher’s
condition as too restrictive, since he could think of many examples in which
recognizing a pattern after the fact, rather than specifying a rejection region
before it, obviously justified the elimination of the chance hypothesis.
For example, Dembski cites the case of a spy listening to and recording
the communication of an adversary in wartime. Before an encoded message isdecrypted, the spy can’t tell whether the combinations of sounds are occurringby chance. Nor can he specify in advance the exact combination of sounds
that would induce him to reject the chance hypothesis—the hypothesis that hehas been listening to random noise or static. Once the spy uncovers thecryptographic key and is able to decode the signals as meaningful text,
however, he realizes—after the fact—that his intercepted signals were notrandom. As Dembski notes, “In contrast to statistics, which always identifies
its patterns before an experiment is performed, cryptanalysis must discover its
patterns after the fact.”8 This same phenomenon occurs daily for most people
as they reflexively reject chance as an explanation for patterns they perceivein events that have already happened, whether a mechanical device or apattern evident in natural objects such as crystals or sand dunes.
As the result of his deliberations, Dembski developed a method of chanceelimination that made the importance of pattern recognition more explicit
than it had been in Fisher’s. Just as historical scientists can often successfully
explain events after the fact that they could not have predicted before the fact,
Dembski argued that it was often possible to discern a pattern after the factthat could not have been specified beforehand (i.e., that was not prespecified).
This allowed historical scientists to use his method to evaluate chance
explanations of past events—events that had already occurred and would notrecur. Since the origin of life constituted precisely such an event, his method
could be used to evaluate the chance hypotheses for the origin of life andbiological information.
Moreover, as I explain in the next chapter, although the scientists working
on the origin of life rarely articulate specific criteria for evaluating chance
hypotheses, they typically employ (if implicitly) Dembski’s criteria inassessing chance explanations for the origin of life. Clearly, Dembski was on
to something.
One Final Factor: Probabilistic Resources
It was now clear to me that the occurrence of an improbable event alone does
not justify eliminating the chance hypothesis. Even so, it was also clear that
judgments about probability did play an important role in our reasoning about
whether something beyond chance is at work. My colleagues and I would nothave accused our students of cheating had they written two essays that justhappened to use a few of the same phrases. Nor would a gambler suspect thata roulette table had a physical defect just because the ball landed in the same
pocket twice in a row. Even though patterns are recognizable in these events,
the events in question are simply not improbable enough to convince us ofwrongdoing.
Pattern recognition can lead us to suspect that something more thanchance is at work. But the presence of a pattern alone does not justify
rejecting chance, any more than an improbable event alone, in the absence ofa pattern, justifies chance elimination. Instead, both the occurrence of an
improbable event and the recognition of a pattern are necessary. Or to makethe point using a logical distinction: the presence of a pattern is a necessary,
but not by itself a sufficient condition of chance elimination. Instead, aquantitative judgment of improbability is also involved.
But this raises another question. How improbable does an event have to
be to justify the elimination of a chance hypothesis? If we begin to detect apattern in an improbable sequence of events, at what point should our doubts
about the chance hypothesis lead us to reject it as untenable or unreasonable?
As a blackjack player repeatedly wins against the house, or a ball repeatedly
falls in the same hole, or as a die repeatedly comes up on the same side, or as
we observe an event that implies that great odds were overcome in the past—at what point do we finally conclude that enough is enough, that something
besides chance must be in play? How improbable is too improbable?
The answer is, “That depends.” It depends on the improbability of theevent in question. But it also depends upon how many opportunities there areto generate the event. Here again my discussions with Dembski helped me
think this through. He noted that there was another common fallacy inreasoning about chance and probability. He had already shown me that somepeople overlook the importance of pattern recognition in our reasoning aboutchance. He now explained that in the case where an improbable event
manifests a pattern, there is another consideration. What matters, he argued, is
not the probability of a particular event or outcome occurring on one occasion(or in one trial), but the probability of generating that event given all theopportunities (or trials) that it has to occur. As Christian de Duve, a Nobel
Prize–winning biochemist who has written several books about the origin oflife, explains, “What counts [in assessing the probability of a chance
hypothesis] is the number of opportunities provided for a given event tohappen, relative to the event’s probability.”9Let’s go back to the roulette table to illustrate this point. It is moderately
improbable that the roulette ball will land in the red 16 pocket twice in a row.
In two spins of the wheel there are 38 × 38, or 1,444, possible outcomes.
Thus, there is just one chance in 1,444 of the ball landing on the red 16 twice
in a row. Nevertheless, if the croupier has 1,444 opportunities to spin thewheel in a week, he will quite likely witness this otherwise unlikely event
sometime during that time. Though the probability of getting two red 16s injust two spins of the roulette wheel is small, the probability of the event
occurring by chance within the week is much higher. The croupier has what
Dembski calls the “probabilistic resources”—the time and number of trials—
necessary to render this event probable.
Dembski uses a coin-tossing scenario to illustrate the concept ofprobabilistic resources and to point up the human tendency to overestimate

the power of our probabilistic resources in overcoming astronomically longodds. He tells a story of a man who had been arrested and convicted for thecrime of running a crooked gambling operation. The convict is brought before
a judge for sentencing. The judge, a gambling man himself, decides to offer
the convict a choice. The judge offers the criminal either ten years in prison ora term in prison that is determined by the outcome of a game of chance.
Specifically, the judge tells the criminal that he can leave prison as soon as heflips a coin and it turns up heads 100 times in a row—with no tails
interrupting the sequence. The judge stipulates that the coin must be fair,biased toward neither heads nor tails. Dembski asks, “What’s the smart
criminal to do?” Should he take the coin-flipping option or the ten years?
Here the criminal’s intuition might lead him astray. He might think, “Even
if it takes two or three years, I’ll be better off than sitting in prison for aguaranteed ten years.” But imagine that before he takes the coin option, his
accountant nudges him from the second row, hands him a calculator, and,
whispering in his ear, reminds him that the judge is one of the people hefleeced with his crooked casino. The criminal stares at the calculator,
perplexed. What’s he supposed to do with that? “Two to the 100th power,” hisaccountant whispers. Then it all comes back to him. As a casino operator, heknows a little about calculating probabilities. He punches 2100 into thecalculator and is greeted by the figure 1.2676506 × 1030. The figure, hesuddenly realizes, is a life sentence without any realistic possibility of parole.
It means that for any given 100-toss series, the man would have a paltry 1
chance in 1,267,650,600,000,000,000,000,000,000,000 of getting all heads.
That ten-year window isn’t enough to give him a realistic chance of flipping
100 heads in a row. A hundred years isn’t enough. A trillion years wouldn’t beenough. The criminal should ignore his initial intuition and trust his
accountant’s calculator.
Dembski explained that if a prisoner flipped a coin at the reasonable rateof once every five seconds for eight hours a day, six days a week, for ten
years he would generate only 17,797,120 (~1.7 x 107) trials.10
Yet this number represents a minuscule portion of the total number ofoutcomes possible (roughly 107 trials out of 1030 total possibilities). Even
taking the number of trials into account, the odds of generating 100 heads in arow in ten years increases to only roughly 1 chance in 1023 (i.e., 107/1030), or
1 in 100,000,000,000,000,000,000,000. Yet this number is obviously far
smaller than 1 in 2 or the 50 percent probability that we usually use to gauge a“reasonable chance of success.” Thus, it is vastly more likely than not that thecriminal will not generate the necessary sequence of 100 heads in a row bychance within ten years. He doesn’t have the necessary “probabilistic
resources.”11

My discussions with Dembski revealed that there are situations in which achance hypothesis can be reasonably rejected. Sometimes there are sufficient
probabilistic resources to make an unlikely event probable (e.g., two red 16s
in a row at the roulette table over the course of a week). And sometimes there
are not (as illustrated by Dembski’s coin-flipping convict). Scientists cannever absolutely prove that some astronomically improbable event could nothave taken place by chance. But they can determine when it is more likely
than not—even vastly more likely than not—that such an event won’t ordidn’t happen by chance alone.
To see this, turn the case of the coin-flipping criminal around. Imagine theconvict arrives in prison and, having accepted the judge’s challenge, begins toflip a quarter in front of a surveillance camera as required by the court toverify any winning result. After two days, the criminal calls for a prison guard
and asks to have the tape reviewed. After viewing the tape of the criminal’s
last 100 flips, the guard and the warden of the prison verify that, yes indeed,
the prisoner flipped 100 heads in a row. What should the court conclude about
the prisoner’s unexpected success? That the prisoner got lucky on one of his
first attempts? This is, of course, a possible, though incredibly improbable,
outcome on the chance hypothesis. But because the court sees a pattern (orfunctional significance) in the event, and because it knows something about
the odds of this event and the prisoner’s probabilistic resources—that theprisoner had few opportunities to beat the odds—the court suspects that theprisoner cheated. So they investigate before granting release. Sure enough,they find that the prisoner snuck a biased quarter into his cell.
Tools for Further Investigation
So what about the origin of life? Could the chance hypothesis explain theorigin of the specified information necessary to produce life in the first place?
As I reflected on Fisher’s method of statistical hypothesis testing and
Dembski’s insights about pattern recognition and probabilistic resources, I
realized that I had some analytical tools with which to address the specificquestions that interested me. My investigation of how chance hypotheses are
generally evaluated suggested a specific line of inquiry and raised several
questions that I would need to answer. Is the chance hypothesis for the originof biological information a substantive hypothesis or merely a vacuous coverfor ignorance? If substantive, are there reasons to affirm the adequacy of thechance hypothesis? Or are there grounds for eliminating chance fromconsideration, at least as the best explanation?
I also wondered: Is there any pattern in the sequence of bases in DNA of
the kind that routinely leads us to doubt chance hypotheses in othersituations? If so, how improbable is the origin of the information necessary to
produce the first life? And finally, what are the relevant probabilistic
resources for generating the information in DNA, RNA, and proteins, and arethey sufficient to render these presumably improbable events probable? The
next two chapters describe what I discovered as I addressed these questions.

9Ends and Odds

As the result of my work with Bill Dembski in the summer of 1992, I now
had a grid by which to evaluate appeals to chance. After I returned from
Cambridge that summer to resume my teaching duties at Whitworth College,
I began to reexamine specific proposals for how the information necessary forthe origin of life might have arisen by chance. Nevertheless, as an assistantprofessor with a heavy teaching load and a father of young children, I didn’t
make as much progress on my research over the next few years as I had
optimistically projected during my heady summer abroad. That might haveturned out for the best, however. I didn’t realize it at the time, but I would bein a much better position to evaluate chance as an explanation for the originof life after Dembski and another scientist I met in Cambridge that summer
had completed their own pieces of the research puzzle.
Even so, I began to evaluate the chance hypothesis as best I could. As I
did, I rediscovered something that had surprised me when I first encountered
it during my graduate studies in England. Most of the leading scientists
investigating the origin of life—none sympathetic to the design hypothesis—
were deeply skeptical of chance alone. Moreover, I now realized that thoughthese scientists rarely articulated explicit criteria for rejecting the chance
hypothesis, they intuitively presupposed criteria that closely resembled thoseexpressed in Fisher’s method of chance elimination, or Dembski’s, or both.
Some also questioned the legitimacy of “chance” because they suspected that
it functioned only as a cover for ignorance.
This skepticism had been, at first, surprising to me, because informal
discussions of the origin of life and even some college-level textbooks often
gave the impression that “chance” was the major factor in the origin of life.
For example, in a memorable passage of his popular college textbook,
Biochemistry, Albert L. Lehninger described how inanimate matter crossed
the great divide to become alive. “We now come to the critical moment inevolution,” he wrote in 1970, “in which the first semblance of ‘life’ appeared,
through the chance association of a number of abiotically formed
macromolecular components.”1 Earlier, in 1954, biochemist George Waldargued for the causal efficacy of chance in conjunction with vast expanses oftime. As he explained, “Time is in fact the hero of the plot…. Given so muchtime, the impossible becomes possible, the possible probable, and theprobable virtually certain.”2 Similarly, in 1968, Francis Crick suggested thatthe origin of the genetic code might be a “frozen accident.”3
Most chance hypotheses assumed, sensibly enough, that life could not
originate without biological information first arising in some form. In extant
cells, DNA stores the information for producing the proteins that perform
most critical functions. It follows then that scientists must explain either (a)where the information in DNA (or perhaps some other equivalent
information-bearing molecule) came from or (b) how proteins might havearisen directly without DNA (or some equivalent source). Chance-basedmodels have taken both these approaches. In some scenarios, scientists
proposed that DNA first arose by chance and then later came into a functional
association with the protein molecules that are needed to transcribe andtranslate genetic information into proteins. In other scenarios, scientists
proposed that protein molecules arose first by chance and then later came into
association with DNA, RNA, and the other molecules that are now part of themodern system of gene expression. More recently, a third approach suggeststhat information first arose in RNA molecules and that RNA then functioned
initially both as a carrier of genetic information (as DNA does now) and as an
enzymatic catalyst (as some proteins do now), although in this model chance
is often coupled with other mechanisms (see Chapter 14).
In nearly all these scenarios, origin-of-life theorists first envisioned somefavorable prebiotic environment rich in the building blocks out of which
DNA, RNA, and proteins are made. Following Oparin’s early theory
proposing that these chemical building blocks collected in the earth’s earlyocean, this hypothesized environment acquired the whimsical name “prebiotic
soup.” Scientists typically imagined subunits of the DNA, RNA, and protein
molecules floating freely in this soup or in some other favorable environment.
The prebiotic soup would not only provide a rich source of the necessary
chemical constituents for building proteins and nucleic acids, but it would
also afford many opportunities for these building blocks to combine andrecombine to form larger biologically relevant molecules.
Thus, chance theories for the origin of biological information typicallyenvision a process of chemical shuffling. This process eventually would
produce large specifically sequenced biopolymers (DNA, RNA, and proteins)by chance starting from an assortment of smaller molecules (such as aminoacids in the case of proteins, or bases, sugars, and phosphates in the case of
DNA and RNA). Some have likened this process to a “cosmic jackpot.” Just
as a series of lucky bets might win a gambler a lot of money, so too, perhaps,
did a series of fortuitous collisions and connections between chemicalbuilding blocks produce the first functional proteins or information-rich

nucleic acids.
But does chance provide an adequate explanation for the origin of
biological information? Or does it merely conceal ignorance of what actually
happened? Do chance-based theories cite processes that are known togenerate information-rich structures with some known or regular frequency?
Or are there good reasons to reject chance as the best explanation for theorigin of the information necessary to life?
These were the questions that animated my investigation of the chance
hypothesis in the years that followed my initial conversations with Bill
Dembski. I knew that many origin-of-life research scientists were deeply
skeptical about explanations that invoked chance alone to explain the originof life. As I investigated this question, I became convinced that thisskepticism was justified—and for several significant reasons.
Functionally Significant Patterns
First, the bases in DNA and RNA and the sequence of amino acids in proteins
do not contain mere Shannon information. Rather, these molecules store
information that is also functionally specified. As such, they manifest one of
the kinds of patterns—a functionally significant pattern—that routinely leadstatisticians to reject chance hypotheses, at least in the case of extremely
improbable events.
To see why the presence of specified information in DNA poses adifficulty for the efficacy of chance processes, recall again our haplessbureaucrat Mr. Jones. When Mr. Jones arrived at the airport in Washington,
D.C., having lost Mr. Smith’s phone number, he did not despair. He knew thatby randomly arranging the numbers 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9 he could
produce a large amount of Shannon information. Of course, he quickly
discovered that he did not need just unspecified information; he needed
specified information. Being naïve about the difference, however, Jones
thought that he could arrange and rearrange the ten digits at random to solve
the problem by making other numbers. And so he did. He tried 258–197–
7292. He tried 414–883–6545. He tried 279–876–2982 and 867–415–5170
and 736–842–3301. He tried hundreds of numbers. In nearly every case, hesucceeded only in getting a recorded message from the phone company.
Twice a person answered only to tell him that he had a wrong number. Jones
kept trying, but he eventually exhausted himself. He simply did not haveenough time to have a reasonable chance of finding the correct (i.e., specified)phone number by chance alone.
Had Jones been more perceptive, he would have learned an importantlesson. Arranging and rearranging characters at random—by chance—canproduce lots of unspecified information. It is not, however, an effective way
to produce specified information, especially if the time available in which to
do so is limited.
During my first few years teaching, I developed another illustration to
help students see why many origin-of-life researchers had come to reject theidea that the specified information necessary to the first life could have arisen
by chance alone. I would take a bag of Scrabble letters and walk through theaisles of my classroom asking each student to pull out one letter at random
from the bag and then walk to the chalkboard and copy that letter on theboard. When all had done so, I pointed out that what we had produced was asequence rich in Shannon information, but no specified information. The
sequence produced at random was complete gibberish. I would then challenge
the class to produce a meaningful sentence, any meaningful sentence, using
this random method.
Occasionally, as if to resist the point of my demonstration, the students
would persist in their efforts until they had produced a very short sequence ofletters that showed some potential for spelling something meaningful, like“ple” or “nes” or even a short word such as “ran.” On these occasions, mystudents would begin to hoot as if they had shown me up—which was exactly
what I was hoping they would do. When this happened, I would stop andquickly calculate the amount of specified information they had produced. I
would then compare that amount to the amount of information in a short
sentence (or functional gene) and ask them to continue selecting letters at
random until they had produced an equivalent amount of specified
information—a complete sentence or even a long word or two. Invariably thesame thing would happen.
After several more selections, whatever promise the students had detectedin their initial string of characters was quickly diluted by more gibberish.
After their failure became apparent, I would challenge them to start over. No
matter how many times they did, they could not produce a long word, letalone a meaningful sentence of any considerable length. By that point, mystudents saw the moral of the story: a random search might produce a small
amount of specified information, but not very much, especially, again, if thetime available to search the possible combinations was limited.
Many origin-of-life scientists have similarly recognized how difficult it isto generate specified biological information by chance alone in the time
available on the early earth (or even in the time available since the beginningof the universe). As one leading biochemist, Alexander Cairns-Smith, wrote
in 1971: “Blind chance…is very limited. Low levels of cooperation he [blindchance] can produce exceedingly easily (the equivalent of letters and small
words), but he becomes very quickly incompetent as the amount oforganization [information] increases. Very soon indeed long waiting periods

and massive material resources become irrelevant.”4
In saying things like this, scientists have recognized that the crucial
problem is not just generating an improbable sequence of chemical
constituents (e.g., an improbable arrangement of nucleotide bases in DNA).
Scientists understand that improbable events happen by chance. Instead, theproblem is relying on a random search or shuffling to generate one of the rare
arrangements of molecules that also performs a biological function. It’s likethe difference between shooting an arrow blindfolded and having it land
anywhere and shooting an arrow blindfolded and having it hit the target. It’smuch harder to do the latter than the former.
Christian de Duve has devised an illustration that underscores theimportance of pattern recognition (and the recognition of functional
specificity) in our reasoning about chance hypotheses. He points out: “Asingle, freak, highly improbable event can conceivably happen. Many highly
improbable events—drawing a winning lottery number or the distribution ofplaying cards in a hand of bridge—happen all the time. But a string of [such]
improbable events—drawing the same lottery number twice, or the samebridge hand twice in a row—does not happen naturally.”5
Here de Duve suggests, like Fisher and Dembski, that an improbable
event or set of conditions does not alone provide grounds for rejecting achance hypothesis. Instead, his illustration indicates that pattern recognition
also plays a key role. As the same person repeatedly wins the lottery or as thesame hand of bridge recurs, a pattern emerges—indeed, one that I have beencalling “a functionally significant pattern.” In de Duve’s examples, animprobable sequence of events has occurred that also produces a significant
objective—winning a game or winning money. De Duve suggests that when
we recognize that a specific sequence of events has produced a significant
outcome—one that achieves a goal or performs a function—we rightly
suspect something more than chance at work.
For this reason, he suggests that we should doubt the chance hypothesis
for the origin of biological information. According to the chance hypothesis,as the molecular building blocks of DNA, RNA, and proteins interact in a
prebiotic environment, these molecular subunits would have attached to one
another to form long chainlike molecules. Amino acids would have attached
to other amino acids to form a growing polypeptide chain. Sugar andphosphate molecules would have attached to each other. Nucleotide bases
would have attached to sugar-phosphate backbones in various arrangements.
Thus, the advocates of the chance hypothesis envision such chains growing
molecule by molecule (or “letter by letter”).
But to form functional genes and proteins, not just any arrangement ofbases or amino acids will do. Not by any means. The overwhelming majority

of arrangements of bases and amino acids perform no biological function atall. So the assembly of working proteins, for example, in the prebiotic soup
would have required functionally appropriate amino acids to attach
themselves by chance to a growing chain of other amino acids—time after
time after time—despite many opportunities for other less fortuitous
outcomes to have occurred. De Duve seems to liken this process to a gambler
repeatedly drawing the winning card or the high hand. In both cases hesuggests that an astute observer should detect a pattern—a pattern of events
that produces a significant outcome.6
De Duve insists that as unlikely patterns of fortuitous events emerge, ourdoubt about the chance hypothesis should increase. Just as we doubt that a
card player could draw the same improbable winning hand time after time
unless he is cheating, so too should we doubt that appropriate amino acids orbases would happen by chance to attach themselves repeatedly to a growing
protein or DNA molecule. In other words, we should be skeptical that the
random interaction of molecular building blocks will produce functionallyspecified sequences in DNA (or proteins) by chance alone.
This makes sense. We know from experience that random searches are
incredibly inefficient means of generating specified sequences of charactersor events—as my Scrabble-bag challenge demonstrated to my students. Thus,
when we encounter functionally specified sequences of characters (ormolecules that function equivalently)—or when we encounter a functionallysignificant pattern in a sequence of events (as we do in de Duve’s gambling
examples)—we are correctly skeptical that chance alone is at work. DNA
contains specified sequences of chemicals that function just like digital or
alphabetic characters: it contains functionally specified information, not justShannon information. As scientists such as de Duve have recognized this,they understandably have become more skeptical about chance as an
explanation for the origin of biological information.7
The Improbability of Producing Genes and Proteins by Chance
There is a second reason for doubting the chance hypothesis as an explanation
for the origin of biological information. Building a living cell not only
requires specified information; it requires a vast amount of it—and theprobability of this amount of specified information arising by chance is“vanishingly small.” But how small? What exactly is the probability that the
information necessary to build the first living cell would arise by chancealone?
It turns out that it was initially hard to quantify the answer to thisquestion, because biologists didn’t know exactly how much information wasnecessary to build and maintain the simplest living cell. For one thing, they
didn’t know how many of the parts of existing cells were necessary tomaintain life and how many weren’t. But beginning in the 1990s, scientists
began to do “minimal complexity” experiments in which they tried to reduce
cellular function to its simplest form. Since then, biologists have been able to
make increasingly informed estimates of the minimum number of proteinsand genes that a hypothetical protocell might have needed to survive.
The simplest extant cell, Mycoplasma genitalium—a tiny bacterium that
inhabits the human urinary tract—requires “only” 482 proteins to perform its
necessary functions and 562,000 bases of DNA (just under 1,200 base pairs
per gene) to assemble those proteins. In minimal-complexity experimentsscientists attempt to locate unnecessary genes or proteins in such simple lifeforms. Scientists use various experimental techniques to “knock out” certain
genes and then examine the effect on the bacterial cell to see if it can survive
without the protein products of the disabled genes. Based upon minimalcomplexity experiments, some scientists speculate (but have notdemonstrated) that a simple one-celled organism might have been able tosurvive with as few as 250–400 genes.
Of course building a functioning cell—at least one that in some way
resembles the cells we actually observe today—would have required morethan just the genetic information that directs protein synthesis. It would havealso required, at the very least, a suite of preexisting proteins and RNA
molecules—polymerases, transfer RNAs, ribosomal RNAs, synthetases, andribosomal proteins, for example—to process and express the information
stored in DNA. In fact, there are over 100 specific proteins involved in a
simple bacterial translation system; roughly 20 more are involved intranscription and over 30 in DNA replication.8 Indeed, although theinformation in DNA is necessary for building proteins, many proteins arenecessary for expressing and processing the information in DNA (see Chapter
5). Extant cells, therefore, need both types of information-rich molecules—
nucleic acids and proteins—functioning together in a tightly integrated way.
Therefore, any minimally complex protocell resembling cells we have today
would have required not only genetic information, but a sizable preexisting
suite of proteins for processing that information.
Building such a cell also would have required other preexisting
components. For example, both proteins and DNA are necessary for building
and maintaining the energy-production and-processing system of the cell, theten-enzyme glycolysis pathway that produces high-energy ATP from its lowenergy precursor ADP. But information-processing and protein synthesis—
and just about everything else in the cell—depends upon a preexisting supply
of ATP or one of the closely related energy-carrying molecules. Thus, for life
to arise in the first place, ATP (or related molecules) must have also beenpresent along with genes and proteins.
Beyond that, the first cell would have required some kind of
semipermeable membrane and a cell wall to protect itself and the chemical
reactions taking place inside it. In modern bacteria, the protective barrier isconstructed by proteins and enzymes that polymerize (link together) the
smaller molecules out of which both the phospholipid bilayer and the cellwall are composed. Cells require a variety of these small molecules—
phosphates, lipids, sugars, vitamins, metals, ATP—in addition to proteins andnucleic acids. Many of these small molecules are synthesized with the help ofproteins in the cell, but proteins also need these molecules in order toaccomplish many of their enzymatic functions. Thus, any protocell likely
would have required a preexisting supply of these smaller molecules toestablish and maintain itself as well.
The integrated complexity of even a “minimally complex cell” has madeit difficult to calculate the odds of all the necessary components of such asystem arising in close association with one another by chance alone.
Nevertheless, as a first-order approximation, many theorists have contented
themselves with calculating the probability of producing just the informationnecessary to build the suite of necessary proteins, while recognizing thatmany more components would also be required and that the probability thus
calculated vastly underestimates the difficulty of the task at hand.
To calculate this probability, scientists typically use a slightly indirect
method. First they calculate the probability of a single functional protein of
average length arising by chance alone. Then they multiply that probability by
the probability of each of the other necessary proteins arising by chance. The
product of these probabilities determines the probability that all the proteinsnecessary to service a minimally complex cell would come together bychance.
Scientists could, of course, analyze DNA to calculate the probability of
the corresponding genetic information arising by chance. But since theinformation in each gene directs the synthesis of a particular protein, the twosequences (the gene sequence and its corresponding protein sequence) carry
the same amount of information.9 Proteins represent, in the parlance of thediscipline, the “expressed function” of the coding region of a DNA molecule.
Further, since measures of probability and information are related (albeit,
inversely), the probability that a particular gene would arise by chance isroughly the same as the probability that its corresponding gene product (theprotein that the gene encodes) would do so.
For that reason, the relevant probability calculation can be made either by
analyzing the odds of arranging amino acids into a functional protein or by
analyzing the odds of arranging nucleotide bases into a gene that encodes that
protein. Because it turns out to be simpler to make the calculation usingproteins, that’s what most origin-of-life scientists have done.10 In any case by
the 1960s, as scientists began to appreciate the complexity of both DNA andproteins and the amount of specified information they contain, many began tosuspect that producing these molecules by chance alone would prove to bequite difficult.
Symposium at the Wistar Institute
The first public inkling of concern about the chance explanation surfaced at a
now famous conference of mathematicians and biologists held in Philadelphia
in 1966. The conference was titled “Mathematical Challenges to NeoDarwinism.”11 It was held at the Wistar Institute, a prestigious medical
research center, and chaired by Sir Peter Medawar, a Nobel laureate fromEngland. The conference was called to discuss the growing doubts of manymathematicians, physicists, and engineers about the ability of random
mutations to generate the information needed to produce new forms of life.
Though the skeptics mainly expressed doubt about the role of random
mutations in biological evolution, the questions they raised had equallyimportant implications for assessing the role of chance in chemical
evolutionary theories about the first life.
These doubts were first discussed at an informal private gathering in
Geneva in the mid-1960s at the home of MIT physicist Victor Weisskopf.
During a picnic lunch the discussion turned to evolution. Several of the MIT
math, physics, and engineering professors present expressed surprise at thebiologists’ confidence in the power of mutations to produce new forms of lifein the time available to the evolutionary process. A vigorous argument
ensued, but was not resolved. Instead, plans were made for a conference todiscuss the concerns of the skeptics.
According to some of these MIT professors, the neo-Darwinianmechanism faced what they called a “combinatorial problem.” In
mathematics, the term “combinatorial” refers to the number of possible waysthat a set of objects can be arranged or combined (the relevant branch ofmathematics is known as “combinatorics”). Some simple bike locks, forexample, have four dials with ten settings on each dial. A bike thief
encountering one of these locks (and lacking bolt cutters) faces a
combinatorial problem because there are 10 × 10 × 10 × 10, or 10,000
possible ways of combining the possible settings on each of the four dials andonly one combination that will open the lock. Guessing at random is unlikely
to yield the correct combination unless the thief has a truly generous amountof time to do so.
The Wistar scientists explained that a similar difficulty confronts theDarwinian mechanism. According to neo-Darwinian theory, new genetic
information arises first as random mutations occur in the DNA of existingorganisms. When mutations arise that confer a survival advantage on theorganisms that possess them, the resulting genetic changes are passed on by
natural selection to the next generation. As these changes accumulate, thefeatures of a population begin to change over time. Nevertheless, natural
selection can “select” only what random mutations first produce. And for theevolutionary process to produce new forms of life, random mutations mustfirst have produced new genetic information for building novel proteins. That,for the mathematicians, physicists, and engineers at Wistar, was the problem.
Why?
The skeptics at Wistar argued that it is extremely difficult to assemble a
new gene or protein by chance because of the sheer number of possible baseor amino-acid sequences. For every combination of amino acids that produces
a functional protein there exists a vast number of other possible combinations
that do not. And as the length of the required protein grows, the number ofpossible amino-acid sequence combinations of that length grows
exponentially, so that the odds of finding a functional sequence—that is, aworking protein—diminish precipitously.
To see this, consider the following. Whereas there are four ways tocombine the letters A and B to make a two-letter combination (AB, BA, AA,
and BB), there are eight ways to make three-letter combinations (AAA, AAB,
ABB, ABA, BAA, BBA, BAB, BBB), and sixteen ways to make four-letter
combinations, and so on. The number of combinations grows geometrically,
22, 23, 24, and so on. And this growth becomes more pronounced when the setof letters is larger. For protein chains, there are 202, or 400, ways to make a
two-amino-acid combination, since each position could be any one of 20
different alphabetic characters. Similarly, there are 203, or 8,000, ways tomake a three-amino-acid sequence, and 204, or 160,000, ways to make asequence four amino acids long, and so on. As the number of possiblecombinations rises, the odds of finding a correct sequence diminishes
correspondingly. But most functional proteins are made of hundreds of aminoacids. Therefore, even a relatively short protein of, say, 150 amino acids
represents one sequence among an astronomically large number of otherpossible sequence combinations (approximately 10195).
Consider the way this combinatorial problem might play itself out in thecase of proteins in a hypothetical prebiotic soup. To construct even one shortprotein molecule of 150 amino acids by chance within the prebiotic soup
there are several combinatorial problems—probabilistic hurdles—toovercome. First, all amino acids must form a chemical bond known as a
peptide bond when joining with other amino acids in the protein chain (seeFig. 9.1). If the amino acids do not link up with one another via a peptidebond, the resulting molecule will not fold into a protein. In nature many othertypes of chemical bonds are possible between amino acids. In fact, whenamino-acid mixtures are allowed to react in a test tube, they form peptide andnonpeptide bonds with roughly equal probability. Thus, with each amino-acidaddition, the probability of it forming a peptide bond is roughly 1/2. Once
four amino acids have become linked, the likelihood that they are joined
exclusively by peptide bonds is roughly 1/2 × 1/2 × 1/2 × 1/2 = 1/16, or(1/2)4. The probability of building a chain of 150 amino acids in which all
linkages are peptide linkages is (1/2)149, or roughly 1 chance in 1045.
Second, in nature every amino acid found in proteins (with one exception)
has a distinct mirror image of itself; there is one left-handed version, or Lform, and one right-handed version, or D-form. These mirror-image forms arecalled optical isomers (see Fig. 9.2). Functioning proteins tolerate only lefthanded amino acids, yet in abiotic amino-acid production the right-handed
and left-handed isomers are produced with roughly equal frequency. Taking
this into consideration further compounds the improbability of attaining a
biologically functioning protein. The probability of attaining, at random, only
L–amino acids in a hypothetical peptide chain 150 amino acids long is(1/2)150, or again roughly 1 chance in 1045. Starting from mixtures of D-forms
and L-forms, the probability of building a 150-amino-acid chain at random inwhich all bonds are peptide bonds and all amino acids are L-form is,therefore, roughly 1 chance in 1090.

Figure 9.1. Amino acids link together when the amino group of one aminoacid bonds to the carboxyl group of another. Notice that water is a by-productof the reaction (called a condensation reaction).
Functioning proteins have a third independent requirement, the mostimportant of all: their amino acids, like letters in a meaningful sentence, mustlink up in functionally specified sequential arrangements. In some cases,changing even one amino acid at a given site results in the loss of protein

function. Moreover, because there are 20 biologically occurring amino acids,
the probability of getting a specific amino acid at a given site is small—1/20.
(Actually the probability is even lower because, in nature, there are also many
nonprotein-forming amino acids.) On the assumption that each site in a
protein chain requires a particular amino acid, the probability of attaining aparticular protein 150 amino acids long would be (1/20)150, or roughly 1chance in 10195.
Nevertheless, molecular biologists have known for a while that most sites
along the chain can tolerate several of the different twenty amino acids
commonly found in proteins without destroying the function of the protein,though some cannot. At the Wistar conference this tolerance was cited bysome of the evolutionary biologists as evidence that random mutations might
be sufficient to produce new functional proteins. Most of the mathematiciansat the conference had assumed that the functional sequences were incrediblyrare within the space of all the possible combinations. But some of theevolutionary biologists asked: What if functional sequences are morecommon than the mathematicians are supposing? This raised an importantquestion, one that could not be answered in 1966: How rare, or common, arethe functional sequences of amino acids among all the possible sequences ofamino acids in a chain of any given length?

Figure 9.2. Two optical isomers of the same amino acid.
In the late 1980s, several important studies were conducted in thelaboratory of MIT biochemist Robert Sauer in order to investigate thisquestion. His research team used a sampling technique known as “cassettemutagenesis” to determine how much variance among amino acids can be
tolerated at any given site in several proteins. This technique would help
resolve an important issue. If proteins can tolerate a lot of variance, then thatwould increase the probability that a random search through the space ofpossibilities would find a functional sequence. If proteins were more finicky—if the requirements of functionality imposed more rigid or restrictedconstraints on sequencing—then that would decrease the probability of arandom process successfully producing a functional protein. Thus, whatever
Sauer’s team discovered would be extremely significant. The results of theirexperiments could help determine the probability that a functional protein

would arise by chance from a prebiotic soup.
So what did they find? Their most clear-cut experiments12 seemed toindicate that, even taking the possibility of variance into account, theprobability of achieving a functional sequence of amino acids in several
known (roughly 100 amino acid) proteins at random is still “exceedingly
small,” about 1 chance in 1063 (to put this in perspective, there are 1065 atoms
in our galaxy).13 Using a variety of mutagenesis techniques, they and otherscientists showed that proteins (and thus the genes that produce them) arehighly specified relative to biological function.14 Earlier studies indicated thatamino-acid residues at many sites cannot vary without functional loss.15 Now
Sauer and others demonstrated that even for sites that do admit somevariance, not just any amino acid will do. Instead, they showed that functional
requirements place significant constraints on sequencing at sites where somevariance is allowed. By quantifying that allowable variance, they made it
possible to calculate the probability of finding a protein with a functionalsequence among the larger ensemble of combinatorial possibilities.
I first learned about the work of Robert Sauer and its relevance toassessing the chance hypothesis in 1992 from a postdoctoral researcher at
Cambridge University named Douglas Axe. Axe and I met in the summer ofthat year as I was conducting research with Bill Dembski. Axe had come to
Cambridge to perform mutagenesis experiments that were similar to Sauer’s.
During his Ph.D. work at Caltech, Axe learned about the structure of proteins
and the intricate folds they need in order to perform their functions. He began
to wonder how difficult it was to produce these folds by random mutations or
random molecular interactions. He began to ask a very similar question to the
one that had been asked at the Wistar conference: How rare, or common, arethe amino-acid sequences that produce the stable folds that make it possible
for proteins to perform their biological functions?
As Axe began to examine Sauer’s experimental method, he asked whether
Sauer might have underestimated how much protein sequences can vary andstill maintain function. To test this possibility, he developed a more rigorous
method of estimating this allowable variability in order to eliminate possible
estimation error. Axe liked what Sauer had done, but wanted to produce amore definitive answer. He wanted to settle the key question from the Wistar
conference beyond any reasonable doubt. His interest in the subject
eventually led him to the Cambridge University laboratory of Alan Fersht,
and then to the Center for Protein Engineering at the famous Medical
Research Council Centre in Cambridge, where the likes of Francis Crick, Max
Perutz, John Kendrew, and Fred Sanger had worked in the early 1960s. Axe’s
work was published in a series of papers in the Journal of Molecular Biology,
Biochemistry, and the Proceedings of the National Academy of Sciencesbetween 1996 and 2004.

Figure 9.3. Douglas Axe. Printed by permission from Douglas Axe.
The results of a paper he published in 2004 were particularly telling.16
Axe performed a mutagenesis experiment using his refined method on afunctionally significant 150-amino-acid section of a protein called betalactamase, an enzyme that confers antibiotic resistance upon bacteria. On thebasis of his experiments, Axe was able to make a careful estimate of the ratioof (a) the number of 150-amino-acid sequences that can perform thatparticular function to (b) the whole set of possible amino-acid sequences ofthis length. Axe estimated this ratio to be 1 to 1077.
This was a staggering number, and it suggested that a random process
would have great difficulty generating a protein with that particular function
by chance. But I didn’t want to know just the likelihood of finding a proteinwith a particular function within a space of combinatorial possibilities. I
wanted to know the odds of finding any functional protein whatsoever withinsuch a space. That number would make it possible to evaluate chance-based
origin-of-life scenarios, to assess the probability that a single protein—any
working protein—would have arisen by chance on the early earth.
Fortunately, Axe’s work provided this number as well.17 Axe knew that innature proteins perform many specific functions. He also knew that in order toperform these functions their amino-acid chains must first fold into stable
three-dimensional structures. Thus, before he estimated the frequency of
sequences performing a specific (beta-lactamase) function, he first performedexperiments that enabled him to estimate the frequency of sequences that willproduce stable folds. On the basis of his experimental results, he calculated
the ratio of (a) the number of 150-amino-acid sequences capable of folding
into stable “function-ready” structures to (b) the whole set of possible aminoacid sequences of that length. He determined that ratio to be 1 to 1074.

Since proteins can’t perform functions unless they first fold into stablestructures, Axe’s measure of the frequency of folded sequences withinsequence space also provided a measure of the frequency of functional
proteins—any functional proteins—within that space of possibilities. Indeed,by taking what he knew about protein folding into account, Axe estimated theratio of (a) the number of 150-amino-acid sequences that produce any
functional protein whatsoever to (b) the whole set of possible amino-acidsequences of that length. Axe’s estimated ratio of 1 to 1074 implied that theprobability of producing any properly sequenced 150-amino-acid protein at
random is also about 1 in 1074. In other words, a random process producing
amino-acid chains of this length would stumble onto a functional protein only
about once in every 1074 attempts.
When one considers that Robert Sauer was working on a shorter proteinof 100 amino acids, Axe’s number might seem a bit less prohibitively
improbable. Nevertheless, it still represents a startlingly small probability. In
conversations with me, Axe has compared the odds of producing a functional
protein sequence of modest (150-amino-acid) length at random to the odds offinding a single marked atom out of all the atoms in our galaxy via a blindand undirected search. Believe it or not, the odds of finding the marked atom
in our galaxy are markedly better (about a billion times better) than those offinding a functional protein among all the sequences of corresponding length.
This was a very significant result. Building on the work of Robert Sauer
and others, Axe established a reliable quantitative estimate of the rarity offunctional sequences within the corresponding space of possible amino-acidsequences, thereby providing an answer to the question first posed at the
Wistar symposium. Though proteins tolerate a range of possible amino acids
at some sites, functional proteins are still extremely rare within the whole setof possible amino-acid sequences.
In June 2007, Axe had a chance to present his findings at a symposiumcommemorating the publication of the proceedings from the original Wistar
symposium forty years earlier. In attendance at this symposium in Boston was
retired MIT engineering professor Murray Eden, with a still incisive mind at
the age of eighty-seven. Eden had been one of the original conveners of the
Wistar conference and was the one who had most forcefully explained the
combinatorial problem facing neo-Darwinism. Forty years later, Axe’s
experimental work had now confirmed Eden’s initial intuition: the odds areprohibitively stacked against a random process producing functional proteins.
Functional proteins are exceedingly rare among all the possible combinationsof amino acids.
Axe’s improved estimate of how rare functional proteins are within“sequence space” has now made it possible to calculate the probability that a

150-amino-acid compound assembled by random interactions in a prebiotic
soup would be a functional protein. This calculation can be made bymultiplying the three independent probabilities by one another: the probabilityof incorporating only peptide bonds (1 in 1045), the probability ofincorporating only left-handed amino acids (1 in 1045), and the probability ofachieving correct amino-acid sequencing (using Axe’s 1 in 1074 estimate).
Making that calculation (multiplying the separate probabilities by adding their
exponents: 1045 + 45 + 74) gives a dramatic answer. The odds of getting evenone functional protein of modest length (150 amino acids) by chance from aprebiotic soup is no better than 1 chance in 10164.
It is almost impossible to convey what this number represents, but let metry. We have a colloquial expression in English, “That’s like looking for aneedle in a haystack.” We understand from this expression that finding theneedle will be difficult because the needle—the thing we want—is mixed inwith a great number of other things we don’t want. To have a realistic chanceof finding the needle, we will need to search for a long, long time. Now
consider that there are only 1080 protons, neutrons, and electrons in theobservable universe. Thus, if the odds of finding a functional protein bychance on the first attempt had been 1 in 1080, we could have said that’s likefinding a marked particle—proton, neutron, or electron (a much smaller
needle)—among all the particles in the universe (a much larger haystack).
Unfortunately, the problem is much worse than that. With odds standing at 1
chance in 10164 of finding a functional protein among the possible 150amino-acid compounds, the probability is 84 orders of magnitude (or powersof ten) smaller than the probability of finding the marked particle in the whole
universe. Another way to say that is the probability of finding a functional
protein by chance alone is a trillion, trillion, trillion, trillion, trillion, trillion,
trillion times smaller than the odds of finding a single specified particle
among all the particles in the universe.
And the problem is even worse than this for at least two reasons. First,
Axe’s experiments calculated the odds of finding a relatively short protein bychance alone. More typical proteins have hundreds of amino acids, and inmany cases their function requires close association with other protein chains.
For example, the typical RNA polymerase—the large molecular machine thecell uses to copy genetic information during transcription (discussed in
Chapter 5)—has over 3,000 functionally specified amino acids.18 The
probability of producing such a protein and many other necessary proteins bychance would be far smaller than the odds of producing a 150-amino-acidprotein.
Second, as discussed, a minimally complex cell would require many more
proteins than just one. Taking this into account only causes the improbability

of generating the necessary proteins by chance—or the genetic information to
produce them—to balloon beyond comprehension. In 1983 distinguished
British cosmologist Sir Fred Hoyle calculated the odds of producing theproteins necessary to service a simple one-celled organism by chance at 1 in
1040,000.19 At that time scientists could have questioned his figure. Scientists
knew how long proteins were and roughly how many protein types there werein simple cells. But since the amount of functionally specified information ineach protein had not yet been measured, probability calculations like Hoyle’s
required some guesswork.
Axe’s experimental findings suggest that Hoyle’s guesses were pretty
good. If we assume that a minimally complex cell needs at least 250 proteinsof, on average, 150 amino acids and that the probability of producing just one
such protein is 1 in 10164 as calculated above, then the probability of
producing all the necessary proteins needed to service a minimally complex
cell is 1 in 10164 multiplied by itself 250 times, or 1 in 1041,000. This kind of
number allows a great amount of quibbling about the accuracy of various
estimates without altering the conclusion. The probability of producing theproteins necessary to build a minimally complex cell—or the genetic
information necessary to produce those proteins—by chance is unimaginably
small.
Conclusion
Axe’s work confirmed the intuitions of an older generation origin-of-liferesearchers and other scientists—such as Harold Morowitz, Fred Hoyle,
Chandra Wickramasinghe, Alexander Cairns-Smith, Ilya Prigogine, Hubert
Yockey, Christian de Duve, Robert Shapiro, and Francis Crick (after 1980)20—who were deeply skeptical about the chance hypothesis. Many of thesescientists had performed their own calculations in which they assumed
extremely favorable prebiotic conditions, more time than was actuallyavailable on the early earth, and maximally fast reaction rates between thechemical constituents of proteins, DNA, and RNA. Invariably, suchcalculations have fueled greater skepticism about the chance hypothesis,
especially since origin-of-life researchers also recognize that DNA andproteins possess functionally specified, rather than just Shannon, information.
But I knew from my discussions with Dembski and from my own
research on the origin-of-life problem that there were other reasons to doubt
the chance hypothesis. In the next chapter, I discuss why these otherconsiderations, in conjunction with the factors discussed here, justify
eliminating chance from consideration as the best explanation for the origin of
the information necessary to produce the first life.

10
Beyond the Reach of Chance

As I investigated the question of whether biological information might havearisen by chance, it became abundantly clear to me that the probability of thenecessary events is exceedingly small. Nevertheless I realized, based on myprevious conversations with Bill Dembski, that the probability of an event byitself does not alone determine whether the event could be reasonably
explained by chance. The probabilities, as small as they were, were not bythemselves conclusive. I remembered that I also had to consider the numberof opportunities that the event in question might have had to occur. I had totake into account what Dembski called the probabilistic resources.
But what were those resources—how many opportunities did thenecessary proteins or genes have to arise by chance? The advocates of thechance hypothesis envisioned amino acids or nucleotide bases, phosphates,and sugars knocking into each other in an ocean-sized soup until the correct
arrangements of these building blocks arose by chance somewhere. Surely,
such an environment would have generated many opportunities for theassembly of functional proteins and DNA molecules. But how many? And
were there enough such opportunities to render these otherwise exceedingly
improbable events probable?
Here again Bill Dembski’s work gave me a way to answer this question.
Dembski had calculated the maximum number of events that could actually
have taken place during the history of the observable universe.1 He did this toestablish an upper boundary on the probabilistic resources that might beavailable to produce any event by chance.2
Dembski’s calculation was elegantly simple and yet made a powerful
point. He noted that there were about 1080 elementary particles3 in theobservable universe. (Because there is an upper limit on the speed of light,
only those parts of the universe that are observable to us can affect events onearth. Thus, the observable universe is the only part of the universe withprobabilistic resources relevant to explaining events on earth.) Dembski also
noted that there had been roughly 1016 seconds since the big bang. (A few
more have transpired since he made the calculation, but not enough to make adifference!)
He then introduced another parameter that enabled him to calculate themaximum number of opportunities that any particular event would have totake place since the origin of the universe. Due to the properties of gravity,
matter, and electromagnetic radiation, physicists have determined that there isa limit to the number of physical transitions that can occur from one state toanother within a given unit of time. According to physicists, a physicaltransition from one state to another cannot take place faster than light can
traverse the smallest physically significant unit of distance (an indivisible“quantum” of space). That unit of distance is the so-called Planck length of
10–33 centimeters. Therefore, the time it takes light to traverse this smallest
distance determines the shortest time in which any physical effect can occur.
This unit of time is the Planck time of 10–43 seconds.
Knowing this, Dembski was able to calculate the largest number ofopportunities that any material event had to occur in the observable universe
since the big bang. Physically speaking, an event occurs when an elementary
particle does something or interacts with other elementary particles. But sinceelementary particles can interact with each other only so many times per
second (at most 1043 times), since there are a limited number (1080) of
elementary particles, and since there has been a limited amount of time since
the big bang (1016 seconds), there are a limited number of opportunities forany given event to occur in the entire history of the universe.
Dembski was able to calculate this number by simply multiplying thethree relevant factors together: the number of elementary particles (1080)
times the number of seconds since the big bang (1016) times the number ofpossible interactions per second (1043). His calculation fixed the total numberof events that could have taken place in the observable universe since theorigin of the universe at 10139.4 This then provided a measure of theprobabilistic resources of the entire observable universe.
Other mathematicians and scientists have made similar calculations.5
During the 1930s, the French mathematician Emile Borel made a much lessconservative estimate of the probabilistic resources of the universe, which heset at 1050.6 More recently, University of Pittsburgh physicist Bret Van deSande has calculated the probabilistic resources of the universe at a more
restrictive 2.6 × 1092.7 MIT computer scientist Seth Lloyd has calculated thatthe most bit operations the universe could have performed in its history(assuming the entire universe were given over to this single-minded task) is
10120, meaning that a specific bit operation with an improbability
significantly greater than 1 chance in 10120 will likely never occur by chance.8
None of these probabilistic resources is sufficient to render the chance
hypothesis plausible. Dembski’s calculation is the most conservative andgives chance its “best chance” to succeed. But even his calculation confirms

the implausibility of the chance hypothesis, whether chance is invoked to
explain the information necessary to build a single protein or the informationnecessary to build the suite of proteins needed to service a minimally complex
cell.
Recall that the probability of producing a single 150-amino-acid
functional protein by chance stands at about 1 in 10164. Thus, for eachfunctional sequence of 150 amino acids, there are at least 10164 other possible
nonfunctional sequences of the same length. Therefore, to have a good (i.e.,
better than 50–50) chance of producing a single functional protein of thislength by chance, a random process would have to generate (or sample) morethan one-half of the 10164 nonfunctional sequences corresponding to eachfunctional sequence of that length. Unfortunately, that number vastly exceeds
the most optimistic estimate of the probabilistic resources of the entire
universe—that is, the number of events that could have occurred since thebeginning of its existence.
To see this, notice again that to have a good (better than 50–50) chance ofgenerating a functional protein by chance, more than half of the 10164
sequences would have to be produced. Now compare that number (call it .5 ×
10164) to the maximum number of opportunities—10139—for that event tooccur in the history of the universe. Notice that the first number (.5 × 10164)
exceeds the second (10139) by more than twenty-four orders of magnitude, bymore than a trillion trillion times.
What does this mean? It means that if every event in the universe over its
entire history were devoted to producing combinations of amino acids of thecorrect length in a prebiotic soup (an extravagantly generous and even absurd
assumption), the number of combinations thus produced would still represent
a tiny fraction—less than 1 out of a trillion trillion—of the total number of
events needed to have a 50 percent chance of generating a functional protein—any functional protein of modest length by chance alone.
In other words, even if the theoretically maximum number (10139) of
amino-acid sequences possible were generated, the number of candidate
sequences would still represent a minuscule portion of the total possible
number of sequences (of a given length). For this reason, it would be vastly
more probable than not that a functional protein of modest length would nothave arisen by chance—simply too few of the possible sequences would havebeen sampled to provide a realistic opportunity for this to occur. Even taking
the probabilistic resources of the whole universe into account, it is extremely
unlikely that even a single protein of that length would have arisen by chanceon the early earth. (And, as explained in the accompanying endnote, proteinscapable of performing many necessary features of a minimally complex celloften have to be at least 150 amino acids in length. Moreover, there are goodreasons to think that these large necessary proteins could not evolve from
simpler proteins or peptide chains.)9
To see this probabilistic reasoning in everyday terms, imagine that Slickperforms a blind search for a single red marble in a huge gunnysack filledwith 10,000 marbles, the remainder of which are blue. To have a better than
50 percent chance of finding the one red marble, Slick must select more than
5,000 of the marbles. But Slick has only ten seconds in which to sample themarbles. Further, it takes one second to find and put each marble aside inanother jar. Thus, he can hope to sample only 10 out of the 10,000 marbles inthe time available. Is it likely that Slick would find the red marble? Clearlynot. Given his probabilistic resources, he has just 1 chance in 1,000 of finding
the red marble, which is much less than 1 in 2, or 50 percent.10 Thus, it ismuch more likely than not that he will not find the red marble by chance inthe time available.
In the same way, it is much more likely than not that a random process
would not produce (or find) even one functional protein (of modest length) in
the whole history of the universe. Given the number of possible sequencesthat need to be sampled and the number of opportunities available to do so,
the odds of success are much smaller than 1/2—the point at which the chance
hypothesis becomes reasonable (see below). Indeed, the odds of producing asingle functional protein by chance in the whole history of the universe are
actually much smaller than the odds of Slick finding the one red marble in myillustration. And beyond that, of course, the odds of producing the suite ofproteins necessary to service a minimally complex cell by chance alone arealmost unimaginably smaller. Indeed, the improbability of that event—calculated conservatively (see Chapter 9) at 1 chance in 1041,000—completely
dwarfs the probabilistic resources of the whole universe. Taking all thoseresources—10139 possible events—into account only increases the probabilityof producing a minimally complex cell by chance alone to, at best, 1 chancein 1040,861, again, an unimaginably small probability.
And that is the third reason that the origin-of-life researchers haverejected the chance hypothesis. The complexity of the events that origin-oflife researchers need to explain exceeds the probabilistic resources of theentire universe. In other words, the universe itself does not possess theprobabilistic resources necessary to render probable the origin of biologicalinformation by chance alone.
The “Chance” of Chance
I knew from my conversations with Bill Dembski and my study of statistical
hypothesis testing that the occurrence of an improbable event alone does notjustify rejecting a chance hypothesis. To justify eliminating chance, I knewthat it was also necessary to recognize a pattern in an event and to consider
the available probabilistic resources. The calculations presented in theprevious section meet both of these conditions. DNA and proteins manifest
functionally significant patterns. And, given all the probabilistic resources of
the universe, the odds of producing a functional protein of modest length
stands at less than one chance in trillion trillion.
In making this calculation, I have computed what statisticians call a“conditional probability.” A conditional probability measures the probabilityof one thing being true on the assumption that another thing is true. In thiscase, I have calculated the probability of a functional protein occurring by
chance given, or “conditioned on,” a best-case estimate of the relevant
probabilistic resources.
But this calculation has also been “conditioned” on something else. Recall
that all along I had been attempting to determine the odds of a functional
protein occurring by chance. That itself was another kind of conditional or“given.” I had not just been asking: What are the odds that a functional
protein would arise? I had been asking: What are the odds that a functional
protein or a minimally complex cell would arise by chance, given theavailable probabilistic resources? In other words, I had been asking: What are
the odds that a functional protein or a cell would arise given the chance
hypothesis (i.e., given the truth of the chance hypothesis)? Recall that the
chance hypothesis in this case asserts that functional proteins or informationrich DNA molecules arose from the random interactions of molecularbuilding blocks in a prebiotic soup. Framing the question this way—as aquestion about the probability of the origin of proteins given the chance
hypothesis—provided grounds for evaluating whether it was more reasonable
or not to accept the chance hypothesis.
This was important, because I often encounter people who think that it
makes sense to cling to the chance hypothesis as long there was some chance—any chance, however small—that life might have arisen by some specified
or even unspecified random process. They often say things like, “Sure, theorigin of life is overwhelmingly improbable, but as long as there is at least
some chance of life arising by chance, then we shouldn’t reject the possibility
that it did.”This way of reasoning turns out to be fallacious, however, because it fails
to recognize what probabilistic resources can tell us about whether it is morereasonable to accept or reject chance. Consider a case in which allprobabilistic resources have been considered and the conditional probabilityof an event occurring given the chance hypothesis is greater than 1/2. Thatmeans that it is more likely than not that the event in question would haveoccurred by chance (if every opportunity for it to occur had been realized). If

Slick is given not one minute, but, say, twelve hours (720 minutes) to sample
the bag of marbles, it is more likely than not that he would find the red marble
by sampling randomly. In twelve hours Slick can sample 7,200 marbles,
which gives him a better than 50 percent chance of finding the red marble.
Conversely, if after all probabilistic resources have been considered andthe conditional probability of an event occurring by chance is less than 1/2,
then it is less likely than not that the event will occur by chance. In the casethat such an event has already occurred and we have no direct knowledge of
how it occurred, it makes more sense to reject the chance hypothesis than toaccept it.
My earlier illustrations made this point. When our hypothetical criminal
claimed to have flipped 100 heads in a row on his second day of jail,suspicions were rightly raised. Given the improbability of the required
outcome (producing 100 heads in a row by flipping a fair coin) and thelimited probabilistic resources available to the prisoner, the court assumed—
reasonably—that the prisoner had cheated, that something other than chance
had been at work.
Similarly, imagine that after starting my demonstration with Scrabble
letters, I left the classroom for a few minutes and instructed my students tocontinue picking letters at random and writing the results on the board in myabsence. Now imagine that upon my return they showed me a detailed
message on the blackboard such as Einstein’s famous dictum: “God does notplay dice with the universe.” Would it be more reasonable for me to supposethat they had cheated (perhaps, as a gag) or that they had gotten lucky?
Clearly, I should suspect (strongly) that they had cheated. I should reject the
chance hypothesis. Why?
I should reject chance as the best explanation not only because of the
improbability of the sequence my students had generated, but also because ofwhat I knew about the probabilistic resources available to them. If I had madea prediction before I had left the room, I would have predicted that they could
not have generated a sequence of that length by chance alone in the time
available. But even after seeing the sequence on the board, I still should haverejected the chance hypothesis.
Indeed, I should know that my students did not have anything like theprobabilistic resources to have a realistic chance of generating a sequence ofthat improbability by chance alone. In one hour my students could not havegenerated anything but a minuscule fraction of the total possible number of
40-character sequences corresponding to the length of the message they had
written on the board. The odds that they could have produced that sequence—or any meaningful sequence at all of that length—in the time available bychoosing letters at random was exceedingly low—much less than 1/2. They
simply did not have time to sample anything close to the number of 40-

character sequences that they would have needed to have a 50 percent chanceof generating a meaningful sequence of that length. Thus, it would be much
more likely than not that they would not produce a meaningful sequence ofthat length by chance in the time available and, therefore, it was also vastly
more likely than not that something other than chance had been in play.
Decision Time: Assessing the Chance Hypothesis
Following many leading origin-of-life researchers, I came to the sameconclusion about the first life and even the first genes and proteins: it is much
more likely than not that chance alone did not produce these phenomena.
Life, of course, does exist. So do the information-rich biologicalmacromolecules upon which living cells depend. But the probability that evenone of these information-rich molecules arose by chance, let alone the suite ofsuch molecules necessary to maintain or build a minimally complex cell, is sosmall as to dwarf the probabilistic resources of the entire universe. The
conditional probability that just one of these information-rich molecules aroseby chance—in effect, the chance that chance is true—is much less than onehalf. It is less than one in a trillion trillion. Thus, I concluded that it is morereasonable to reject the chance hypothesis than to accept it.
This was an intellectually liberating conclusion. Anyone can claim that a
fantastically improbable event might have occurred by chance. Chance, inthat sense, is always a possible explanation. But it doesn’t follow that chancenecessarily constitutes the best explanation. And following what I knew about
the historical scientific method, I wanted to find the best explanation for theorigin of biological information. When I realized that I did not need toabsolutely disprove the chance hypothesis in order to make an objective
determination about its merits, clarity came. By assessing the probability ofan event in light of the available probabilistic resources, I could determine
whether it was more reasonable to affirm or to reject the chance hypothesis
for that event. When I realized that it was far more reasonable to reject the
chance hypothesis for the origin of functional genes and proteins, I concluded
that chance was not a terribly promising candidate for “best explanation” of
the DNA enigma. Chance was certainly not a more reasonable explanation
than its negation, namely, that something other than chance had been at workin the origin of biological information. Indeed, when I remembered that the
chance hypothesis implicitly negated both design and lawlike necessity, andthat rejecting chance, therefore, meant affirming “something other thanchance” at work, I began to evaluate alternative explanations.
My own reasoning about the chance hypothesis mirrored that of many
origin-of-life researchers, many of whom expressed exasperation at the waysome scientists used “chance” as a catchall explanation or a cover for
ignorance.11 For example, after I first began reading the scientific articles
about the origin of life, I found a spirited critique of chance published in
Nature in 1963, just about the time molecular biologists were first coming togrips with the complexity of DNA and proteins. The paper was written by P.
T. Mora, a senior research biologist at the National Institutes of Health. Here’s
what he wrote:
To invoke statistical concepts, probability and complexity to accountfor the origin and the continuance of life is not felicitous or sufficient.
As the complexity of a molecular aggregate increases, and indeed
very complex arrangements and interrelationships of molecules arenecessary for the simplest living unit, the probability of its existence
under the disruptive and random influence of physico-chemical forces
decreases; the probability that it will continue to function in a certain
way, for example, to absorb and to repair, will be even lower; and theprobability that it will reproduce, still lower. Statisticalconsiderations, probability, complexity, etc., followed to their logicalimplications suggest that the origin and continuance of life is notcontrolled by such principles. An admission of this is the use of aperiod of practically infinite time to obtain the derived result. Usingsuch logic, however, we can prove anything…. When in statisticalprocesses, the probability is so low that for practical purposes infinite
time must elapse for the occurrence of an event, statisticalexplanation is not helpful.12
I had come to much the same conclusion. Not only were the odds
overwhelmingly against life arising by chance even considering all available
probabilistic resources, but the chance hypothesis was usually invoked in a
way that didn’t explain anything. To say that “given infinite time, life might
have arisen by chance” was, in essence, a tautology. Given infinite time,
anything might happen. But that doesn’t explain why life originated here orwhat actually caused it to do so.
Environmental Factors
There were some additional reasons to reject the chance hypothesis. The
chance hypothesis for the origin of information-rich biological molecules
assumes the existence of a favorable prebiotic soup in which an abundant
supply of the chemical building blocks of proteins and nucleic acids couldinteract randomly over vast expanses of time. These chemical building blocks
were thought to have been produced by the kinds of chemical reactions that
Stanley Miller simulated in his famous 1953 experiment. Yet when Stanley
Miller conducted his experiment simulating the production of amino acids onthe early earth, he had presupposed that the earth’s atmosphere was composedof a mixture of what chemists call reducing gases, such as methane (CH4),
ammonia (NH3), and hydrogen (H2). He also assumed that the earth’s
atmosphere contained virtually no free oxygen.13 In the years following
Miller’s experiment, however, new geochemical evidence showed that theassumptions Miller had made about the early atmosphere were incorrect.
Instead, evidence strongly suggested that neutral gases such as carbon
dioxide, nitrogen, and water vapor14—not methane, ammonia, and hydrogen—predominated in the early atmosphere. Moreover, a number of geochemical
studies showed that significant amounts of free oxygen were also present even
before the advent of plant life, probably as the result of the photo-dissociationof water vapor.15
This new geological and geochemical evidence implied that prebiotic
atmospheric conditions were hostile, not friendly, to the production of aminoacids and other essential building blocks of life. As had been well known
even before Miller’s experiment, amino acids will form readily in a mixtureof reducing gases. In a chemically neutral atmosphere, however, reactions
among atmospheric gases will not take place readily, and those reactions that
do take place will produce extremely low yields of biological building
blocks.16 Further, even a small amount of atmospheric oxygen will quench the
production of biologically significant building blocks and cause biomolecules
otherwise present to degrade rapidly.17
An analogy may help to illustrate. Making amino acids in a reducing
atmosphere is like getting vinegar and baking soda to react. Because thereaction releases stored chemical energy as heat, it occurs easily. (It is an
example of what chemists call an “exothermic” reaction.) Trying to makebiological building blocks in a neutral atmosphere, however, is more liketrying to get oil and water to mix.18
Scientists investigating the origin of life haven’t tried to adjust theirprobability calculations in light of these developments. But they haverecognized that these developments do complicate matters further for the
chance hypothesis. To make matters worse, an accumulating body ofgeochemical evidence has shown—perhaps, not surprisingly, in light of theprevious discussion—that there likely never was a prebiotic soup. Twoleading geochemists, James Brooks and Gordon Shaw, argued that if an oceanrich in amino and nucleic acids had existed, it would have left large depositsof nitrogen-rich minerals (nitrogenous cokes) in metamorphosed Precambriansedimentary rocks. No evidence of such deposits exists, however. In thewords of Brooks: “The nitrogen content of early Pre-Cambrian organic matteris relatively low (less than .015%). From this we can be reasonably certain
that: there never was any substantial amount of ‘primitive soup’ on earth
when Pre-Cambrian sediments were formed; if such a soup ever existed it was
only for a brief period of time.”19
Given my own deliberations about what constituted a substantive rather
than a vacuous chance hypothesis, this seemed significant. A substantive
chance hypothesis must invoke a definite process that produces the outcome
in question with some regular or statistically predictable frequency—just as aroulette wheel produces various outcomes with a predictable frequency. The
chance hypothesis envisioned DNA and proteins arising from a random
process of chemical “roulette” in a favorable nitrogen-rich prebiotic ocean. If
no such environment had ever existed, then whatever specificity the chance
hypothesis might have once had was now lost. If there was no “chemicalroulette” in which life would emerge as an inevitable if improbable outcome,chance could no longer be considered a substantive hypothesis; it would
instead be just a vacuous notion that at best concealed ignorance of the truecause of life’s origin.
Additionally, I knew from my Ph.D. work that there were othersignificant, if less quantifiable, problems with the idea that information-richbiomolecules had arisen by chance from a prebiotic soup. Most origin-of-liferesearchers recognized that, even if there had been a favorable prebiotic soup,
many destructive chemical processes would have necessarily been at work at
the same time.20 Simulation experiments of the type performed by Stanley
Miller had repeatedly demonstrated this. They have invariably produced
nonbiological substances in addition to biological building blocks such asamino acids. Without intelligent intervention, these other substances will react
readily with biologically relevant building blocks to form biologicallyirrelevant compounds—chemically insoluble sludge.21 To prevent this fromhappening and to move the simulation of chemical evolution along abiologically promising trajectory, experimenters often remove thosechemicals22 that degrade or transform amino acids into nonbiologically
relevant compounds. They also must artificially manipulate the initialconditions in their experiments. For example, rather than using both short-and
long-wavelength ultraviolet light, which would have been present in any
realistic early atmosphere, they use only short-wavelength UV. Why? The
presence of the long-wavelength UV light quickly degrades amino acids.23
Zero HourI began to wonder if the odds of life arising by chance alone, at least under thecircumstances envisioned by advocates of the chance hypothesis, weren’t
actually zero. Imagine that a casino owner invents a game in which the object
is to roll 777 consecutive “sevens” with a set of dice. He asks the odds makers
to calculate the chances of any one contestant winning. They are, of course,
infinitesimally small. But now he gives the odds makers some additional
information. The dice are made of white chocolate with dark chocolate spots
on the faces, both of which will melt as the result of the glare of the lights
over the game table and repeated handling by the game players. Now what are
the odds of turning up 777 sevens in a row? Clearly, they have diminishedfurther. In fact, they are not just effectively zero, but under thesecircumstances with destructive processes inevitably predominating they are
actually zero. Seven hundred seventy-seven “sevens” will never appear,
because the faces of the dice will be destroyed in the attempt to generatethem. The destructive processes will ensure that the desired outcome will
never occur. I wondered if the same problem didn’t afflict the chance
hypothesis for the origin of life.
In the face of these and other difficulties, most origin-of-life researchers
have decided to consider other theories that do not rely heavily on chance. In
the next chapter, I examine one of the other main contending approaches:
self-organization. Since the odds of a purely random process producing life
are “vanishingly small,” many scientists have concluded that somenonrandom, lawlike process must have been at work to help overcome theseodds. If chance is insufficient, then perhaps “necessity” will do the job.
Christian de Duve expresses the reasoning of researchers who favor thisapproach: “A string of improbable events—drawing the same lottery number
twice, or the same bridge hand twice in a row—does not happen naturally. Allof which lead me to conclude that life is an obligatory manifestation ofmatter, bound to arise where conditions are appropriate.”24
Nevertheless, I should note that a few theorists have attempted to either
retain or resuscitate a role for chance. They have done so in one of two ways.
Either they have attempted to lower the complexity threshold that random
processes must first produce or postulated an increase in the probabilistic
resources available to such processes.
Some theorists, notably those proposing an initial “RNA world,” havesought to retain a role for chance by suggesting that natural selection might
have played a key role in the origin of life, even before the origin of a fully
functioning cell. They propose combining chance with natural selection (orother lawlike processes) as a way of explaining how the first cell arose. In
doing so, they suggest that random processes would have had to produce
much less biological information by chance alone. Once a self-replicating
molecule or small system of molecules had arisen, natural selection would

“kick in” to help produce the additional necessary information. In Chapter 14,
I evaluate theories that have adopted this strategy.
Others theorists have attempted to resuscitate chance theories altogether
by postulating the existence of other possible universes beyond our own.25 In
doing so, they have attempted to increase the probabilistic resources available
for producing biological information and life itself. These other universes
would presumably provide more opportunities to generate favorable
environments in which random processes could generate the vast number of
combinatorial possibilities necessary to give life a realistic chance of arisingsomewhere. Since this idea depends upon highly speculative and technicalcosmological models, I have decided to evaluate it separately, in Appendix B.
There I argue that although it is impossible to disprove the existence of otheruniverses, postulating such universes does not actually provide either asatisfying explanation or, still less, the best explanation of the origin of
biological information. As I explain, there are several reasons that suchmodels fail to do so. For now, however, it is fair to say that most serious
origin-of-life researchers have not found speculations about other possible
universes terribly relevant to understanding what actually happened on theearly earth to produce the first life on our planet. So in the next chapters, I
turn my attention to evaluating the other leading contenders I haveencountered in my investigation of the DNA enigma.

11
Self-Organization and Biochemical Predestination

By 1968, the chance hypothesis was already suspect. The preceding fifteen
years had revealed a cellular realm of stunning complexity, and though it
would take several more decades to pinpoint the exact odds of a functioning
protein or DNA molecule arising by chance alone, many scientists alreadyhad grown skeptical of chance as an explanation for the origin of life. Amongthem was a young biophysicist at San Francisco State University named Dean
Kenyon. As he wrote a year later: “It is sometimes argued in speculative
papers on the origin of life that highly improbable events (such as the
spontaneous formation of a molecule of DNA and a molecule of DNApolymerase in the same region of space at the same time) become virtuallyinevitable over the vast stretches of geological time. No serious quantitativearguments, however, are given in support of such conclusions.” Instead, heargued, “such hypotheses are contrary to most of the available evidence.”1 Toemphasize the point Kenyon noted: “If the association of amino acids were acompletely random event…there would not be enough mass in the entire
earth, assuming it was composed exclusively of amino acids, to make evenone molecule of every possible sequence of…a low-molecular-weightprotein.”2
Kenyon began exploring a different approach. With a Ph.D. from Stanfordand having worked under Melvin Calvin, a leading biochemist and origin-oflife researcher at the University of California, Berkeley, Kenyon was steepedin the scientific culture of the late 1960s. He was well aware of Jacques
Monod’s conceptual dichotomy between chance and necessity. He understoodthat the logical alternative to chance was what Monod called necessity, thelawlike forces of physics and chemistry. If chance events couldn’t explain theorigin of biological information, then, Kenyon thought, perhaps necessity
could. Eventually he and a colleague, Gary Steinman, proposed just such atheory; except that they didn’t call it “necessity.” They called it“predestination”—biochemical predestination. In 1969 Kenyon and Steinman
wrote a book by this title. Through the 1970s and early 1980s it became thebestselling graduate-level text on the origin of life and established Kenyon asa leading researcher in the field.
In Biochemical Predestination, Kenyon and Steinman not only presented

a new theory about the origin of biological information; they also inaugurated
a fundamentally new approach to the origin-of-life problem, one that came tobe called “self-organization.” In physics, the term “self-organization” refers toa spontaneous increase in the order of a system due to some natural process,force, or law. For example, as a bathtub drains, a highly ordered structure, avortex, forms as the water swirls down the drain under the influence ofgravity and other physical forces such as the Coriolis force. In this system, aforce (gravity) acts on a system (the water in the draining tub) to generate astructure (the vortex), thus increasing the order of the system. Selforganizational theories of the origin of life try to attribute the organization inliving things to physical or chemical forces or processes—ones that can be
described mathematically as laws of nature.
Of course, the self-organizational theorists recognized that they needed to
do more than simply assert that life arose by physical or chemical necessity.
They needed to identify a specific lawlike process (or series of processes) that
could generate life or critical components of living cells starting from somespecific set of conditions. In other words, self-organizational theorists needed
to identify deterministic processes that could help overcome the otherwise
long odds against the origin of life occurring by chance alone. And that iswhat Kenyon and those who followed in his wake set out to do. In the process
Kenyon and other self-organizational theorists formulated theories that either
tried to explain, or circumvented the need to explain, the DNA enigma.
More Than the Sum
What accounts for the difference between a chunk of marble and a great
sculpture? What explains the difference between a cloud and a message
created from steam by a skywriter? What accounts for the difference between
a functioning cell and the various molecules that jointly constitute it? These
questions raise the classical philosophical issue of reductionism. Does thewhole reduce to the sum of its parts? Or conversely, do the properties of theparts explain the structure and organization of the whole? As with many suchquestions, the best answer is, “It depends.”
Consider a silver knife, fork, and spoon. The metal in all three has adefinite set of chemical properties describable by physical and chemical laws.
Silver atoms will react with certain atoms, under certain conditions, but notwith others. Silver will melt at certain temperatures and pressures, but not atothers. Silver has a certain strength and resistance to shearing. Insofar as theseproperties reliably manifest themselves under certain specified conditions,they can be described with general laws. Nevertheless, none of theseproperties of silver accounts for the shape of the various utensils and,

therefore, the functions they perform.
From a purely chemical point of view nothing discriminates the silver in aspoon from that in a knife or fork. Nor does the chemistry of these items
explain their arrangement in a standard place setting with the fork on the left
and the knife and spoon on the right. The chemistry of the silverware isindifferent to how the silver is arranged on the table. The arrangement isdetermined not by the properties of the silver or by chemical laws describing
them, but by the choice of a rational agent to follow a human convention.

Figure 11.1. Dean Kenyon. Printed by permission from Dean Kenyon.
Yet one cannot say the same of every structure. Many objects display astructure and order that results from the chemical properties of theiringredients. For example, a crystal of salt has a lattice structure that exhibits astriking, highly repetitive pattern. This structure results from forces of mutual
electrostatic attraction (describable by natural law) among the atoms in thelattice. Similarly, a spiral galaxy has a definite structure that results largely
from lawlike forces of gravitational attraction among the stars in the galaxies.
Structure does not always defy reduction to chemistry and physics.
What about living cells and the complex molecules they contain? Does
their organization derive from the physical and chemical properties of theirparts? Do the chemical constituents of proteins or DNA molecules haveproperties that could cause them to self-organize? Are there physical orchemical forces that make the production of information-rich molecules
inevitable under plausible prebiotic conditions? Dean Kenyon thought theanswer to these questions might well be yes.
Biochemical Predestination
Like Aleksandr Oparin, whom he credited with inspiration, Kenyon sought toaccount for the origin of the first life in a series of gradual steps. Like Oparin,
Kenyon constructed a scenario describing how life might have arisen througha series of chemical transformations in which more complex chemical
structures arose from simpler ones. In Kenyon’s model, simple monomers(e.g., amino acids, bases, and sugars) arose from simpler atmospheric gases
and energy; polymers (proteins and DNA) arose from monomers; primitive
membranes formed around these polymers; and a primitive metabolism
emerged inside these membranes as various polymers interacted chemicallywith one another. Unlike Oparin, however, who relied on chance variations toachieve some of the chemical transformations, Kenyon relied moreexclusively on deterministic chemical reactions. In his scenario, each stage
along the way to the origin of life was driven by deterministic chemical
processes, including the most important stage, the origin of biologicalinformation.
Whereas Oparin had suggested that the process by which monomers
arranged themselves into polymers in the prebiotic soup was essentiallyrandom, Kenyon and Steinman suggested that forces of chemical necessity
had performed the work. Specifically, they suggested that just as electrostaticforces draw sodium (Na+) and chloride (Cl–) ions together into highlyordered patterns within a crystal of salt, amino acids with special affinities foreach other might have arranged themselves to form proteins. As the twoscientists explained, “In the same fashion that the difference in the nature ofreactivity of the units of a growing inorganic crystal determines the final
constitution of the three-dimensional crystal array, so differences inreactivities of the various amino acids with one another could possibly serve
to promote a defined ordering of sequence in a growing peptide chain.”3
Kenyon and Steinman first came to this idea while performing “di-mer”
bonding experiments in the laboratory of Melvin Calvin at the University ofCalifornia, Berkeley. They wanted to see whether specific amino acids bondmore readily with some amino acids than others. Their experimental results
suggested that they do. For example, they discovered that glycine forms
linkages with alanine twice as frequently as glycine with valine. Moreover,they discovered that these differences in chemical affinity seemed related todifferences in chemical structure. Amino acids with longer side chains bondless frequently to a given amino acid than do amino acids with shorter side
chains.4 Kenyon and Steinman summarized their findings in a table showing
the various differential bonding affinities they discovered.
In the wake of these findings, Kenyon and Steinman proposed that thesedifferences in affinity imposed constraints on the sequencing of amino acids,
rendering certain sequences more likely than others. As they put it, “It would
appear that the unique nature of each type of amino acid as determined by its
side chain could introduce nonrandom constraints into the sequencing
process.”5 They further suggested that these differences in affinity mightcorrelate with the specific sequencing motifs typical in functional proteins. If
so, the properties of individual amino acids could themselves have helped tosequence the amino-acid chains that gave rise to functional proteins, thereby
producing the information the chains contain. Biochemical necessity could
thus explain the seeming improbability of the functional sequences that exist
in living organisms today. Sequences that would be vastly improbable in theabsence of differing bonding affinities might have been very probable whensuch affinities are taken into account.
Kenyon and Steinman did not attempt to extend this approach to explain
the information in DNA, since they favored a protein-first model.6 They knewthat proteins perform most of the important enzymatic and structuralfunctions in the cell. They thought that if functional proteins could arise
without the help of nucleic acids, then initially they need not explain theorigin of DNA and RNA and the information they contained. Instead, theyenvisioned proteins arising directly from amino acids in a prebiotic soup.
They then envisioned some of these proteins (or “proteinoids”) forming
membranes surrounding other proteins. Thus, the two were convinced that
only later, once primitive metabolic function had arisen, did DNA and RNA
need to come on the scene. Kenyon and Steinman did not speculate as to how
this had happened, content as they were to think they may have solved themore fundamental question of the origin of biological information by showingthat it had arisen first in proteins.
Doubts About Self-Organization
Did Kenyon and Steinman solve the DNA enigma? Was the informationnecessary to produce the first life “biochemically predestined” to arise on theearly earth? Surprisingly, even as their bold self-organizational model grew inpopularity among origin-of-life researchers, Kenyon himself began to doubt
his own theory.7
Kenyon’s doubts first surfaced in discussions with one of his students at
San Francisco State University. In the spring of 1975 near the end of asemester-long upper-division course on evolution, a student began to raisequestions about the plausibility of chemical evolution.8 The student—
ironically named Solomon Darwin—pressed Kenyon to examine whether his
self-organizational model could explain the origin of the information in DNA.
Kenyon might have deflected this criticism by asserting that his protein-firstmodel of self-organization had circumvented the need to explain theinformation in DNA. But by this point he found himself disinclined to makethat defense.
For some time Kenyon himself had suspected that DNA needed to play amore central role in his account of the origin of life. He realized that whether
functional proteins had arisen before DNA or not, the origin of informationrich DNA molecules still needed explanation, if for no other reason thanbecause information-rich DNA molecules exist in all extant cells. At somepoint, DNA must have arisen as a carrier of the information for building
proteins and then come into association with functional proteins. One way or
another, the origin of genetic information still needed to be explained.
Now he faced a dilemma. Having opted for a self-organizational
approach, he had only two options for explaining the information in DNA.
Either (a) the specific sequences of amino acids in proteins had somehow
provided a template for sequencing the bases in newly forming DNA
molecules or (b) DNA itself had self-organized in much the same way he andSteinman supposed proteins had. As he reflected more on Solomon Darwin’s
challenge, Kenyon realized that neither option was very promising. First,
Kenyon knew that to propose that the information in proteins had somehow
directed the construction of DNA would be to contradict everything thenknown about molecular biology. In extant cells, DNA provides the template of
information for building proteins and not the reverse. Information flows from
DNA to proteins. Moreover, there are several good reasons for thisasymmetry. Each triplet of DNA bases (and corresponding RNA codons)
specifies exactly one amino acid during transcription and translation. Yet
most amino acids correspond to more than one nucleotide triplet or RNA
codon. This feature of the genetic code ensures that information can flow
without “degeneracy,” or loss of specificity, in only one direction, from DNA
to proteins and not the reverse.
Additionally, Kenyon realized that for structural and chemical reasons,proteins made poor candidates for replicators—molecules that can function as
easily copied informational templates. Unlike DNA, proteins do not possess
two antiparallel strands of identical information and thus cannot be unwoundand copied in the way DNA can. Further, proteins are highly reactive once
they are unwound (due to exposed amino and carboxl groups and exposedside chains). For this reasons, most “denatured” (unwound) proteins tend to
cross-link and aggregate. Others are quickly destroyed in the cell. Either way,
denatured proteins tend to lose their structural stability and function.
Moreover, they do not regain their original three-dimensional shape or
activity once they lose it. By contrast, DNA is a stable, chemically inert
molecule that easily maintains its chemical structure and composition whileother molecules copy its information. For all these reasons, it seemed difficult
to envision proteins serving as replicators of their own stored information.
Indeed, as Kenyon later told me, “getting the information out of proteins andinto DNA” would pose an insuperable conceptual hurdle.
And there was another difficulty. By the late 1980s new empiricalfindings challenged the idea that amino-acid bonding affinities had produced
the biological information in proteins. Although Kenyon and Steinman had
shown that certain amino acids form linkages more readily with some aminoacids than with others,9 new studies showed that these differential affinities
do not correlate with actual sequencing patterns in large classes of known
proteins.10 In other words, differential bonding affinities exist, but they don’t
seem to explain (or to have determined) the specific sequences of amino acids
that actual proteins now possess. Instead, there was a much more plausible
explanation of these new findings, one consistent with the Crick’s central
dogma. The amino-acid sequences of known proteins had been generated by
the information encoded in DNA—specifically, by the genetic information
carried in the DNA of the organisms in which these proteins reside. After all,proteins in extant cells are produced by the gene-expression system. Of
course, someone could argue that the first proteins arose directly from aminoacids, but now it was clear there was, at the very least, no evidence of that in
the sequences of amino acids in known proteins.
All this would later reinforce Kenyon’s conviction that he could not
circumvent the need to explain the origin of information in DNA by positing aprotein-first model. He would have to confront the DNA enigma.
Kenyon realized that if the information in DNA did not arise first inproteins, it must have arisen independently of them. This meant that the basesequences of DNA (and the molecule itself) must have self-organized under
the influence of chemical laws or forces of attraction between the constituent
monomers. Yet based upon his knowledge of the chemical structure of the
DNA molecule, Kenyon doubted that DNA possessed any self-organizational
properties analogous to those he had identified in amino acids and proteins.
He was strengthened in this conclusion by reading an essay about DNA by adistinguished Hungarian-born physical chemist and philosopher of science
named Michael Polanyi. As it happened, this same essay would shape myown thinking about the viability of self-organizational theories as well.
Life Transcending Physics and Chemistry
I first encountered Dean Kenyon in 1985, sixteen years after the publicationof Biochemical Predestination. I was in the audience in Dallas during thepresentation in which Kenyon announced that he had come to doubt all
current chemical evolutionary theories of the origin of life—including his
own. Thus, when I arrived in Cambridge, I already knew that one of theleading proponents of “self-organization” had repudiated his own work.
Nevertheless, I did not yet fully appreciate why he had done so. I learned
from Charles Thaxton that Kenyon had begun to doubt that his protein-firstmodels eliminated the need to explain the origin of information in DNA. But I
didn’t yet see why explaining the DNA enigma by reference to selforganizing chemical laws or forces would prove so difficult.
That began to change as I encountered the work of Michael Polanyi. In
1968, Polanyi published a seminal essay about DNA in the journal Science,“Life’s Irreducible Structure,” and another essay in the Chemical andEngineering News the year before, “Life Transcending Physics andChemistry.” During my first year of graduate study, I wrote a paper about thetwo essays and discussed them at length with my supervisor, Harmke
Kamminga, since it seemed to me that Polanyi’s insights had profound
implications for self-organizational models of the origin of life.
Polanyi’s essays did not address the origin of life question directly, butrather a classical philosophical argument between the “vitalists” and“reductionists” (discussed in Chapter 2). Recall, that for centuries, vitalists
and reductionists had argued about whether a qualitative distinction existed
between living and nonliving matter. Vitalists maintained that livingorganisms contained some kind of immaterial “vital force” or spirit, an elanvital that distinguished them qualitatively from nonliving chemicals.
Reductionists, for their part, held that life represented merely a quantitatively
more complex form of chemistry. Thus, in their view, living organisms, likecomplex machines, functioned as the result of processes that could be“reduced” or explained solely by reference to the laws of physics andchemistry.
The molecular biological revolution of the 1950s and 1960s seemed toconfirm the reductionist perspective.11 The newly discovered molecularmechanisms for storing and transmitting information in the cells confirmed
for many biologists that the distinctive properties of life could, as Francis
Crick put it in 1966, “be explained in terms of the ordinary concepts ofphysics and chemistry or rather simple extensions of them.”12 As Richard
Dawkins later wrote, the discovery of DNA’s role in heredity “dealt the final,
killing blow to the belief that living material is deeply distinct from nonliving
material.”13
But had it really? Even if biochemists were no longer looking for somemysterious life force, was it really clear that living things could be explained
solely by reference to the laws of physics and chemistry?
Polanyi’s answer turned the classical reductionism-vitalism debate on its
head. He did this by challenging an assumption held by reductionists andvitalists alike, namely, that “so far as life can be represented as a mechanism,
it [can be] explained by the laws of inanimate nature.”14 Whereas vitalists hadargued against reductionism by contesting that life can be understoodmechanistically, Polanyi showed that reductionism fails even if one grants

that living organisms depend upon many mechanisms and machines. To show
this, Polanyi argued that even if living organisms function like machines, theycannot be fully explained by reference to the laws of physics and chemistry.15
Consider an illustration. A 1960s vintage computer has many parts,
including transistors, resistors, and capacitors. The electricity flowing throughthese various parts conforms to the laws of electromagnetism, for example,
Ohm’s law (E = IR, or voltage equals current times resistance). Nevertheless,the specific structure of the computer, the configuration of its parts, does not
result from Ohm’s or any other law. Ohm’s law (and, indeed, the laws ofphysics generally) allows a vast ensemble of possible configurations of thesame parts. Given the fundamental physical laws and the same parts, anengineer could build many other machines and structures: different modelcomputers, radios, or quirky pieces of experimental art made from electricalcomponents. The physical and chemical laws that govern the flow of current
in electrical machines do not determine how the parts of the machine arearranged and assembled. The flow of electricity obeys the laws of physics, butwhere the electricity flows in any particular machine depends upon thearrangement of its parts—which, in turn, depends on the design of anelectrical engineer working according to engineering principles. And theseengineering principles, Polanyi insisted, are distinct from the laws of physics
and chemistry that they harness.
Polanyi demonstrated that the same thing was true of living things. He did
this by showing that communications systems, like machines, defy reduction
to physical and chemical law and by showing further that living organisms
contain a communications system, namely, the gene-expression system inwhich DNA stores information for building proteins.
Polanyi argued that, in the case of communications systems, the laws ofphysics and chemistry do not determine the arrangements of the charactersthat convey information. The laws of acoustics and the properties of air do not
determine which sounds are conveyed by speakers of natural languages.
Neither do the chemical properties of ink determine the arrangements ofletters on a printed page. Instead, the laws of physics and chemistry allow avast array of possible sequences of sounds, characters, or symbols in any codeor language. Which sequence of characters is used to convey a message is notdetermined by physical law, but by the choice of the users of thecommunications system in accord with the established conventions ofvocabulary and grammar—just as engineers determine the arrangement of theparts of machines in accord with the principles of engineering.
Thus, Polanyi concluded, communications systems defy reduction tophysics and chemistry for much the same reasons that machines do. Then hetook a step that made his work directly relevant to the DNA enigma: heinsisted that living things defy reduction to the laws of physics and chemistrybecause they also contain a system of communications—in particular, theDNA molecule and the whole gene-expression system. Polanyi argued that, aswith other systems of communication, the lower-level laws of physics andchemistry cannot explain the higher-level properties of DNA. DNA basesequencing cannot be explained by lower-level chemical laws or properties
any more than the information in a newspaper headline can be explained byreference to the chemical properties of ink.16 Nor can the conventions of thegenetic code that determine the assignments between nucleotide triplets andamino acids during translation be explained in this manner. Instead, thegenetic code functions as a higher-level constraint distinct from the laws ofphysics and chemistry, much like a grammatical convention in a humanlanguage.
Polanyi went further, arguing that DNA’s capacity to convey information
actually requires a freedom from chemical determinism or constraint, inparticular, in the arrangement of the nucleotide bases. He argued that if thebonding properties of nucleotides determined their arrangement, the capacityof DNA to convey information would be destroyed.17 In that case, thebonding properties of each nucleotide would determine each subsequent
nucleotide and thus, in turn, the sequence of the molecular chain. Under these
conditions, a rigidly ordered pattern would emerge as required by theirbonding properties and then repeat endlessly, forming something like acrystal. If DNA manifested such redundancy, it would be impossible for it tostore or convey much information. As Polanyi concluded, “Whatever may be
the origin of a DNA configuration, it can function as a code only if its order is
not due to the forces of potential energy. It must be as physicallyindeterminate as the sequence of words is on a printed page.”18
DNA and Self-Organization
Polanyi’s argument made sense to me. DNA, like other communication
systems, conveys information because of very precise configurations ofmatter. Was there a law of physics or chemistry that determined these exact
arrangements? Were there chemical forces dictating that only biologicallyfunctional base sequences and no others could exist between the strands of the
double helix? After reading Polanyi’s essays, I doubted this.
I realized that his argument also had profound implications for selforganizational theories of the origin of life. To say that the information in
DNA does not reduce to or derive from physical and chemical forces impliedthat the information in DNA did not originate from such forces. If so, thenthere was nothing Kenyon could do to salvage his self-organizational model.
But was Polanyi correct? How did we know that the constituent parts of

DNA did not possess some specific bonding affinities of the kind that Kenyon
and Steinman had discovered in amino acids? Polanyi argued largely on
theoretical grounds that the nucleotide bases could not possess suchdeterministic affinities and still allow DNA to store and transmit information.
How do we know that such affinities do not exist?
In 1987 when I first encountered Polanyi’s argument, I did not yet
understand the chemical structure of DNA well enough to answer thisquestion. And Polanyi, who no doubt did, did not bother to explain it in his
articles. So the question lingered in the back of my mind for several years
after completing my Ph.D. Not until the mid-1990s, when I began to make amore systematic evaluation of self-organizational theories, did I find an
answer to it.
I remember vividly the day the breakthrough came. I was listening to acolleague, a biologist at Whitworth College, teach a college class about thediscovery of the double helix when I noticed something about the chemical
structure of DNA on the slide that she had projected on the screen. What I
noticed wasn’t anything I hadn’t seen before, but somehow its significance
had previously escaped me. It not only confirmed for me Polanyi’s conclusion
about the information in DNA transcending physics and chemistry, but it also
convinced me that self-organizational theories invoking bonding affinities orforces of attraction would never explain the origin of the information thatDNA contains.
Figure 11.2 shows what I saw on the slide that suddenly seized myattention. It portrays the chemical structure of DNA, including the chemicalbonds that hold the molecule together. There are bonds, for example, between
the sugar and the phosphate molecule forming the two twisting backbones of
the DNA molecule. There are bonds fixing individual nucleotide bases to thesugar-phosphate backbones on each side of the molecule. There are alsohydrogen bonds stretching horizontally across the molecule between
nucleotide bases, forming complementary pairs. The individually weakhydrogen bonds, which in concert hold two complementary copies of the
DNA message text together, make replication of the genetic instructions
possible. But notice too that there are no chemical bonds between the bases
along the longitudinal axis in the center of the helix. Yet it is precisely along
this axis of the DNA molecule that the genetic information is stored.

Figure 11.2. Model of the chemical structure of the DNA molecule depicting
the main chemical bonds between its constituent molecules. Note that no
chemical bonds link the nucleotide bases (designated by the letters in boxes)in the longitudinal message-bearing axis of the molecule. Note also that thesame kind of chemical bonds link the different nucleotide bases to the sugarphosphate backbone of the molecule (denoted by pentagons and circles).
These two features of the molecule ensure that any nucleotide base can attach
to the backbone at any site with equal ease, thus showing that the propertiesof the chemical constituents of DNA do not determine its base sequences.
Adapted by permission from an original drawing by Fred Hereen.
There in the classroom this elementary fact of DNA chemistry leaped out
at me. I realized that explaining DNA’s information-rich sequences byappealing to differential bonding affinities meant that there had to bechemical bonds of differing strength between the different bases along theinformation-bearing axis of the DNA molecule. Yet, as it turns out, there are
no differential bonding affinities there. Indeed, there is not just an absence ofdiffering bonding affinities; there are no bonds at all between the criticalinformation-bearing bases in DNA.19 In the lecture hall the point suddenlystruck me as embarrassingly simple: there are neither bonds nor bonding
affinities—differing in strength or otherwise—that can explain the origin of
the base sequencing that constitutes the information in the DNA molecule. A
force has to exist before it can cause something. And the relevant kind offorce in this case (differing chemical attractions between nucleotide bases)
does not exist within the DNA molecule.
Of course it might be argued that although there are no bonds between thebases that explain the arrangement of bases, there might be either somethingelse about the different sites on the DNA molecule that inclines one baserather than another to attach at one site rather than another. I investigated thepossibility with a colleague of mine named Tony Mega, an organic chemist. I
asked him if there were any physical or chemical differences between thebases or attachment sites on DNA that could account for base sequencing. We
considered this question together for a while, but soon realized that there areno significant differential affinities between any of the four bases and thebinding sites along the sugar-phosphate backbone. Instead, the same type ofchemical bond (an N-glycosidic bond) occurs between the base and the
backbone regardless of which base attaches. All four bases are acceptable;
none is chemically favored.
This meant there was nothing about either the backbone of the moleculeor the way any of the four bases attached to it that made any sequence more
likely to form than another. Later I found that the noted origin-of-lifebiochemist Bernd-Olaf Küppers had concluded much the same thing. As he
explained, “The properties of nucleic acids indicate that all the
combinatorially possible nucleotide patterns of a DNA are, from a chemical
point of view, equivalent.”20 In sum, two features of DNA ensure that “selforganizing” bonding affinities cannot explain the specific arrangement of
nucleotide bases in the molecule: (1) there are no bonds between bases along
the information-bearing axis of the molecule and (2) there are no differential
affinities between the backbone and the specific bases that could account forvariations in sequence.
While I was teaching I developed a visual analogy to help my students
understand why these two features of the chemical structure of DNA havesuch devastating implications for self-organizational models, at least thosethat invoke bonding affinities to explain the DNA enigma. When my children
were young, they liked to spell messages on the metallic surface of ourrefrigerator using plastic letters with little magnets on the inside. One day I
realized that the communication system formed by the refrigerator andmagnetic letters had something in common with DNA—something that could
help me explain why the information in DNA is irreducible to physical orchemical forces of attraction.
To demonstrate this to my students, I would bring a small magnetic

“chalkboard” to class with a message spelled on it, such as “Biology Rocks!”
using the same kind of plastic letters that my kids used at home. I would pointout that the magnetic forces between the letters and the metallic surface of the
chalkboard explain why the letters stick to the board, just as forces ofchemical attraction explain why the nucleotides stick to the sugar-phosphatebackbone of the DNA. But I would also point out that there are no significant
forces of attraction between the individual letters that determine theirarrangement, just as there are no significant forces of attraction between thebases in the information-bearing axis of the DNA molecule. Instead, themagnetic forces between the letters and the chalkboard allow numerous
possible letter combinations, some of which convey functional or meaningful
information and most of which do not.
To demonstrate that the magnetic forces do not dictate any specific letter
sequence, I arranged and rearranged the letters on the board to show that they
have perfect physical freedom to spell other messages or mere gibberish. I
would further note that there are no differential forces of attraction at work to
explain why one letter sticks to the chalkboard at one location rather thananother. I then pointed out that the same is true of DNA: there are nodifferential forces of attraction between the DNA bases and the sites on thesugar-phosphate backbone. In the case of the magnetic board nothing about
the magnetic force acting on the “B” inclines it to attach to the board at thefront of the “Biology Rocks!” sequence rather than at the back or middle.
Instead, each letter can attach to the chalkboard at any location, just as any
one of the nucleotide bases can attach to any sugar molecule along the sugarphosphate backbone of DNA. Forces of attraction (N-glycosidic bonds) do
explain why the bases in DNA attach to the backbone of the molecules, butthey do not explain why any given nucleotide base attaches to the molecule atone site rather than another. Nor, given the absence of chemical bonds
between the bases, do any other bonding affinities or chemical forces internal
to the molecule explain the origin of DNA base sequencing.
I later learned that Kenyon already had reckoned with these same
stubborn facts of molecular biology and reached the same conclusion about
the futility of explaining the arrangement of DNA bases by reference tointernal bonding affinities. The properties of the building blocks of DNA
simply do not make a particular gene, let alone life as we know it, inevitable.
Yet the opposite claim is often made by self-organizational theorists, albeit
without much specificity. De Duve states, for example, that “the processes
that generated life” were “highly deterministic,” making life as we know it“inevitable,” given “the conditions that existed on the prebiotic earth.”21 Yet
imagine the most favorable prebiotic conditions. Imagine a pool of all four
DNA bases and all necessary sugars and phosphates. Would any particular
genetic sequence inevitably arise? Given all necessary monomers, would any

particular functional protein or gene, let alone a specific genetic code,
replication system, or signal transduction circuitry, inevitably arise? Clearlynot.
The most obvious place to look for self-organizing properties to explain
the origin of genetic information is in the constituent parts (the monomers) of
the molecules that carry that information; but biochemistry makes clear thatforces of attraction between the bases in DNA do not explain the specificsequences in these large information-bearing molecules. Because the same istrue of RNA, researchers who speculate that life began in a self-organizing
RNA world also must confront another sequencing problem,22 in particular,
the problem of explaining how information in a functioning RNA molecule
could have first arisen in the absence of differential forces of chemical
attraction between the bases in RNA.23
The Mystery of the CodeRecall from Chapter 5 that, in addition to the specified information in nucleic
acids and proteins, origin-of-life researchers need to account for the origin of
the integrated complexity of the gene-expression and-translation system.
Recall also that the gene-expression system not only utilizes the digital
information inscribed along the spine of the DNA molecule (the genetic text);
it also depends on the genetic code or translation system imbedded in thetRNA molecule (along with its associated synthetase proteins). (The genetic
code is to the genetic information on a strand of DNA as the Morse code is toa specific message received by a telegraph operator.)
It turns out that it is just as difficult to explain the origin of the genetic
code by reference to self-organizational bonding affinities as it is to explain
the origin of the genetic “text” (the specific sequencing of the DNA bases).
The next several paragraphs describe why in some detail. Those unfamiliar
with the relevant facts may find it useful to consult the accompanying figure(Figure 11.3) or to skip ahead and pick up the main thread of the argument
beginning with the heading “The Necessity of Freedom.”
Self-organizational theories have failed to explain the origin of the genetic
code for several reasons. First, to explain the origin of the genetic code,
scientists need to explain the origin of the precise set of correspondences
between specific nucleotide triplets in DNA (or codons on the messenger
RNA) and specific amino acids (carried by transfer RNA). Yet molecularbiologists have failed to find any significant chemical interaction between thecodons on mRNA (or the anticodons on tRNA) and the amino acids on theacceptor arm of tRNA to which the codons correspond. This means that forcesof chemical attraction between amino acids and these groups of bases do notexplain the correspondences that constitute the genetic code.

Figure 11.3. The transfer-RNA molecule showing the anticodon on one end of
the molecule and an attached amino acid on the other. Notice that there is no
direct chemical interaction between the amino acid and the nucleotide codon
that specifies it.
Instead, the mRNA codon binds not to the amino acid directly, but to theanticodon triplet on the tRNA molecule. Moreover, the anti-codon triplet andamino acid are situated at opposite ends of tRNA. They do not interact
chemically in any direct way. Although amino acids do interact chemicallywith a nucleotide triplet at the 3' acceptor end of the tRNA molecule, thetriplet remains the same in all twenty tRNA molecules, meaning the bonds
there do not display any differential affinities that could explain the differing
amino-acid assignments that constitute the code. All twenty tRNA molecules
have the same final triplet of bases (ACC) at the 3' arm where their aminoacids attach. Since all twenty amino acids in all twenty tRNA molecules
attach to the same nucleotide sequence, the properties of that nucleotide
sequence clearly do not determine which amino acids attach and which donot. The nucleotide sequence is indifferent to which amino acid binds to it(just as the sugar-phosphate backbone in DNA is indifferent to which
nucleotide base binds to it). All twenty triplets are acceptable; none ispreferred.
Thus, chemical affinities between nucleotide codons and amino acids do
not determine the correspondences between codons and amino acids thatdefine the genetic code. From the standpoint of the properties of theconstituents that comprise the code, the code is physically and chemicallyarbitrary. All possible codes are equally likely; none is favored chemically.
Moreover, the discovery of seventeen variant genetic codes has put to restany doubt about this.24 The existence of many separate codes (multiple sets ofcodon–amino acid assignments) in different microorganisms indicates that thechemical properties of the relevant monomers allow more than a single set ofcodon–amino acid assignments. The conclusion is straightforward: thechemical properties of amino acids and nucleotides do not determine a single
universal genetic code; since there is not just one code, “it” cannot be
inevitable.
Instead, scientists now know the codon–amino acid relationships thatdefine the code are established and mediated by the catalytic action of sometwenty separate proteins, the so-called aminoacyl-tRNA synthetases (one foreach tRNA anticodon and amino-acid pair). Each of these proteins recognizes
a specific amino acid and the specific tRNA with its corresponding anticodonand helps attach the appropriate amino acid to that tRNA molecule. Thus,
instead of the code reducing to a simple set of chemical affinities between asmall number of monomers, biochemists have found a functionallyinterdependent system of highly specific biopolymers, including mRNA,
twenty specific tRNAs, and twenty specific synthetase proteins, each ofwhich is itself constructed via information encoded on the very DNA that ithelps to decode. But such integrated complexity was just what needed
explanation in the first place. The attempt to explain one part of the integrated
complexity of the gene-expression system, namely, the genetic code, byreference to simple chemical affinities leads not to simple rules of chemical
attraction, but instead to an integrated system of large molecular components.
One aspect of the DNA enigma leads to another.
Certainly, the chemical interactions between the functional polymers inthis complex translation system proceed deterministically. But to explain how
a windmill or an operating system or a genetic code works is one thing; toexplain how any of them originated is quite another. To claim that
deterministic chemical affinities made the origin of this system inevitable
lacks empirical foundation. Given a pool of the bases necessary to tRNA andmRNA, given all necessary sugars and phosphates and all twenty amino acids
used in proteins, would the molecules comprising the current translation
system, let alone any particular genetic code, have had to arise? Indeed,
would even a single synthetase have had to arise from a pool of all thenecessary amino acids? Again, clearly not.
As origin-of-life biochemists have taught us, monomers are “building
blocks.” And like the building blocks that masons use, molecular building
blocks can be arranged and rearranged in innumerable ways. The propertiesof stone blocks do not determine their arrangement in the construction ofbuildings. Similarly, the properties of biological building blocks do notdetermine the arrangement of monomers in functional DNA, RNA, or
proteins. Nor do they determine the correspondences between DNA bases andthe amino acids that constitute the genetic code. Instead, the chemical
properties of the building blocks of these molecules allow a vast ensemble ofpossible configurations and associations, the overwhelming majority of which
would have no biological function. Thus, functional genes and proteins are no
more inevitable, given the properties of their “building blocks,” than thepalace of Versailles was inevitable, given the properties of the bricks andstone used to construct it.
The Necessity of Freedom
As I thought more about the chemical structure of DNA and tRNA, Polanyi’s
deeper point about the expression of information requiring chemical
indeterminacy, or freedom, came into clearer focus. Polanyi had argued that if“forces of potential energy” determined the arrangement of the bases, “thecode-like character” of the molecule “would be effaced by an overwhelming
redundancy.”25 I now understood why.
Consider, for example, what would happen if the individual nucleotide
bases (A, C, G, T) in the DNA molecule did interact by chemical necessity(along the information-bearing axis of DNA). Suppose that every time
adenine (A) occurred in a growing genetic sequence, it attracted cytosine (C)to it,26 which attracted guanine (G), which attracted thymine (T), which
attracted adenine (A), and so on. If this were the case, the longitudinal axis of
DNA would be peppered with repetitive sequences of ACGT. Rather thanbeing a genetic molecule capable of virtually unlimited novelty andcharacterized by unpredictable and aperiodic sequences, DNA would containsequences awash in repetition or redundancy—much like the arrangement of
atoms in crystals.
To see why, imagine a group of captive soldiers are told they can typeletters to their families at home. The only condition is that they have to begin
with the lower case letter a, follow it with the next letter in the alphabet, b,then the next and the next, moving through the entire alphabet, circling backto a at the end and then continuing the process until they have filled in the
sheet of typing paper. They are instructed to follow the same process forfilling out the front of the envelope. Finally, if they don’t feel up to the task or
if they accidentally strike a wrong key, the prison guards will take over thetask for them and “do it properly.”This would, of course, be nothing more than a cruel joke, for the soldiers
couldn’t communicate a single bit of information with their letters or evenmail the letters to the appropriate addresses—all because the content of theletters was inevitable, strictly governed by the lawlike algorithm forced onthem by the prison guards. In the same way, the lawlike forces of chemicalnecessity produce redundancy (repetition), which reduces the capacity toconvey information and express novelty. Instead, information emerges fromwithin an environment marked by indeterminacy, by the freedom to arrange
parts in many different ways. As the MIT philosopher Robert Stalnaker puts
it, information content “requires contingency.”27
Information theory reveals a deeper reason for this. Recall that classicalinformation theory equates the reduction of uncertainty with the transmissionof information, whether specified or unspecified. It also equates improbability
and information—the more improbable an event, the more information its
occurrence conveys. In the case that a law-like physical or chemical process
determines that one kind of event will necessarily and predictably follow
another, then no uncertainty will be reduced by the occurrence of such a highprobability event. Thus, no information will be conveyed. Philosopher ofscience Fred Dretske, the author of an influential book on information theory,
explains it this way: “As p(si) [the probability of a condition or state ofaffairs] approaches 1 [i.e., certainty], the amount of information associatedwith the occurrence of si goes to 0. In the limiting case when the probabilityof a condition or state of affairs is unity [p(si) = 1], no information isassociated with, or generated by, the occurrence of si. This is merely anotherway to say that no information is generated by the occurrence of events forwhich there are no possible alternatives.”28
Dretske’s and Polanyi’s observations were decisive: to the extent thatforces of attraction among the members of a sequence determine thearrangement of the sequence, the information-carrying capacity of the system
will be diminished or effaced by redundancy.29 Bonding affinities, to theextent they exist, inhibit the production of information, because they
determine that specific outcomes will follow specific conditions with highprobability.30 Information-carrying capacity is maximized, however, when the
opposite situation occurs, namely, when chemical conditions allow many
improbable outcomes.
Polanyi appreciated this point, but also its converse. He knew that it was
precisely because the sequences of bases in DNA were not biochemicallydetermined (or predestined) that the molecule could store and transmit
information. Because any nucleotide can follow any other, a vast array ofsequences is possible, which allows DNA to encode a vast number of proteinfunctions. As he explains, “It is this physical indeterminacy of the sequencethat produces the improbability of occurrence of any particular sequence andthereby enables it to have a meaning—a meaning that has a mathematicallydeterminate information content equal to the numerical improbability of thearrangement.”31
As noted in Chapter 4, the base sequences in DNA not only possess
information-carrying capacity as measured by classical Shannon information
theory, they also store functionally specified information; they are specified aswell as complex. Clearly, however, a sequence cannot be both specified andcomplex if it is not at least complex. Therefore, self-organizational forces ofchemical necessity, which produce redundant order and preclude complexity,
preclude the generation of specified complexity (or specified information) as
well. Lawlike chemical forces do not generate complex sequences. Thus, theycannot be invoked to explain the origin of information, whether specified orotherwise.
Conclusion
At a small private conference in 1993, I had a chance to meet and talk with
Kenyon personally for the first time. I learned that he had come to the sameconclusion about his theory of “biochemical predestination” as I had. It now
seemed clear to both of us that there was a significant, in principle, objection
to the very idea that chemical attractions could produce information as
opposed to simple redundant order. Indeed, if Kenyon had found that bonding
affinities between nucleotide bases in DNA determined their sequencing, hewould have also discovered that chemists had been mistaken about DNA’s
information-bearing properties. Yet no one doubted DNA’s capacity to store
information.
But were there other such theories that could explain what Kenyon’s
could not? Self-organizational models emphasizing internal bonding affinities
had failed, but perhaps models emphasizing lawlike forces external to DNA
would succeed. And although simple algorithms like “repeat ATCG” clearlylacked the capacity to convey biological information, perhaps some far moresophisticated algorithm or some dance of forces between a large group ofmolecules could cause life (or the information it required) to self-organize. By
the early 1990s, some researchers were proposing models of just this kind.

12Thinking Outside the Bonds

There’s an oft-told tale, apocryphal but nonetheless instructive, of Christopher
Columbus dining with several men who are unimpressed by his discovery of
the New World. Columbus, not surprisingly, objects to their disdain. Certainly
the way to the New World is obvious now, but if finding the way had been soeasy, why hadn’t someone done it before? To illustrate what he’d achieved, heasks for an egg and challenges the men at the table to balance it on its head.
Each man tries and fails. Columbus then takes the egg and offers a simple
solution: he sets the egg down just hard enough to break the end, allowing it
to remain upright.
Today we have an expression to describe innovative thinking thattranscends unnecessary, but established constraints. We say that innovative
problem solvers “think outside the box.” Following Kenyon and Polanyi, I
had come to see that differences in internal bonding affinity, either between
nucleotide bases or amino acids, did not and could not solve the DNA enigma.
But if the internal affinities were inside the box, both proverbially andliterally, perhaps the failure of models relying on such forces merely signaled
the need for researchers to journey outside the box. And that’s precisely what
subsequent self-organizational researchers tried to do.
External Self-Organizational Forces: Just Add Energy?
If internal bonding affinities did not explain the information in DNA andproteins, might there be some ubiquitous external force that caused the bases
in DNA (or amino acids in proteins) to align themselves into information-richsequences? Magnetic forces cause iron filings to align themselves into orderly“lines of force” around the magnet. Gravitational forces create vortices indraining bathtubs. Perhaps some pervasive self-organizing forces external to
DNA and proteins could explain the origin of the information-richbiomolecules or other forms of biological organization.
In 1977 the Russian-born Belgian physicist Ilya Prigogine wrote a bookwith a colleague, Grégoire Nicolis, exploring this possibility. Prigogine
specialized in thermodynamics, the science of energy and heat. He became
interested in how energy flowing into a system could cause order to arisespontaneously. His work documenting this phenomenon won him the Nobel
Prize in Chemistry in 1977, the same year he published his book with Nicolis.
In their book Self-Organization in Nonequilibrium Systems, Prigogine andNicolis suggested that energy flowing into primitive living systems might
have played a role in the origin of biological organization. They characterizedliving organisms as open systems that maintain their particular form oforganization by utilizing large quantities of energy and matter from theenvironment (and by “dissipating” large quantities of energy and matter into
the environment).1 An open system is one that interacts with the environment
and whose behavior or structure is altered by that interaction. Prigogine
demonstrated that open systems driven far from equilibrium (i.e., driven far
from the normal state they would occupy in the absence of the environmental
input) often display self-ordering tendencies as they receive an input ofenergy. For example, thermal energy flowing through a heat sink will generatedistinctive convection currents or “spiral wave activity.”In their book, Prigogine and Nicolis suggested that the organizedstructures observed in living systems might have similarly “self-originated”
with the aid of an energy source. They conceded the improbability of simple
building blocks arranging themselves into highly ordered structures undernormal equilibrium conditions. Indeed, Prigogine previously had
characterized the probability of living systems arising by chance alone as“vanishingly small.”2 But now he and Nicolis suggested that under
nonequilibrium conditions, where an external source of energy is supplied,biochemical systems might arrange themselves into highly ordered patterns
and primitive biological structures.
Order Versus Information
When I first learned about Prigogine and Nicolis’s theory and the analogies
by which they justified it, it did seem plausible. But as I considered the meritsof their proposal, I discovered that it had an obvious defect, one that theprominent information theorist Hubert Yockey described to me in aninterview in 1986. Yockey pointed out that Prigogine and Nicolis invokedexternal self-organizational forces to explain the origin of order in living
systems. But, as Yockey noted, what needs explaining in biological systems is
not order (in the sense of a symmetrical or repeating pattern), but information,
the kind of specified digital information found in software, written languages,
and DNA.
Energy flowing through a system may produce highly ordered patterns.
Strong winds form swirling tornados and the “eyes” of hurricanes;
Prigogine’s thermal baths develop interesting convection currents; andchemical elements coalesce to form crystals. But Yockey insisted that thiskind of symmetric order has little to do with the specified complexity or
information in DNA, RNA, and proteins. To say otherwise conflates two
distinct types of patterns or sequences.
As was my habit, I developed a visual illustration to convey this point to
my college students. Actually, I borrowed the visual aid from the children of aprofessor friend who lived in my neighborhood. The homemade toy hischildren played with was meant to entertain, but I realized that it perfectly
illustrated Yockey’s distinction between order and specified complexity andhis critique of Prigogine’s self-organizational model.
The toy was made of two one-liter soda bottles that were sealed andfastened together at each opening by a red plastic coupling. The two bottles
together made one large hourglass shape. The device also contained aturquoise liquid (probably water with food coloring) and some silver flecks
that would sparkle as the liquid swirled around. Liquid from one bottle could
flow into the other bottle. The children liked to hold the bottles upright until
all the liquid from the top bottle flowed into the bottom one. Then they would
quickly turn the whole apparatus over and give it a sudden shake by thenarrow neck. Next they would watch as the blue liquid would organize into aswirling vortex in the top bottle and begin to drain into the bottom bottle.
After convincing the children to lend me their toy, I used it in class toillustrate how an infusion of energy could spontaneously induce order in asystem. This was an important point to establish with my students, becausesome of them had heard creationist arguments about how the second law ofthermodynamics dictates that order in nature always dissipates into disorder
over time. Prigogine had shown that, although disorder will ultimately
increase over time in a closed system such as our whole universe, order may
arise from disorder spontaneously when energy enters into smaller (than theuniverse) open systems. I used the big blue vortex maker to demonstrate thatorder can, indeed, arise from an infusion of energy (in this case, a sudden
shake and flipping of the apparatus) into an open system.
Nevertheless, I also wanted my students to understand that there was adifference between order and specified complexity and why that distinction
called into question the ultimate relevance of Prigogine’s ideas. To illustrate
this, I would ask them to focus on the individual flecks sparkling within the
swirling blue liquid. Could they see any interesting arrangements of theseflecks that performed a communication function? Did the flecks spell any
messages or encode any digital information? Obviously, the answer was no.
Students could see a highly random arrangement of sparkling flecks. They
also could see an orderly pattern in the motion of the liquid as a whole as the
swirling blue water formed the familiar funnel shape of a vortex.
Nevertheless, they could not detect any specified or functional information,

no interesting patterns forming sequences of alphabetic or digital characters.
My students had no trouble comprehending the point of my somewhat
crude illustration. Energy flowing through an open system will readily
produce order. But it does not produce much specified complexity or
information.
The astrophysicist Fred Hoyle had a similar way of making the same
point. He famously compared the problem of getting life to arise
spontaneously from its constituent parts to the problem of getting a 747
airplane to come together from a tornado swirling through a junk yard. An
undifferentiated external force is simply too blunt an instrument toaccomplish such a task. Energy might scatter parts around randomly. Energy
might sweep parts into an orderly structure such as a vortex or funnel cloud.
But energy alone will not assemble a group of parts into a highly
differentiated or functionally specified system such an airplane or cell (or into
the informational sequences necessary to build one).
Kenyon’s self-organizational model had already encountered thisproblem. He came to realize that, although internal chemical affinities might
produce highly repetitive or ordered sequences, they certainly did not produce
the information-rich sequences in DNA. Now a similar problem reemerged as
scientists considered whether lawlike external forces could have produced theinformation in DNA. Prigogine’s work showed that energy in an open system
can create patterns of symmetrical order. But it provided no evidence thatenergy alone can encode functionally specified information-rich sequences—whether biochemical or otherwise. Self-organizational processes explain wellwhat doesn’t need explaining in life.
It’s actually hard to imagine how such self-organizing forces could
generate or explain the specificity of arrangement that characterizes
information-rich living systems. In my vortex maker, an externally induced
force infused energy through the system, sweeping all the constituents of thesystem along basically the same path. In Prigogine’s convection baths, anenergy source established a pattern of motion throughout the system that
affected all the molecules in a similar way, rather than arranging them
individually and specifically to accomplish a function or convey a message.
Yet character-by-character variability and specificity of arrangement arehallmarks of functional information-rich sequences. Thus, as Yockey notes:“Attempts to relate the idea of order…with biological organization or
specificity must be regarded as a play on words that cannot stand careful
scrutiny. Informational macromolecules can code genetic messages andtherefore can carry information because the sequence of bases or residues isaffected very little, if at all, by [self-organizing] physicochemical factors.”3

The Limits of the Algorithm
As a result of these difficulties, few, if any, scientists now maintain that
Prigogine and Nicolis solved the problem of the origin of biologicalinformation. Nevertheless, some scientists continued to hope that further
research would identify a specific self-organizational process capable ofproducing biological information. For example, biophysicist Manfred Eigen
suggested in 1992 that “Our task is to find an algorithm, a natural law that
leads to the origin of information.”4This sounded good, but I began to wonder whether any lawlike process
could produce information. Laws, by definition, describe events that
repeatedly and predictably recur under the same conditions. One version of
the law of gravity states that “all unsuspended bodies will fall.” If I lift a ballabove the earth and let it go, it will fall. Every time. Repeatedly. Another law
states that “water heated to 212 degrees Fahrenheit at sea level will boil andproduce steam.” Apply heat to a pan of water. Watch and wait. Bubbles andsteam will appear. Predictably. Laws describe highly predictable and regularconjunctions of events—repetitive patterns, redundant order. They do notdescribe the kind of complexity necessary to convey information.
Here’s another way to think of it. Scientific laws often describe
predictable relationships between antecedent conditions and consequent
events. Many scientific laws take the form, “If A occurs, then B will follow,
given conditions C.” If the conditions C are present, and an event of type A
occurs, then an event of type B will follow, predictably and “of necessity.”Thus, scientific laws describe patterns in which the probability of eachsuccessive event (given the previous event) approaches one, meaning theconsequent must happen if the antecedents are present. Yet, as noted
previously, events that occur predictably and “of necessity” do not convey
information. Instead, information arises in a context of contingency.
Information mounts as improbabilities multiply. Thus, to say that scientificlaws generate complex informational patterns is essentially a contradiction interms. If a process is orderly enough to be described by a law, it does not, bydefinition, produce events complex enough to convey information.
Of course, lawlike processes might transmit information that alreadyexists in some other form, but such processes do not generate specified
information. To see why, imagine that a group of small radio-controlled
helicopters hovers in tight formation over a football stadium, the Rose Bowl
in Pasadena, California. From below, the helicopters appear to be spelling amessage: “Go USC.” At halftime with the field cleared, each helicopter
releases either a red or gold paint ball, one of the two University of Southern
California colors. The law of gravity takes over and the paint balls fall to theearth, splattering paint on the field after they hit the turf. Now on the fieldbelow, a somewhat messier but still legible message appears: “Go USC.”
Did the law of gravity, or the force described by the law, produce thisinformation? Clearly, it did not. The information that appeared on the fieldalready existed in the arrangement of the helicopters above the stadium—in
what physicists call the “initial conditions.” Neither the force of gravity nor
the law that describes it caused the information on the field to self-organize.
Instead, gravitational forces merely transmitted preexisting information fromthe helicopter formation—the initial conditions—to the field below.
Sometimes when I’ve used these illustrations I’ve been asked: “Butcouldn’t we discover a very particular configuration of initial conditions thatgenerates biological information? If we can’t hope to find a law that produces
information, isn’t it still possible to find a very particular set of initialconditions that generates information in a predictable law-like way?” But thisobjection just restates the basic self-organizational proposal in new words. It
also again begs the question of the ultimate origin of information, since “avery particular set of initial conditions” sounds precisely like an informationrich—a highly complex and specified—state. As I would later discover,
however, this wasn’t the only proposal to beg the question about the ultimate
origin of information. Instead, attempts to explain the origin of information byreference to some prior set of conditions invariably shifted—or displaced—
the problem someplace else. My first inkling of this problem came as I
reflected on perhaps the most innovative—“outside the box”—selforganizational proposal of all.
The Kauffman ModelIf neither internal bonding affinities between the constituents of DNA nor
ubiquitous external forces acting upon those constituents can account for the
specific sequence of the DNA bases, then what was left? It didn’t seem to me
that there could be much left, since “forces external” and “forces internal” tothe molecule seemed to exhaust the set of possibilities. Yet, even so, I knewthat another player was about to step onto the stage, Stuart Kauffman.
Kauffman is a brilliant scientist who trained as a physician at the
University of California, San Francisco, and later worked as professor ofbiochemistry and biophysics at the University of Pennsylvania before leaving
to help head up the Santa Fe Institute, a research institute dedicated to thestudy of complex systems. During the early 1990s, as I was beginning to
examine the claims of self-organizational theories, I learned that Kauffman
was planning to publish a treatise advancing a new self-organizational
approach. His new book promised to bring the progress made at Santa Fe tobear on the problem of the origin of life and indeed to make significant steps

toward solving that problem within a self-organizational framework. His
long-anticipated book was titled The Origins of Order: Self-Organization and
Selection in Evolution.
I remember the day Kauffman’s book finally arrived at the college where
I taught. I quickly opened the package in my office only to discover a rather
imposing seven-hundred-page behemoth of a book. After I scanned the tableof contents and read the opening chapters, it became clear that much of
Kauffman’s book provided a rather generalized discussion of themathematical properties of complex systems. His specific proposal for
explaining the origin of life occupied less than eighty pages of the book andwas mainly confined to one chapter. My curiosity got the better of me. I
decided to read this section first and return to the rest of the book later.

Figure 12.1. Dr. Kauffman’s primary work has been as a theoretical biologist
studying the origin of life and molecular organization. Courtesy of the
University of Calgary.
Kauffman had, in keeping with his reputation as an “outside the box”
thinker, made a bold and innovative proposal for explaining the origin of life.
Rather than invoking either external lawlike forces or internal bonding
affinities to explain how biological information had self-organized, his
hypothesis sought to transcend the problem altogether. He proposed a selforganizational process that could bypass the need to generate genetic
information.
Kauffman attempted to leapfrog the “specificity” (or information)
problem by proposing a means by which a self-reproducing metabolic system
might emerge directly from a set of “low-specificity” catalytic peptides andRNA molecules in a prebiotic soup, or what he called a “chemical
minestrone.”5 A metabolic system is a system of molecules that react witheach other inside a living cell in order to sustain a vital function. Some
metabolic systems break down molecules to release energy; others build
molecules to store energy (as occurs during ATP synthesis) or information (asoccurs in DNA replication or protein synthesis). In extant forms of life, thesereactions usually involve or are mediated by a number of highly specificenzyme catalysts and other proteins. As such, Kauffman’s proposal representsa kind of protein-first theory similar in some respects to Kenyon’s earlier
model.
Nevertheless, Kauffman suggests, unlike Kenyon, that the first metabolic
system might have arisen directly from a group of low-specificity
polypeptides. He proposes that once a sufficiently diverse set of catalytic
molecules had assembled (in which the different peptides performed enoughdifferent catalytic functions, albeit inefficiently), the ensemble of individual
molecules spontaneously underwent a kind of phase transition (akin to
crystallization) resulting in a self-reproducing metabolic system. Kauffman
envisions, as the historian of biology Iris Fry puts it, “a set of catalytic
polymers in which no single molecule reproduces itself but the system as awhole does.”6 In this way, Kauffman argues that metabolism (and the proteinsnecessary to it) could have arisen directly without genetic information
encoded in DNA.7
Kauffman’s model was clearly innovative and arguably moresophisticated than many previous self-organizational models. Unlike previous
models, Kauffman’s claims that an ensemble of relatively short and “lowspecificity” catalytic peptides and RNA molecules would together be enoughto establish a metabolic system. He defends the biochemical plausibility of his
scenario on the grounds that some proteins can perform enzymatic functions
with low specificity and complexity. To support his claim, he cites a class ofproteins known as proteases (including one in particular, called trypsin) thatcleave peptide bonds at single amino-acid sites.8 But is he right?
As I thought more about Kauffman’s proposal and researched theproperties of proteases, I became convinced that his proposal did not solve or
successfully bypass the problem of the origin of biological information.
Kauffman himself acknowledges that, as yet, there is no experimental
evidence showing that such autocatalysis could occur. But, beyond that, I
realized that Kauffman had either presupposed the existence of unexplained
sequence specificity or transferred the need for specified information out ofview. In fact, Kauffman’s model has at least three significant informationrelated problems.
First, it does not follow, nor is it the case biochemically, that just becausesome enzymes might function with low specificity, that all the catalytic
peptides (or enzymes) needed to establish a self-reproducing metabolic cycle
could function with similarly low levels of specificity and complexity.
Instead, modern biochemistry shows that most and usually all of themolecules in a closed interdependent metabolic system of the type that

Kauffman envisions require high-complexity and-specificity proteins.
Enzymatic catalysis (which his scenario would surely require) needs
molecules long enough to form tertiary structures. Tertiary structures are thethree-dimensional shapes of proteins, which provide the spatial positioning of
critical amino-acid residues to convey particular or specialized functions.9
How long a protein chain needs to be to form one of those shapes depends on
its complexity. For the very simplest shapes, something like forty or fifty
amino acids are needed. More complicated shapes may require several
hundred amino acids. Further, these long polymers require very specific threedimensional geometries (which in turn derive from sequence-specificarrangements of monomers) in order to catalyze necessary reactions. How dothese molecules acquire their specificity of sequencing? Kauffman does notaddress this question, because his model incorrectly suggests that he does notneed to do so.
Second, I discovered that even the allegedly low-specificity molecules(the proteases) that Kauffman cites to illustrate the plausibility of his scenario
are actually very complex and highly specific in their sequencing. I also
discovered that Kauffman confuses the specificity and complexity of the partsof the polypeptides upon which the proteases act with the specificity andcomplexity of the proteins (the proteases) that do the enzymatic acting.
Though trypsin, for example, acts upon—cleaves—peptide bonds at a
relatively simple target (the carboxyl end of two separate amino acids,
arginine and lysine), trypsin itself is a highly complex and specificallysequenced molecule. Indeed, trypsin is a non-repeating 247-amino-acidprotein that possesses significant sequence specificity as a condition offunction.10
Further, trypsin has to manifest significant three-dimensional (geometric)
specificity in order to recognize the specific amino acids arginine and lysine,at which sites it cleaves peptide bonds. By equivocating in his discussion of
specificity, Kauffman obscures from view the considerable specificity andcomplexity requirements of the proteases he cites to justify his claim that lowspecificity catalytic peptides will suffice to establish a metabolic cycle. Thus,
Kauffman’s own illustration, properly understood (i.e., without equivocating
about the relevant locus of specificity), shows that for his scenario to havebiochemical plausibility, it must presuppose the existence of many highcomplexity and-specificity polypeptides and polynucleotides. Where does thisinformation in these molecules come from? Kauffman again does not say.
Third, Kauffman acknowledges that for autocatalysis to occur, themolecules in the chemical minestrone must be held in a very specific spatialtemporal relationship to one another.11 In other words, for the direct
autocatalysis of integrated metabolic complexity to occur, a system ofcatalytic peptide molecules must first achieve a very specific molecularconfiguration (or what chemists call a “low-configurational entropy state”).12
This requirement is equivalent to saying that the system must start with alarge amount of specified information or specified complexity. By Shannon’s
theory of information, information is conveyed every time one possibility is
actualized and others are excluded. By admitting that the autocatalysis of ametabolic system requires a specific arrangement of polypeptides (only one ora few of the possible molecular arrangements, rather than any one of themany possibilities) Kauffman tacitly concedes that such an arrangement has ahigh-information content. Thus, to explain the origin of specified biologicalcomplexity at the systems level, Kauffman has to presuppose a highly specificarrangement of those molecules at the molecular level as well as the existenceof many highly specific and complex protein and RNA molecules (see above).
In short, Kauffman merely transfers the information problem from themolecules into the soup.
In addition to these problems, Kauffman’s model encounters some of thesame problems that Kenyon’s protein-first model and other metabolism-firstmodels encounter. It does not explain (a) how the proteins in various
metabolic pathways came into association with DNA and RNA or any othermolecular replicator or (b) how the information in the metabolic system ofproteins was transferred from the proteins into the DNA or RNA. And it gives
no account of (c) how the sequence specificity of functional polypeptides
arose (given that the bonding affinities that exist among amino acids don’t
correlate to actual amino-acid sequences in known proteins).
Robert Shapiro, a leading chemist at New York University, has recently
proposed that origin-of-life researchers begin to investigate metabolism-firstmodels of the kind that Kauffman proposed. Shapiro argues that these models
have several advantages that other popular origin-of-life scenarios(particularly RNA-first models, see Chapter 14) don’t.13 Though Shapiro
favors these metabolism-first approaches, he acknowledges that researchers
have not yet identified what he calls a “driver reaction” that can convert small
molecules into products that increase or “mobilize” the organization of thesystem as a whole. He also notes that researchers on metabolism-first models“have not yet demonstrated the operation of a complete [metabolic] cycle or
its ability to sustain itself and undergo further evolution.”14 In short, theseapproaches remain speculative and do not yet offer a way to solve thefundamental problem of the origin of biologically relevant organization (orinformation).
In any case, I concluded that Kauffman’s self-organization model—to theextent it had relevance to the behavior of actual molecules—presupposes ortransfers, rather than explains, the ultimate origin of the specified information

necessary to the origin of a self-reproducing metabolic cycle. I wasn’t the
only one to find Kauffman’s self-organizational model insufficient. Other
scientists and origin-of-life researchers made similar criticisms.15 Thoughmany origin-of-life researchers have expressed their admiration for
Kauffman’s innovative new approach, few, if any, think that his model
actually solves the problem of the origin of information or the origin of life.
Perhaps, for this reason, after 1993 Kauffman proposed some new selforganizational models for the origin of biological organization. His
subsequent proposals lacked the biological specificity of his bold, if ill-fated,
original proposal. Nevertheless, Kauffman’s later models did illustrate justhow difficult it is to explain the origin of information without presupposing
other preexisting sources of information.
Buttons and Strings
In 1995 Kauffman published another book, At Home in the Universe, inwhich he attempted to illustrate how self-organizational processes might work
using various mechanical or electrical systems, some of which could be
simulated in a computer environment.16 In one, he conceives a system of
buttons connected by strings. The buttons represent novel genes or geneproducts, and the strings represent the lawlike forces of interaction between
the gene products, namely, the proteins. Kauffman suggests that when thecomplexity of the system (as represented by the number of buttons andstrings) reaches a critical threshold, new modes of organization can arise in
the system “for free”—that is, without intelligent guidance—after the mannerof a phase transition in chemistry, such as water turning to ice or the
emergence of superconductivity in some metals when cooled below a certain
temperature.
Another Kauffman model involves a system of interconnected lights.
Each light can flash in a variety of states—on, off, twinkling, and so forth.
Since there is more than one possible state for each light and many lights,
there are many possible states the system can adopt. Further, in his system,
rules determine how past states will influence future states. Kauffman asserts
that, as a result of these rules, the system will, if properly tuned, eventually
produce a kind of order in which a few basic patterns of light activity recurwith greater than random frequency. Since these patterns represent a small
portion of the total number of possible states for the system, Kauffman
suggests that self-organizational laws might similarly produce a set of highly
improbable biological outcomes within a much larger space of possibilities.
But do these simulations accurately model the origin of biologicalinformation? It’s hard to think so. Kauffman’s model systems are notconstrained by functional considerations and, thus, are not analogous tobiological systems. A system of interconnected lights governed by
preprogrammed rules may well settle into a small number of patterns within amuch larger space of possibilities. But since these patterns need not meet any
functional requirements, they fail at a fundamental level to model biologicalorganisms. A system of lights flashing “Warning: Mudslide Ahead” wouldmodel a biologically relevant self-organizational process, at least if such a
message arose without agents previously programming the system withequivalent amounts of functional information. Kauffman’s arrangements offlashing lights are not of this sort. They serve no function, and certainly nofunction comparable to the information-rich molecules found in the biologicalrealm.
Kauffman’s model systems differ from biological information in anotherstriking way. The series of information-bearing symbols we find in theprotein-coding regions of DNA, in sophisticated software programs, or in thesentences on this page are aperiodic. The sequences of characters do notrepeat in a rigid or monotonous way. Kauffman’s model, in contrast, ischaracterized by large amounts of symmetrical order or internal redundancyinterspersed with aperiodic sequences (mere complexity) lacking function.17
Getting a law-governed system to generate repetitive patterns of flashinglights, even with a certain amount of variation, is clearly interesting, but notbiologically relevant. Since Kauffman’s models do not produce functional
structures marked by specified aperiodic symbol sequences such as we find inDNA, they do not serve as promising models for explaining the origin of
biological information.
But there is another fundamental problem with Kauffman’s model
systems, one he tacitly acknowledges. To the extent that Kauffman’s systems
do succeed in producing interesting nonrandom patterns, they do so only
because of an unexplained intervention of information. For example,
Kauffman notes that if his system of flashing lights is properly “tuned,”18
then it will shift from a chaotic regime to an orderly regime that will produce
the outcomes or patterns he regards as analogous to processes or structures
within living systems. By “tuning,” Kauffman means the careful setting of aparticular “bias parameter” to make his system shift from a chaotic regime
into one in which order is produced. In other words, the tuning of thisparameter ensures that certain kinds of outcomes are actualized and others
precluded. Such an act constitutes nothing less than an infusion of
information. When someone tunes a radio dial or a musical instrument, he or
she selects a certain frequency and excludes many others. Yet Shannon
defined an informative intervention as precisely electing one option andexcluding others.

In his system of flashing lights, Kauffman briefly mentions that two of his
collaborators—physicists Bernard Derrida and Gerard Weisbuch—were
responsible for the “tuning” that produced the patterns he thinks analogous toorder in living systems. Nevertheless, he does not think that any such agency
played a role in the origin of life. Thus, even assuming for the sake ofargument that his system of flashing lights manifests features analogous tothose in living systems, his model still falls prey to what Dembski calls the“displacement problem”—the problem of explaining the origin of one(putatively) information-rich system only by introducing another unexplained
source of information.
Conclusion: The Displacement Problem
As I examined Kauffman’s model, it occurred to me that I was beginning tosee a pattern. Self-organizational models for the origin of biologicalorganization were becoming increasingly abstract and disconnected from
biological reality. Model systems such as Prigogine’s or Kauffman’s did noteven claim to identify actual chemical processes that led in a life-friendly
direction. Instead, these models claimed to describe processes that produced
phenomena with some limited similarity to the organization found in living
systems. Yet upon closer inspection these allegedly analogous phenomena
actually lacked important similarities to life, in particular, the presence ofspecified complexity, or information.
But beyond that, I realized that self-organizational models either failed tosolve the problem of the origin of specified information, or they “solved” theproblem only at the expense of introducing other unexplained sources of
information. Kauffman’s models provided only the best illustration of thislatter “displacement problem.” In addition, the earlier models such as
Kenyon’s or Prigogine’s, relying as they did on processes that produced orderrather than complexity, each fell prey to both empirical and conceptual
difficulties. They not only failed to explain the origin of information; they did
so in a highly instructive way—one that helped to clarify our understandingof the nature of information and why it stands conceptually distinct fromredundant order and the lawful processes that produce such order.
Thus, despite the cachet associated with self-organizational theories as the“new wave” of thinking in evolutionary biology, I came to reject them ascomplete nonstarters, as theories that were unlikely ever to succeed regardlessof the outcome of future empirical studies. In my view, these models either
begged the question or invoked a logical contradiction. Proposals that merelytransfer the information problem elsewhere necessarily fail because they
assume the existence of the very entity—specified information—they aretrying to explain. And new laws will never explain the origin of information,
because the processes that laws describe necessarily lack the complexity that
informative sequences require. To say otherwise betrays confusion about thenature of scientific laws, the nature of information, or both.
As I reflected on the failure of these models, my interest in the design
hypothesis increased. But the reason for this was not just that selforganizational scenarios had failed. Instead, it was that self-organizational
theories failed in a way that exposed the need for an intelligent cause to
explain the relevant phenomena. Remember my magnetic chalkboard
demonstration? I had used that demonstration to show that chemical forces of
attraction don’t explain the specific arrangements of bases in DNA any morethan magnetic forces of attraction explained the arrangement of letters on myletter board. But what did explain the arrangement of magnetic letters on thatboard? Obviously, an intelligent agent. Might the arrangement of bases in
DNA have required such a cause as well?
Recall Kauffman’s model systems. In each case, they explained the originof information by reference to an unexplained source of information. These
scenarios too lacked just what the design hypothesis provided: a cause known
to be capable of generating information in the first place. In one case,Kauffman presupposes that his system will work only once it had been“tuned.” But how? Was intelligence necessary to do what self-organizational
processes alone could not?
A similar thought had occurred to me earlier when reflecting on chanceelimination. Many events that we would not credit to chance—in particular,
highly improbable events that matched independent patterns—were actually
best explained by intelligent design. The improbable match between the twocollege papers that led my colleagues and me to exclude the chance
hypothesis also led us to conclude plagiarism, a kind of design.
Even Christian de Duve, in explaining why the origin of life could nothave occurred by chance, acknowledged (if inadvertently) that the kind of
events that lead us to reject chance also suggest design. Recall that de Duvepointed out that a “string of improbable events” such as someone winning thelottery twice in a row (a kind of pattern match) “do not happen naturally.”19
Of course, de Duve went on to state that the failure of the chance hypothesis
implied, for him, that life must be “an obligatory manifestation of matter”—
one that had self-organized when the correct conditions arose.20
Having examined the leading self-organizational theories in detail, I now
doubted this. I also later learned from de Duve himself that he felt compelled
to elect a self-organizational model, because he was unwilling to consider
design as an alternative to chance. He thought invoking design violated therules of science. As he explains: “Cells are so obviously programmed todevelop according to certain lines…that the word design almost unavoidably
comes to mind,…[but] life is increasingly explained strictly in terms of thelaws of physics and chemistry. Its origin must be accounted for in similarterms.”21 As I explain later (see Chapters 18 and 19), I saw no reason toaccept this prohibition against considering the design hypothesis. To me, it
seemed like an unnecessary restriction on rational thought. So the failure ofchance and self-organizational models—as well as the way these models
failed—only made me more open to intelligent design as a possible
hypothesis.
Indeed, the design hypothesis now seemed more plausible to me thanwhen I first encountered it and certainly more plausible than it had before I
had investigated the two most prominent alternative categories of explanation,
chance and necessity. But I knew that there was another category of
explanation that I needed to investigate more fully, one that combined chanceand necessity. Some of the most creative proposals for explaining the originof biological information relied on the interplay of lawlike processes ofnecessity with the randomizing effects of “chance” variations. So I needed to
examine this class of theories as well. The next two chapters describe what I
discovered about them.

13
Chance and Necessity, or the Cat in the Hat Comes BackIn the Dr. Seuss children’s book The Cat in the Hat Comes Back, a tall bipedal
cat makes a return visit to the home of two children, Sally and her unnamed
little brother. On the Cat’s first visit, he made a colossal mess, so the children
are less than delighted to see him a second time. As before, the Cat makes
himself right at home, this time by eating a slice of pink cake in the bathtub
and leaving behind a pink ring. Ever helpful, the Cat immediately begins
cleaning up his own mess. Unfortunately, every attempt he makes to remove
the pink ring from the bathtub only results in spreading parts of the pink ringto various other household objects. Next the Cat pulls a series of smaller catsfrom his enormous hat to lend a hand, but none of them succeeds in solving
the problem. Instead, now the pink stuff is everywhere. Finally the last andlittlest cat produces a mysterious device called Voom, which suddenly anddramatically cleans up every shred of pink.
What does this story have to do with the origin of biological information?
As I examined more and more theories about the origin of life, I discovered apattern, one curiously reminiscent of Dr. Seuss’s Cat and his indissoluble pinkicing. Origin-of-life researchers have been looking for their “Voom” for overfifty years, a process that can, once and for all, clean up the problem of
explaining the origin of information. I discovered that every attempt to
explain the origin of biological information either failed or transferred the
problem elsewhere—either by presupposing some other unexplained sourcesof information or by overlooking the indispensable role of an intelligence inthe generation of the information in question. I first noticed this pattern as I
was examining self-organizational theories. As I began to examine theories
that invoked both chance and necessity in combination, I noticed this same
pattern emerging in spades.
The Classical Oparin Model: Chance and Necessity
The strategy of combining chance with necessity to explain the origin of life
began with Aleksandr Oparin, who modeled his approach on Darwin’s. Just as
Darwin sought to explain the origin of new biological forms by reference tothe interplay of random variations and the lawlike process of natural

selection, Oparin sought to explain the origin of the first life by combining
chance and various types of necessity, including both deterministic chemical
reactions and a kind of prebiotic natural selection.
In his original model, Oparin asserted that a series of chemical reactions
between smaller molecules produced the first amino acids and other building
blocks (see Chapter 2).1 These reactions took place almost exclusively bychemical necessity.2 Next Oparin envisioned a series of chance interactions
between chemical building blocks, such as amino acids and sugars, eventually
resulting in the first proteins and other complex polymers, such ascarbohydrates, that are needed for establishing metabolic processes.3 Afterthese molecules arose, and after some were enclosed in coacervate bodies,
competition for nutrients ensued. Then “necessity” asserted itself again. The
coacervate protocells containing the most complex metabolic processes andmeans of absorbing nutrients would have grown fastest and multiplied mostoften. As the availability of nutrient substances diminished, the more
numerous, highly organized protocells would have overwhelmed simpler
structures in the “struggle for existence.”4 Oparin envisioned natural selection
acting on random changes in the metabolic processes of coacervate bodies.
More metabolically complex and efficient coacervates would generally
outcompete the less complex ones. In this way, the interplay between chancevariation and the imperatives of competition (necessity) gradually produced
coacervates containing more complex metabolic processes and, eventually, aliving cell with the features we see today.
Oparin Revised: The Natural Selection of Unspecified Polymers
The displacement problem was not evident in Oparin’s original scenario. But
Oparin’s original theory did not explain the origin of the information in DNA.
When Oparin published his theory in the 1920s and 1930s, he did not yet
know about the information encoded in DNA. He therefore, understandably,
made no attempt to explain its origin.
By the late 1950s, developments in molecular biology began to cast doubt
on Oparin’s original scenario. Not only did his model give no explanation for
the emerging DNA enigma; his scenario relied heavily on chance to explain
the initial formation of the proteins that made cellular metabolism possible.
The discovery of the extreme complexity and specificity of protein molecules
undermined the plausibility of this proposal for the reasons discussed in
Chapters 9 and 10. Protein function depends upon hundreds of specificallysequenced amino acids, and the odds of a single functional protein arising by
chance alone are prohibitively low, given the probabilistic resources of theentire universe.

As the complexity of DNA and proteins became apparent, Oparin
published a revised version of his theory in 1968 that envisioned a role fornatural selection earlier in the process of abiogenesis. The new version of his
theory claimed that natural selection acted on unspecified polymers as they
formed and changed within his coacervate protocells.5 Instead of natural
selection acting on fully functional proteins in order to maximize theeffectiveness of primitive metabolic processes at work within the protocells,
Oparin proposed that natural selection might work on less than fully
functional polypeptides, which would gradually cause them to increase their
specificity and function, eventually making metabolism possible. He
envisioned natural selection acting on “primitive proteins” rather than on
primitive metabolic processes in which fully functional proteins had alreadyarisen. By claiming that the first polymers need not have been highly
sequence-specific to be preserved by natural selection, Oparin attempted tocircumvent, at least partially, the information problem.
I say “partially,” because Oparin’s revised scenario also attempted to
explain the origin of genetic information in DNA. It proposed that both
unspecified polypeptides and unspecified polynucleotides arose by chance
and were later enclosed within coacervates. Natural selection then began toact on both types of molecules by favoring the coacervates that graduallydeveloped functionally specified proteins and genes over those that did not.6
As the specificity of the DNA and protein molecules within the coacervates
increased, they came into association with each other, gradually producing
metabolic processes and mechanisms of information storage and transmission.
Thus Oparin sought to take into account the importance and apparent primacyof the genetic material to living systems. In doing so, he also sought tocounter criticisms from advocates of a competing theory of abiogenesis
known as the “gene theory.”7
Gene theorists such as Hermann Muller believed that life had evolved
first in the form of a single (“living”) genetic molecule as the result of afortuitous combination of chemicals. To such theorists, the primacy of DNA
in information storage and its importance in initiating the process of protein
synthesis suggested an inadequacy in Oparin’s original theory. Muller
attacked Oparin’s view that life evolved first in a metabolizing
multimolecular system (i.e., in coacervates) “on the grounds that whilechanges in genes lead to changes in metabolism, the reverse was known not tobe the case.”8 Oparin’s silence about a specifically genetic molecule in his
1936 work did leave his scenario vulnerable to the criticism that it wasinadequate to explain the DNA enigma. But during the 1960s, Oparin adapted
his theory to accommodate new information about the function and structureof DNA and the mechanisms of self-replication and protein synthesis.9 Thisrevision enabled it to weather this initial challenge and to remain in currency
for another decade or so.10
Oparin’s revised theory nevertheless encountered almost immediate
criticism. First, many scientists recognized that Oparin’s concept of prebiotic
natural selection begged the question. Natural selection occurs only inorganisms capable of reproducing or replicating themselves. Yet, in all extant
cells, self-replication depends on functional and, therefore, sequence-specific
DNA and protein molecules. As theoretical biologist Howard Pattee explains,“There is no evidence that hereditary evolution [natural selection] occurs
except in cells which already have…the DNA, the replicating and translating
enzymes, and all the control systems and structures necessary to reproduce
themselves.”11 But this fact of molecular biology posed an obvious difficulty
for Oparin’s theory of prebiotic natural selection. In order to explain theorigin of specified information in DNA, Oparin invoked a process that
depends upon preexisting sequence-specific (i.e., information-rich) DNA
molecules. Yet, the origin of these molecules is precisely what his theory
needed to explain. As Christian de Duve explains, theories of prebiotic natural
selection necessarily fail because they “need information which implies they
have to presuppose what is to be explained in the first place.”12
I tell a story in lectures to illustrate how the prebiotic natural selection
hypothesis begs the question. My story is about a man who has fallen into a
thirty-foot pit. To climb out of the pit, the man knows he needs a ladder.
Unmoved by the severity of his difficulty, the man goes home and gets aladder out of his garage, walks back to the pit, climbs back in, sets up theladder, and climbs out of the hole. The ladder from his garage was just the
ticket, but my story begs an obvious question: How did the man get out of thepit in the first place in order to fetch the ladder? In the same way, the conceptof prebiotic natural selection begs the question of how nature generated thesequence-specific information-rich DNA and proteins that are needed to makeself-replication, and thus natural selection, possible. Indeed, for this reason,
Theodosius Dobzhansky, one of the leading evolutionary biologists of thetwentieth century, insisted in 1968 that “Prebiological natural selection is acontradiction in terms.”13
In fairness, Oparin did attempt to circumvent this problem. He proposedthat prebiotic natural selection initially would act on unspecified strings ofnucleotides and amino acids. But this created another problem for his
scenario. Researchers pointed out that any system of molecules for copying
information would be subject to a phenomenon known as “error catastrophe”
unless those molecules are specified enough to ensure an error-freetransmission of information. An error catastrophe occurs when small errors—
deviations from functionally necessary sequences—are amplified insuccessive replications.14 Since the evidence of molecular biology shows that
unspecified polypeptides will not replicate genetic information accurately,
Oparin’s proposed system of initially unspecified polymers would have beenhighly vulnerable to such an error catastrophe.
Thus, the need to explain the origin of specified information created an
intractable dilemma for Oparin. If, on the one hand, Oparin invoked natural
selection early in the process of chemical evolution (i.e., before functional
specificity in amino acids or nucleotide strings had arisen), accurate
replication would have been impossible. But in the absence of such
replication, differential reproduction cannot proceed and the concept ofnatural selection is incoherent.
On the one hand, if Oparin invoked natural selection late in his scenario,
he would need to rely on chance alone to produce the sequence-specificmolecules necessary for accurate self-replication. But even by the late 1960s,
many scientists regarded that as implausible given the complexity andspecificity of the molecules in question.
Oparin’s dilemma was made all the more acute by the logic of natural
selection itself.15 As proposed by Darwin, the process of natural selection
favors or “selects” functionally advantageous variations. Thus, it can “select”
only what random variations first produce. Yet it is extremely unlikely that
random molecular interactions would produce the information present in evena single functioning protein or DNA molecule of modest length (see Chapters
9 and 10). And the improbability of biological information arising by chance
undermines the plausibility of prebiotic natural selection because it implies anexceedingly high (i.e., improbable) initial threshold of selectable function.
Given the probabilistic resources of the whole universe, it is extremely
unlikely that even one functional protein or DNA molecule—to say nothing of
the suite of such molecules necessary to establish natural selection—would
arise by chance. Yet the hypothesis of prebiotic natural selection presupposesthat a series of such improbable events occurred before natural selection
played any role at all.
The work of John von Neumann, one of the leading mathematicians of thetwentieth century, made this dilemma more acute. In 1966, von Neumann
showed that any system capable of self-replication would require subsystems
that were functionally equivalent to the information storage, replicating, andprocessing systems found in extant cells.16 His calculations established an
extremely high threshold of minimal biological function, a conclusion thatwas confirmed by later experimental work.17 On the basis of minimal
complexity and related considerations, several scientists during the late 1960s(von Neumann, physicist Eugene Wigner, biophysicist Harold Morowitz)
made calculations showing that random fluctuations of molecules wereextremely unlikely to produce the minimal complexity needed for a primitive
replication system.18 Indeed, as we saw in Chapters 9 and 10, theimprobability of developing a replication system vastly exceeds the
improbability of developing the protein or DNA components of such asystem.
As a result, by the late 1960s many scientists came to regard the
hypothesis of prebiotic natural selection as indistinguishable from the purechance hypothesis, since random molecular interactions were still needed togenerate the initial complement of biological information that would makenatural selection possible. Prebiotic natural selection could add nothing to theprocess of information generation until after vast amounts of functionallyspecified information had first arisen by chance. Oparin’s idea of prebiotic
natural selection succeeded only in pushing the pink ring back into the murky
realm of small probabilities.
DNA First
As I investigated various other models combining chance and necessity, I
found some highly creative proposals. One of the most creative was devisedin 1964 by Henry Quastler, an early pioneer in the application of information
theory to molecular biology. Quastler proposed a DNA-first model for theorigin of life in which chance processes create a system of unspecified
polynucleotides that can self-replicate via complementary base pairing (bychemical necessity). Like Oparin, Quastler thought these initialpolynucleotides would have arisen without the specificity of sequencing
necessary to build specific proteins.19 Quastler considered this an advantage
for his model. He acknowledged that it was extremely improbable that chancealone would produce a specific sequence of nucleotide bases long enough tofunction as a gene. Nevertheless, he knew that if there were no constraints on
how DNA was sequenced—if any sequence would do—then chance alone
might do the job.
But then how did biological specificity and functional information arise?
Quastler thought that specificity in these molecules arose later, after his
system of polynucleotides had—by chance—come to associate with a set ofproteins and ribosomes capable of producing proteins from the particular
nucleotide sequences that happened to have arisen. At some point, thepreviously unspecified polynucleotide sequences acquired specificity andfunctional significance by their association with a system of other molecules
for producing proteins. Thus, Quastler characterized the origin of informationin polynucleotides as an “accidental choice remembered.”20
Quastler developed an illustration to convey what he had in mind using a

combination lock. He asked his readers to imagine someone blindly choosing
a combination—any combination—at random. Quastler suggested that after
this combination was selected, someone could set the tumblers to match it. As
a result, the combination would thereafter open the lock. Quastler used thisillustration to show how an initially unspecified sequence that had arisen bychance could later acquire functional specificity.
Quastler’s scenario possessed one overriding difficulty, however. It did
not account for the origin of the specificity of the molecular system that
conferred functional significance and specificity on the initial sequence of
nucleotides. In Quastler’s combination lock example, a conscious agent chosethe tumbler settings that made the initial combination functionally significant.
Further, engineers designed the lock that made it possible to specify the
tumbler settings. Yet Quastler expressly precluded conscious design as apossibility for explaining the origin of life.21 Instead, he seemed to suggestthat the origin of the set of functionally specific proteins (and the translation
system) necessary to create a “symbiotic association” between
polynucleotides and proteins would arise by chance.22 He offered some roughcalculations to show that a multimolecular system could have originated bychance in the prebiotic soup. But these calculations, which were performed in
1964, are no longer credible. They vastly underestimated the improbability ofgenerating a single protein by chance alone, to say nothing of the whole geneexpression system.23
Moreover, Quastler’s approach to the problem of the origin of biologicalinformation provided another striking example of the displacement problem.
Quastler “solved” the problem of the origin of complex specificity in nucleic
acids only by transferring the problem to an equally complex and specified
system of proteins, RNA molecules, and ribosomes. In his scenario, any
polynucleotide sequence would suffice at first, but the proteins and ribosomal
material that made up the translation system that arose later would have toexhibit an extreme specificity relative to the initial polynucleotide sequence.
Each of these molecules would also have to possess a specific shape andsequence in order to play the functional role that Quastler expected it to playin converting unspecified sequences of nucleotide bases into proteins. The
proteins would have to possess the correct sequences of amino acids in order
to be able to unwind and copy genetic information; the ribosomal proteins andRNAs would need to be sequenced precisely in order to fold into subunits that
fit together to form a functional ribosome; the transfer RNAs would have tomediate specific associations in order to convert the random sequences ofbases on the polynucleotides into specific amino-acid sequences; and thesequences of amino acids thus produced would have to be arranged precisely
in order to fold into stable three-dimensional structures. Like other scenarios Ihad encountered, his attempt to bypass the sequence-specificity problem
merely shifted it elsewhere.
Hypercycles
Another popular model for explaining the origin of life—one that combines
chance and chemical necessity24—was proposed in the late 1970s by Manfred
Eigen, a German biophysicist who won the Nobel Prize in Chemistry in 1967.
Eigen proposed a system called a hypercycle to explain how new biologicalinformation and structure might have accumulated over time as life
developed.25 A hypercycle is a hypothetical self-reproducing system made ofmany enzymes and RNA molecules. In the hypercycle, enzymes and RNA
molecules react with each other chemically to make structural improvementsin the system as a whole. The system develops as one group of enzymes
increases the replication rate of a sequence-specific RNA molecule, which inturn increases the rate of production of other enzymes, which in turn increases
the replication rate of a different RNA molecule, and so on. Eventually, agroup of enzymes increases the replication rate of the original RNA in thereaction sequence, forming a cycle that repeats indefinitely (see Fig. 13.1).
Theoretically, the RNA and protein molecules of a hypercycle can beorganized in an interdependent way to give the hypercycle, as a whole, a kindof structural stability. Because Eigen proposed this as a mechanism forincreasing the molecular information and structure of a developing protocell,some have claimed that he solved—at least in theory—the information
problem associated with life’s origin.
Critics of his model, such as evolutionary biologist John Maynard Smith,physicist Freeman Dyson, and chemist Robert Shapiro, have contested thisassessment, showing that Eigen’s hypothetical cycles are more likely to loseor degrade genetic information over time.26 They point out that hypercycles,
as conceived by Eigen, lack an error-free mechanism of self-replication. As aresult, his proposed mechanism would succumb to various “error
catastrophes” that would diminish, rather than increase, the specified
information content of the system over time.
In any case, hypercycles have a more obvious limitation. In formulating
the idea, Eigen presupposed a large initial complement of information in theform of sequence-specific RNA molecules and some forty functional proteins.
Thus, his model does not solve, even hypothetically, the problem of theultimate origin of biological information. At best, it models how information
might have developed from a preexisting source, though even that is doubtful
for the reasons his critics have described. Eigen himself has apparently
acknowledged that hyper-cycles presuppose, but do not explain, the ultimate

origin of biological information. He notes that he devised hypercycles tomodel how information and structure might develop, rather than originate.27

Figure 13.1. A schematic representation of Manfred Eigen’s concept of a
hypercycle. The E’s represent enzymes capable of replicating RNA
molecules, the R’s in the circles represent RNA molecules. Notice that
hypercycles require an initial complement of both sequence-specific enzymes
and information-rich RNA molecules.
“Life” in a Computer: Genetic Algorithms
As it became more difficult to envision how life arose by chance andnecessity from the molecular constituents of actual living cells, evolutionary
biologists sought to simulate the production of biological information using
computer programs. The first such attempts occurred during the late 1980s. At
that time, Richard Dawkins and Bernd-Olaf Küppers attempted to simulate
how natural selection acting on random mutations could explain the origin of
biological information. Each tried to do this by developing software programs
that simulated the production of genetic information.28 Both accepted thefutility of naked appeals to chance and invoked what Küppers calls a“Darwinian optimization principle.” Both used computers to demonstrate theefficacy of natural selection. Thus, both combined chance and necessity in aclassically Darwinian way.
In order to show how chance and selection can produce information,
Dawkins and Küppers first provided the computer program with a target
sequence to represent a desired functional polymer. In their programs, thetarget sequences were English phrases, such as the line from Shakespeare’s
play Hamlet, “Methinks it is like a weasel.” Next the computer program
generated another alphabetic sequence the same length as the target. Then the
program generated a crop of variant sequences at random. After that, thecomputer selected the sequences from among the crop that matched the target
sequence most closely. The computer then preserved those sequences andamplified the production of them, eliminated the others (to simulatedifferential reproduction), and repeated the process. As Küppers puts it,“Every mutant sequence that agrees one bit better with the meaningful orreference sequence…will be allowed to reproduce more rapidly.”29 After amere thirty-five generations, his computer succeeded in spelling his target
sequence, “Natural selection.” Dawkins’s simulation took only a bit longer. Ina mere forty-three generations, his program produced: “Methinks it is like aweasel.”Such simulations are impressive from a distance, but they conceal an
obvious flaw: molecules in situ do not have a target sequence “in mind.” Inbiology, where differential survival depends upon maintaining function,
selection cannot occur until functional sequences arise. Nor will sequences
confer any selective advantage on a cell and thus differentially reproduce until
they combine in a functionally advantageous arrangement. Nothing in nature(biology or chemistry) corresponds to the role that the computer plays inselecting functionally nonadvantageous sequences that happen to agree “onebit better” than others with a target sequence. The simulation doesn’t evenmake sense on its own terms. The sequence “normal election” may agree
more with “natural selection” than does the sequence “mistress defection,”but neither of the two yields any advantage in communication over the otherin trying to communicate something about natural selection. If that is the goal,
both are equally ineffectual.
More to the point, a completely nonfunctional polypeptide would confer
no selective advantage on a hypothetical protocell, even if its sequence
happened to “agree one bit better” with an unrealized target protein than someother nonfunctional polypeptide. Such a molecule would not be preserved but
eliminated, stopping the evolutionary process in its tracks. Yet both Küppers’sand Dawkins’s published results of their simulations show the early
generations of variant phrases awash in nonfunctional gibberish.30 In
Dawkins’s simulation, not a single functional English word appears until after
the tenth iteration (unlike the more generous example above that starts withthe actual, albeit incorrect, words). Clearly it is impossible to distinguishsequences based on considerations of comparative functional advantage when
the sequences in question have no function whatsoever. Such determinations
can be made only if considerations of proximity to possible future function
are allowed, but that requires foresight, the very thing natural selection lacks.
As philosopher and mathematician David Berlinski has argued, genetic
algorithms need something akin to a “forward-looking memory” to succeed.
Yet such foresighted selection has no analogue in nature.31 In biology, where
differential survival depends upon maintaining function, selection cannot
occur before new functional sequences arise. Natural selection lacks foresight.
A computer programmed by a human being can make selections based uponrelative proximity to distant targets or goals, but to imply that molecules cando so illicitly personifies nature. If computer simulations demonstrate
anything, they subtly demonstrate the need for an intelligent agent to electsome options and exclude others—that is, to create information.
Ev
Since the publication of Küppers’s and Dawkins’s work, other biologists havedevised more sophisticated evolutionary algorithms to simulate how mutationand selection—chance and necessity—can generate new information. One of
the most famous examples of such an algorithm is called Ev. Ev was createdby Thomas Schneider, a research biologist at the National Institutes of
Health.32 Schneider cites the success of Ev in simulating the production of
information “from scratch” to support his claim that the undirected material
processes of “replication, mutation and selection are necessary and sufficientfor information gain to occur.”33
In particular, Schneider claims Ev can simulate the production of
nucleotide binding sites. Nucleotide binding sites are very short sequences ofnucleotides on the genome located upstream of specific genes. These bindingsites enable RNA polymerase to bind to the correct place on a genome, thus
allowing it to read and copy the genes in question. The binding sites typically
represent between six and twenty specifically sequenced nucleotides, a small(but specified) amount of information in relation to an entire gene. Schneider
claims that “the program simulates the process of evolution of new bindingsites from scratch.”34 He further indicates that the Ev program has thus
created 131 bits of information.35
Though the Ev program uses a more complex set of subroutines thaneither of the early genetic algorithms devised by Küppers or Dawkins, it uses
essentially the same strategy to ensure that the program will generate an
information-rich sequence. Like Küppers and Dawkins, Schneider supplies Ev
with a target sequence, in this case a particular sequence of nucleotide basesthat function as a binding site. He then has the program generate a random
crop of sequences of equal length. After favoring sequences that manifest thegeneral profile of a binding site, Ev applies a fitness function to the remaining
sequences. The fitness function then assesses the degree of divergence
between the mutated sequences and the target sequence and applies an error
value to each mutated sequence. Then the Ev program preserves thesequence(s) with the least error (i.e., the degree of difference from the target)and permits them to replicate and mutate.36 Then he repeats the process again
and again, until finally Ev converges on the target sequence.
The target sequence involves foresight. So too does the program’s fitness

function, which makes selections based upon proximity to future function.
Thus, it again simulates a goal-directed foresight that natural selection does
not possess. It makes use of information about a functional state (nucleotide
binding sites) in a way that natural selection cannot.
Ev incorporates one additional step that Dawkins’s and Küppers’s
simulations lack. Before Ev applies its fitness function, it applies a filter to thecrop of mutated sequences. The filter favors sequences that have the generalprofile of a binding site. Like the fitness function, this coarser filter makes useof information about the functional requirements of binding sites to favor
some sequences over others. As such, it imparts information based on
knowledge that Thomas Schneider, not natural selection or the environment,
has imparted into the Ev simulation. Ev exhibits the genius of its designer.
Informational Accounting
Recently, the senior engineering professor Robert Marks, formerly of the
University of Washington in Seattle and now at Baylor University in Texas,
analyzed evolutionary algorithms such as Ev. Marks shows that despite claims
to the contrary by their sometimes overly enthusiastic creators, algorithms
such as Ev do not produce large amounts of functionally specified information“from scratch.” Marks shows that, instead, such algorithms succeed ingenerating the information they seek either by providing information about
the desired outcome (the target) from the outset, or by adding information
incrementally during the computer program’s search for the target. To
demonstrate this, Marks distinguishes and defines three distinct kinds ofinformation: exogenous information, endogenous information, and active
information.
“Endogenous information” represents the information present in thetarget. It also provides a measure of the difficulty of the search for that target—that is, the improbability of finding the specific sequence, or target, among
the exponentially large space of alternative possibilities. Recall that theamount of information present in a sequence or system is inversely
proportional to the probability of the sequence or system arising by chance. If
the probability of finding the target is small the information required to findthe target is correspondingly large. By calculating the size of the space ofalternative possibilities in which the target resides, the computer scientist candetermine both the probability of finding the target in a random search and theinformation content of the target in question. Marks’s analysis of evolutionary
algorithms shows that, in order to produce or find the (endogenous)
information present in the target, a programmer must design a searchalgorithm that reduces the information requirements of the search to amanageable level. The information added by the programmer to reduce thedifficulty of the search he dubs “active information.” The “exogenous
information” is what is left after the active information is subtracted from theendogenous information. It measures the difficulty of the residual search
problem.37
In his critique of Ev as well as other evolutionary algorithms, Marksshows that each of these putatively successful simulations of undirected
mutation and selection actually depends upon several sources of active
information. The Ev program, for example, uses active information by
applying a filter to favor sequences with the general profile of a nucleotide
binding site. And it uses active information in each iteration of its evaluation
algorithm or fitness function. The fitness function in Ev uses information
about the target sequence to assess degrees of difference between aprespecified target and the mutated sequences produced by the program.
Those sequences that have the lowest error values—greatest proximity to theprespecified functional sequence—are selected to replicate and mutate. Marksshows that each time the program uses knowledge of the target sequence toexclude some sequences and preserve others, it imparts a quantifiable amountof information in its selection. Marks quantifies these sources of active
information and shows that they reduce the difficulty well below the 131 bits
Schneider claims that Ev can produce “from scratch.”38 He also shows thatthe endogenous information in even modestly difficult search problems
usually cannot be generated (or the search problem solved) without added or“active” information to assist the search.
Avida
Since the release of Ev in 2000, another evolutionary algorithm has attracted
widespread interest. A program called Avida—A for artificial, and vida for life—was designed in 1993 by three computer scientists: Christopher Adami and
Titus Brown, of Caltech, and Charles Ofria, of Michigan State University.39
In 2003, Adami and Ofria, teamed up with biologist Richard Lenksi andphilosopher Robert Pennock, both of Michigan State, to publish an article in
Nature describing the results of an evolutionary simulation they had
conducted with Avida.40 In the article, the authors claimed that Avida
demonstrates that “digital organisms” capable of replication could generatecomplex features and functions “by random mutations and natural
selection.”41 Avida is now widely cited to show that biological complexity(and presumably the information necessary to produce it) could have arisen
from the twin forces of chance and necessity.42 Unlike the earlier simulationsof Dawkins, Küppers, and Schneider, the Avida program does not provide thecomputer with a specific target sequence, nor does it select sequences ofcharacters on the basis of proximity to possible future function. Does it
therefore demonstrate the efficacy of chance and necessity in a way thatearlier simulations did not?
Avida consists of two main parts, the Avida world and the Avida digitalorganisms that populate this world. Avida organisms have two basic parts:
software and hardware. Each Avida organism has a small software program
consisting of a loop of instructions composed from a set of twenty-six
predefined commands. Additionally, each digital organism also consists ofvirtual hardware that can execute each of the twenty-six commands among
the set of possibilities.
These commands direct Avida organisms to perform various operations on
two fixed input strings of binary digits, thus producing various output strings.
The loop of instructions contains a program that has been written to replicate
the loop (initially this is all the instructions in the loop do). Upon replication,
the loop of instructions is mutated as the Avida world makes random changes
to the loop by inserting, deleting, or switching commands, thus making it
possible for Avida organisms to generate new output strings from the two
fixed input strings (see Fig. 13.2).
These Avida organisms exist within a larger Avida “world.” The Avida
world represents the rules by which the digital organisms operate andcompete with each other for survival. The Avida world contains an evaluation
algorithm—what a colleague of mine calls the “sniffer”—that determines
whether one of nine logical relationships exists between the input strings
given to each digital organism and the output strings they produce. Anotherway to think of this is that the sniffer is evaluating whether a “logic function”
has been performed on the input strings to produce the output string. For
example, one of the logic functions the sniffer is looking for is OR. If the OR
function is applied to a pair of binary characters, then it will generate a “1” if
at least one or the other of the characters is a “1.” Otherwise it will generate a“0.”43 If all the binary digits in an output string are consistent with any of thenine logic functions having been applied to each pair of binary digits along
the two input strings, then the sniffer will recognize that a logic function has
been performed. (See Fig. 13.2.)

Figure 13.2. Visual representation of the Avida organism within Avida world.
The loop of instructions (left) operates on the input sequences (top) while
stored in the registers (center) to produce an output sequence (bottom). The
evaluation algorithm (depicted in personified form but only for effect)
compares the input sequence to the output sequence to see if one of ninespecific logical relationships exists between the sequences. If so, the Avida
world rewards the Avida organism with resources that improve the organism’s
ability to replicate itself.
In that case, the Avida organism that performed the logic function isrewarded with resources that improve its ability to replicate itself. Replication
is imperfect, meaning that the “offspring” of an Avida organism may carryrandom changes to the instructions in their program loops. These changes
involve replacing a command with another (from the set of twenty-six),
inserting a command (making the loop longer), or deleting a command(making the loop shorter).
The authors of the Nature paper claim to have used Avida to simulate how
natural selection and mutation can produce more complex functions byrearranging genomic instructions. Their simulation showed that after many
generations Avida produced digital organisms capable of performing both
simple and compound logic functions. (A compound logic function consists ofa series of discrete logic functions that have been performed in sequence.)44
As the authors put it, starting from a digital organism that could only replicate
itself, Avida evolved “digital organisms capable of performing multiple logic
functions requiring the coordinated execution of many genomic
instructions.”45 Since, in actual organisms, biological functions invariably
result from specified sequences of bases—that is, from specified information—and since the Avida authors claim to have simulated the production ofcomplex functions, they also effectively claim to have simulated theproduction of new biological information. So, does this state-of-the-art
algorithm solve or at least simulate a solution to the DNA enigma? Well, notexactly.
Avida Presupposes Information
First, even as a demonstration of how biological evolution might generate
new biological information starting from a preexisting organism, Avida leaves
much to be desired. As I discuss in more detail in an accompanying note,46
Avida lacks realism as a simulation of biological evolution because theprogram selects functionally significant logic functions possessing too little
complexity to represent the actual information content of functional proteinsor genes. By allowing it to choose logic functions that are far simpler than any
functional gene or protein, the program diminishes the probabilistic task thatnature would face in “trying” to evolve the first self-reproducing cell. Thus,
the program does not simulate how a random search through a relevantly
sized combinatorial space could generate new information—information andstructure that must arise first by random mutation before natural selection can
act to preserve it. Yet any complete theory of biological evolution must
explain precisely the origin of the specified information present in new genes
and proteins.
In any case, Avida does not simulate how the information necessary to
produce the first organism might have originated. Much like Eigen’s
hypercycles, Avida begins with a sizable complement of preexisting
information, including virtual “organisms” that have been programmed withthe capacity to self-replicate. In an Avida organism much of this informationis stored in the loop of discrete instructions drawn from the set of twenty-six.
These instructions express discrete commands such as, “Calculate thedifference between the values in two registers.” These instructions direct the
Avida organism to perform specific tasks on the strings of binary digits (theinputs) described above. These commands, whether expressed in the C++
programming language, a machine code, or translated into English, represent
a significant amount of functionally specified information.
In addition, the instructions on the initial loop in the digital organisms
were specifically arranged and programmed to replicate themselves. The
sequencing of these discrete instructions on the loop constitutes anothersignificant source of preexisting information beyond the information in the
instructions themselves. Still further, an intricately designed computing
device interprets the instruction sets. This computer hardware constitutes ahighly improbable and precisely configured arrangement of parts. These
devices represent a sizable amount of structural information that would haverequired a large (and, in principle, calculable) infusion of digital information
to manufacture. Therefore, if the Avida organisms tell us anything about theorigin of life, they tell us something about how much information must be
supplied before a self-replicating organism capable of selection could arise.
In fairness, Avida’s advocates do not explicitly claim that their program
simulates the origin of the information necessary to build a cell in the firstplace. Instead, they claim that Avida shows how new complex functions andfeatures (and the instructions necessary to produce them) might have arisen
from preexisting organisms. Even so, much of the hype surrounding Avida
fails to distinguish these two cases. In a recent conference presentation at
Oxford, Robert Pennock, one of the four authors of the Avida article in
Nature, claimed—rather categorically—that Avida simulates how natural
selection and random mutation produce “design without a designer.”47
Even if one were to ignore the intelligent design of the Avida program
itself and grant that Avida simulates how some appearances of design might
have arisen without a designer starting from a preexisting self-replicatingorganism, it would not follow that Avida has simulated the origin of the mostsalient appearance of design: the original “machine code of the genes.”
Instead, Avida presupposes, but does not explain, the origin of the informationnecessary to produce the first self-replicating organism. Avida “solves” theproblem of the origin of biological information by presupposing its solution at
the outset. It dissolves the pink stuff only by using more of it as a solvent.
The Conservation of Information
The failure of Ev and Avida to simulate a naturalistic solution to the DNA
enigma illustrates a more general problem with evolutionary algorithms and,indeed, a more general principle about information itself. Invariably,
evolutionary algorithms succeed in producing specified information (or its
functional equivalent) as the result of preexisting information, or the
programming skill of computer scientists, or both. With the exception of asmall and quantifiable amount of information that may arise as the result ofrandom effects, the information produced in these algorithms does not exceed
the information that was provided to them by the programmers who designed
them (and the engineers who designed the hardware). In some programs, suchas Richard Dawkins’s rather simple simulation, the role of the programmer inproviding the information necessary to the outcome is obvious. In others
programs, such as Avida, programmers play a more subtle role, though not
one that escapes a careful accounting of the source of critical informational
inputs. Either way, information in a computational context does not magicallyarise without the assistance of the computer scientist.

My office is in Redmond, Washington, and I have friends who arecomputer programmers for some of the software companies in the area. One
of my friends is a retired forty-something programmer, who was formerly oneof Microsoft’s elite architect-level programmers. He also has a special interestin the origin of life and evolutionary algorithms. He said somethinginteresting to me about these programs: “There is absolutely nothing
surprising about the results of these algorithms. The computer is programmed
from the outset to converge on the solution. The programmer designed thecode to do that. What would be surprising is if the program didn’t converge
on the solution. That would reflect badly on the skill of the programmer.
Everything interesting in the output of the program came as a result of the
programmer’s skill—the information input. There are no mysterious outputs.”
Computer science has two principles that codify this insight. Indeed, theseprinciples can be or, in some cases, were derived from a careful analysis ofevolutionary algorithms themselves. The first principle is called the “no freelunch” (NFL) theorem. The theorem was first developed by David Wolpert
and William Macready, two computer scientists at NASA’s Ames ResearchCenter.48 It describes a constraint that evolutionary algorithms invariably faceas they attempt to find information-rich targets—such as a meaningful
sequence of letters or a functional arrangement of nucleotide bases—in large
combinatorial spaces. The NFL theorem states that an evolutionary algorithm
will, on average, perform no better than a blind search in finding a target
within a large space of possibilities unless external sources of information areprovided to the algorithm to point it toward the target. In other words, finding
an information-rich target usually requires an enormous amount of “active
information” to reduce the difficulty of a random search to a manageable level(i.e., to a level commensurate with the probabilistic resources available to theevolutionary algorithm).
The game Twenty Questions illustrates this principle. In the game, one
person has information about a person, place, or thing that another person
must guess. If the person guessing is clever, he or she can acquire that
information by asking a series of yes or no questions that gradually narrows
the field of possibilities. But each time the person guessing receives a yes or
no to a question, he or she is also receiving information. The NFL theorem
simply states that such information is usually—barring a rare and very lucky
search—indispensable to the success of the search. Richard Dawkins’s
simulation provided an obvious illustration of this principle. His evolutionary
algorithm succeeded, but only because he provided it with an external sourceof information—in particular, a target sequence and fitness function—which
the computer uses to guide its selection of sequences in each successive
iteration of its search.
The NFL theorem affirms what a careful analysis of individualevolutionary simulations reveals: the information produced by anevolutionary simulation does not arise for “free,” that is, without an input
from the programmer. Large informational outputs require (roughly)
equivalent informational inputs.
The second principle relevant to the assessment of evolutionary
algorithms is closely related to the first. It is called the law of the“conservation of information” (COI). Leon Brillouin, a French-American
physicist and innovator in computer science and information theory, states thelaw in its most basic form: “The computing machine does not create any new
information.”49 Robert Marks has a more colorful way of expressing the sameidea. “Computers,” he says, “are no more able to create information thaniPods are capable of creating music.”50 Computer scientists have formulated
various mathematically precise laws of conservation of information to express
this basic principle. Most state that within certain quantifiable limits theamount of information in a computer in its initial state (considering both its
hardware and software) equals or exceeds the amount of information in its
final state. A careful analysis of evolutionary algorithms confirms thisprinciple. Genetic algorithms can “solve” the information problem, but only if
programmers first supply information about proximity to target sequences,
selection criteria, or loops of precisely sequenced instructions.
But I noticed something else. The idea of the conservation of information
also seems to apply beyond the computer domain. Indeed, most of us know
from our ordinary experience that information typically degrades over time
unless intelligent agents generate (or regenerate) it. The sands of time haveerased some inscriptions on Egyptian monuments. The leak in the attic roofsmudged the ink in the stack of old newspapers, making some illegible. In thegame of Telephone, the message received by the last child in line bears little
resemblance to the one the first child whispered. Common experience
confirms this general trend—and so do prebiotic simulation experiments andorigin-of-life research. Simulation experiments produce biologically relevantmolecules, but only if experimentalists manipulate initial conditions andguide outcomes—that is, only if they first provide specified information
themselves. Those origin-of-life theories “succeed” in accounting for theorigin of information only by presupposing an ample supply of information insome preexisting form.
In light of this, I formulated a conservation law of my own as a working
hypothesis to distill my experience and what I had discovered about origin-oflife research. Since I was not principally concerned with whether biologicalevolution could generate specified information, I decided to formulate a“conservative” conservation law—one that applied only to a nonbiologicalcontext (and thus not to an information-rich initial state). My statement of thelaw does not say anything about whether undirected natural processes could
produce an increase in specified information starting from preexisting formsof life. But it does encapsulate what repeated experience had demonstrated
about the flow of information starting from chemistry and physics alone.
Here’s my version of the law of conservation of information: “In anonbiological context, the amount of specified information initially present ina system, Si, will generally equal or exceed the specified information content
of the final system, Sf.” This rule admits only two exceptions. First, theinformation content of the final state may exceed that of the initial state, Si, if
intelligent agents have elected to actualize certain potential states while
excluding others, thus increasing the specified information content of thesystem. Second, the information content of the final system may exceed thatof the initial system if random processes have, by chance, increased thespecified information content of the system. In this latter case, the potential
increase in the information content of the system is limited by the“probabilistic resources” available to the system.
As noted in Chapter 10, the probabilistic resources of the entire universe
equal 10139 trials, which, in turn, corresponds to an informational measure ofless than 500 bits. This represents the maximum information increase that
could be reasonably expected to occur by chance from the big-bang
singularity to the present—without assistance from an intelligent agent.
Systems that exist over a shorter period of time will have correspondingly
smaller envelopes of maximal information increase and will, in any case,usually experience informational loss or degradation without input from anagent. Taking these caveats into account allows a more general statement of
the law as follows: “In a nonbiological context and absent intelligent input,
the amount of specified information of a final system, Sf, will not exceed thespecified information content of the initial system, Si, by more than thenumber of bits of information the system’s probabilistic resources cangenerate, with 500 bits representing an upper bound for the entire observable
universe.”51
Conclusion
The law of the conservation of information as variously articulated provides
another way of describing the displacement problem that I had seen
repeatedly demonstrated by attempts to solve the DNA enigma. The failure ofgenetic algorithms to simulate the production of specified information within
the obviously artificial domain of a computer only provided anotherillustration of this apparently ubiquitous problem. These attempts to simulatehow purely undirected processes might have produced information only
pushed the information problem back to a decidedly directing entity—thehuman mind. Thus, in an oddly unexpected way, evolutionary algorithms andthe conservation principle derived in part from analyzing them pointed to an
external source of specified information. For this reason, the law ofconservation of information seemed to have profound implications for theorigin-of-life debate.
But is the conservation law true? It was certainly consistent witheverything I knew from my ordinary prescientific experience. And I had
discovered nothing in my study of origin-of-life research to contradict thelaw. Quite the opposite was the case. Model after model failed precisely to
explain the origin of biological information, the DNA enigma. Those theories
that appeared to solve the problem of the origin of information—themetaphorical “pink stuff”—did so only by displacing or transferring it
elsewhere.
Yet I also knew that there was another major hypothesis about the originof life that was attracting attention. It too combined chance and necessity, but
envisioned a role for natural selection much earlier in the process of
abiogenesis. This hypothesis also held out the possibility of explaining theclassical “chicken and egg” problem—the origin of the interdependence of
DNA and proteins—by starting the process of abiogenesis in a different place—or rather, with a different molecule.
The “RNA world” hypothesis as it is called, is the big one—the idea thathas currently captivated the wider scientific community and a host of working
molecular biologists “at the bench.” Could this model solve the problem of
the origin of biological information without displacing it elsewhere? Does it
refute the law of information conservation—or at least provide a documented
exception to it? Before I could establish that biological information pointed
decisively to an intelligent cause, I knew I would need to examine this theory
in more detail. The idea was too big and too popular to ignore.

14
The RNA World

By the mid-1980s many researchers concluded that both DNA-first andprotein-first origin-of-life models were beset with many difficulties. As aresult, they sought a third way to explain the mystery of life’s origin. Insteadof proposing that the first informational molecules were proteins or DNA,these scientists argued that the earliest stages of abiogenesis unfolded in achemical environment dominated by RNA molecules. The first scientist to
propose this idea was Carl Woese, a microbiologist at the University of
Illinois. Walter Gilbert, a Harvard biophysicist, later developed the proposal
and coined the term by which it is now popularly known, the “RNA world.”1
The RNA world is now probably the most popular theory of how life
began. Scientists in some of the most prestigious labs around the world have
performed experiments on RNA molecules in an attempt to demonstrate its
plausibility, and in the opinion of many scientists, the RNA-world hypothesis
establishes a promising framework for explaining how life on earth might
have originated.
I had an encounter with one such scientist in the spring of 2000. I had justwritten an article about DNA and the origin of life in the April issue of aprominent New York journal of opinion.2 When the letters to the editor camein, I initially blanched when I saw one from a fierce critic named Kenneth R.
Miller, a biology professor at Brown University and a skilled debater. Had I
made a mistake in reporting some biological detail in my argument? When I
saw his objection, however, I was relieved. Miller claimed that my critique ofattempts to explain the origin of biological information had failed to address
the “RNA first” hypothesis. Miller asserted that I had ignored “nearly two
decades of research on this very subject” and failed to tell my “readers ofexperiments showing that very simple RNA sequences can serve as biologicalcatalysts and even self-replicate.”3

Figure 14.1. Walter Gilbert, photographed in front of a chalkboard in his
office at Harvard. Courtesy of Peter Menzel/Science Photo Library
Miller was half right. I hadn’t told my readers about these experiments.
But I knew that two decades of research on this topic had not solved the
problem of the origin of biological information. Because of space constraints
and the format of the journal, I had decided not to address this issue in myoriginal article. But now Miller’s letter gave me a chance to do so.
At the time I had been studying research articles from origin-of-life
specialists who were highly critical of the RNA-world hypothesis, and in myresponse to Miller I cited and summarized many of their arguments. I heard
nothing more from Miller on the matter, but as I attended various conferences
over the next several years, I discovered that he was far from alone. Despite
the pervasive skepticism about the RNA world among leading origin-of-liferesearchers, many practicing molecular biologists, including some very
prominent scientists at famous labs, continued to share Miller’s enthusiasm.
Moreover, I discovered that many of these molecular biologists had recently
initiated new experimental work inspired by their confidence in the viabilityof the RNA-world approach. Had they solved the information problem?
Second Things FirstThe RNA world is a world in which the chicken and egg no longer confoundeach other. At least that has been the hope. Building proteins requires genetic
information in DNA, but information in DNA cannot be processed without
many specific proteins and protein complexes. This problem has dogged
origin-of-life research for decades. The discovery that certain molecules ofRNA possess some of the catalytic properties seen in proteins suggested a
way to solve the problem. RNA-first advocates proposed an early stage in thedevelopment of life in which RNA performed both the enzymatic functions ofmodern proteins and the information-storage function of modern DNA, thus
sidestepping the need for an interdependent system of DNA and proteins in
the earliest living system.
Typically RNA-first models have combined chance events and a law-likeprocess of necessity, in particular, the process of natural selection. As Gilbert
and others envision it, a molecule of RNA capable of copying itself (orcopying a copy of itself) first arose by the chance association of nucleotide
bases, sugars, and phosphates in a prebiotic soup (see Fig. 14.2). Thenbecause that RNA enzyme could self-replicate, natural selection ensued,
making possible a gradual increase in the complexity of the primitive selfreplicating RNA system, eventually resulting in a cell with the features weobserve today. Along the way, a simple membrane, itself capable of selfreproduction, enclosed the initial RNA enzymes along with some amino acids
from the prebiotic soup.4
According to this model, these RNA enzymes eventually were replacedby the more efficient proteins that perform enzymatic functions in modern
cells. For that to occur, the RNA-replicating system first had to begin
producing a set of RNA enzymes that could synthesize proteins. As Gilbert
has explained, in this step RNA molecules began “to synthesize proteins, firstby developing RNA adapter molecules that can bind activated amino acids
and then by arranging them according to an RNA template using other RNA
molecules such as the RNA core of the ribosome.”5 Finally, DNA emerged forthe first time by a process called reverse transcription. In this process, DNA
received the information stored in the original RNA molecules, and eventuallythese more stable DNA molecules took over the information-storage role thatRNA had performed in the RNA world. At that point, RNA was, as Gilbert
put it, “relegated to the intermediate role it has today—no longer the center ofthe stage, displaced by DNA and the more effective protein enzymes.”6

Figure 14.2. The RNA World Scenario in Seven Steps. Step 1: The building
blocks of RNA arise on the early earth. Step 2: RNA building blocks link up
to form RNA oligonucleotide chains. Step 3: An RNA replicase arises bychance and selective pressures ensue favoring more complex forms ofmolecular organization. Step 4: RNA enzymes begin to synthesize proteinsfrom RNA templates. Step 5: Protein-based protein synthesis replaces RNAbased protein synthesis. Step 6: Reverse transcriptase transfers genetic
information from RNA molecules into DNA molecules. Step 7: The modern
gene expression system arises within a proto-membrane.
I knew that origin-of-life theories that sound plausible when stated in a
few sentences often conceal a host of practical problems. And so it was with
the RNA world. As I investigated this hypothesis, both before and after myexchange with Professor Miller, I found that many crucial problems lurked in
the shadows, including the one I had seen before: the theory did not solve theproblem of biological information—it merely displaced it.
Because so many scientists assume that the RNA world has solved the
problem of the origin of life, this chapter will provide a detailed and, in someplaces, technical critique of this hypothesis. My critique details five crucial
problems with the RNA world, culminating in a discussion of the information
problem. To assist nontechnical readers, I have placed some of this critique innotes for the scientifically trained. I would ask technically minded readers toread these notes in full, because in some cases they provide important
additional support for, or qualifications to, my arguments.
Each element of this critique stands mostly on its own. So if you find thatthe technical material under one subheading presupposes unfamiliar scientificconcepts or terminology, take note of the heading, which summarizes the
take-home message of the section, and skip ahead to the next one, or even the
final two, which address the theory’s greatest weakness: its inability to
explain the origin of biological information.
Problem 1: RNA Building Blocks Are Hard to Synthesize and Easy to
Destroy
Before the first RNA molecule could have come together, smaller constituent
molecules needed to arise on the primitive earth. These include a sugar known
as ribose, phosphate molecules, and the four RNA nucleotide bases (adenine,cytosine, guanine, and uracil). It turns out, however, that both synthesizing
and maintaining these essential RNA building blocks, particularly ribose (thesugar incorporated into nucleotides) and the nucleotide bases, has proveneither extremely difficult or impossible to do under realistic prebiotic
conditions.7 (See Fig. 14.3.)
Consider first the problems with synthesizing the nucleotide bases. In theyears since the RNA world was proposed, chemist Robert Shapiro has made a
careful study of the chemical properties of the four nucleotide bases to assess
whether they could have arisen on the early earth under realistic conditions.
He notes first that “no nucleotides of any kind have been reported as productsof spark-discharge experiments or in studies of meteorites.” Stanley Miller,
who performed the original prebiotic simulation experiment, published asimilar study in 1998.8 Moreover, even if they did somehow form on the early
earth, nucleotide bases are too chemically fragile to have allowed life enoughtime to evolve in the manner Gilbert and other RNA-first theorists envision.
Shapiro and Miller have noted that the bases of RNA are unstable attemperatures required by currently popular high-temperature origin-of-lifescenarios. The bases are subject to a chemical process known as“deamination,” in which they lose their essential amine groups (NH2). At 100
degrees C, adenine and guanine have chemical half-lives of only about one
year; uracil has a half-life of twelve years; and cytosine a half-life of justnineteen days. Because these half-lives are so short, and because theevolutionary process envisioned by Gilbert would take so long—especiallyfor natural selection to find functional ribozymes (RNA molecules withcatalytic activity) by trial and error—Stanley Miller concluded in 1998 that “ahigh temperature origin of life involving these compounds [the RNA bases]
therefore is unlikely.”9 Miller further noted that, of the four required bases,
cytosine has a short half-life even at low temperatures, thus raising thepossibility that “the GC pair” (and thus RNA) “may not have been used in thefirst genetic material.” Shapiro concurred. He showed that it would have been
especially difficult to synthesize adenine and cytosine at high temperaturesand cytosine even at low temperatures. Thus he concluded that thepresumption that “the bases, adenine, cytosine, guanine and uracil werereadily available on the early earth” is “not supported by existing knowledgeof the basic chemistry of these substances.”10

Figure 14.3. The chemical structure and constituents of RNA.
Producing ribose under realistic conditions has proven even more
problematic. Prebiotic chemists have proposed that ribose could have arisenon the early earth as the by-product of a chemical reaction called the formose
reaction. The formose reaction is a multistep chemical reaction that begins as
molecules of formaldehyde in water react with one another. Along the way,
the formose reaction produces a host of different sugars, including ribose, asintermediate by-products in the sequence of reactions. But, as Shapiro haspointed out, the formose reaction will not produce sugars in the presence ofnitrogenous substances.11 These include peptides, amino acids, and amines, acategory of molecules that includes the nucleotide bases.
This obviously poses a couple of difficulties. First, it creates a dilemma
for scenarios that envision proteins and nucleic acids arising out of a prebiotic
soup rich in amino acids. Either the prebiotic environment contained aminoacids, which would have prevented sugars (and thus DNA and RNA) fromforming, or the prebiotic soup contained no amino acids, making protein
synthesis impossible. Of course, RNA-first advocates might try to circumvent
this difficulty by proposing that proteins arose well after RNA. Yet since theRNA-world hypothesis envisions RNA molecules coming into contact withamino acids early on within the first protocellular membranes (see above),choreographing the origin of RNA and amino acids to ensure that the two
events occur separately becomes a considerable problem.
The RNA-world hypothesis faces an even more acute, but related,
obstacle—a kind of catch-22. The presence of the nitrogen-rich chemicals
necessary for the production of nucleotide bases prevents the production ofribose sugars. Yet both ribose and the nucleotide bases are needed to build
RNA. (See note for details).12 As Dean Kenyon explains, “The chemical
conditions proposed for the prebiotic synthesis of purines and pyrimidines[the bases] are sharply incompatible with those proposed for the synthesis ofribose.”13 Or as Shapiro concludes: “The evidence that is currently available
does not support the availability of ribose on the prebiotic earth, except
perhaps for brief periods of time, in low concentration as part of a complex
mixture, and under conditions unsuitable for nucleoside synthesis.”14
Beyond that, both the constituent building blocks of RNA and whole RNA
molecules would have reacted readily with the other chemicals present in theprebiotic ocean or environment. These “interfering cross-reactions” would
have inhibited the assembly of RNA from its constituent monomers andinhibited any movement from RNA molecules toward more complex
biochemistry, since the products of these reactions typically produce
biologically inert (or irrelevant) substances.
Furthermore, in many cases, reactions (such as the formose reaction) that
produce desirable by-products such as ribose also produce many undesirable
chemical by-products. Unless chemists actively intervene, undesirable anddesirable chemical by-products of the same reaction react with each other toalter the composition of the desired chemicals in ways that would inhibit theorigin of life. In sum, synthesizing the building blocks of the RNA molecule
under realistic prebiotic conditions has proven formidably difficult.
Problem 2: Ribozymes Are Poor Substitutes for ProteinsAnother major problem with the RNA world is that naturally occurring RNA
molecules possess very few of the specific enzymatic properties of proteins.
To date, scientists have shown that ribozymes can perform a small handful of
the thousands of functions performed by modern proteins. Scientists haveshown that some RNA molecules can cleave other RNA molecules (at thephosphodiester bond) in a process known as hydrolysis. Biochemists also
have found RNAs that can link (ligate) separate strands of RNA (bycatalyzing the formation of phosphodiester bonds). Other studies have shownthat the RNA in ribosomes (rRNA) promotes peptide-bond formation within
the ribosome15 and can promote peptide bonding outside the ribosome,though only in association with an additional chemical catalyst.16 Beyond
that, RNA can perform only a few minor functional roles and then usually as
the result of scientists intentionally “engineering” or “directing” the RNA
catalyst (or ribozyme) in question.17
For this reason, claiming that catalytic RNA could replace proteins in theearliest stages of chemical evolution is extremely problematic. To say
otherwise would be like asserting that a carpenter wouldn’t need any tools
besides a hammer to build a house, because the hammer performed two or
three carpentry functions. True, a hammer does perform some carpentry
functions, but building a house requires many specialized tools that canperform a great variety of specific carpentry functions. In the same way, RNAmolecules can perform a few of the thousands of different functions proteins
perform in “simple” single cells (e.g., in the E. coli bacterium), but that does
not mean that RNA molecules can perform all necessary cellular functions.
Problem 3: An RNA-based Translation and Coding System Is
Implausible
The inability of RNA molecules to perform many of the functions of protein
enzymes raises a third and related concern about the plausibility of the RNA
world. RNA-world advocates offer no plausible explanation for how primitive
self-replicating RNA molecules might have evolved into modern cells that
rely on a variety of proteins to process genetic information and regulate
metabolism.18
To evolve beyond the RNA world, an RNA-based replication system
eventually would have to begin to produce proteins, and not just any proteins,but proteins capable of template-directed protein manufacture. But for that tooccur, the RNA replicator first would need to produce machinery for building
proteins. In modern cells it takes many proteins to build proteins. So, as a firststep toward building proteins, the primitive replicator would need to produce
RNA molecules capable of performing the functions of the modern proteinsinvolved in translation. (Recall from Chapter 5 that translation is the processof building proteins from the instructions encoded on an mRNA transcript.)
Presumably, these RNA molecules would need to perform the functions of thetwenty specific tRNA synthetases and the fifty ribosomal proteins, among themany others involved in translation. At the same time, the RNA replicator
would need to produce tRNAs and the many mRNAs carrying the information
for building the first proteins. These mRNAs would need to be able to direct
protein synthesis using, at first, the transitional ribozyme-based proteinsynthesis machinery and then, later, the permanent and predominantly
protein-based protein-synthesis machinery. In short, the evolving RNA world
would need to develop a coding and translation system based entirely on RNA
and also generate the information necessary to build the proteins that later
would be needed to replace it.
This is a tall order. The cell builds proteins from the information stored on
the mRNA transcript (i.e., the copy) of the original DNA molecule. To do this,a bacterial cell depends upon a translation and coding system consisting of
106 distinct but functionally integrated proteins as well several distinct typesof RNA molecules (tRNAs, mRNAs, and rRNAs).19 This system includes theribosome (consisting of fifty distinct protein parts), the twenty distinct tRNA
synthetases, twenty distinct tRNA molecules with their specific anticodons(all of which jointly embody the genetic code), various other proteins, freefloating amino acids, ATP molecules (for energy), and—last, but not least—
information-rich mRNA transcripts for directing protein synthesis.
Furthermore, many of the proteins in the translation system perform multiple
functions and catalyze coordinated multistep chemical transformations (seeFig. 14.4).
Is it possible that a similar translation and coding system capable ofproducing genetically encoded proteins might first have arisen using only
RNA catalysts (ribozymes)? Advocates of the RNA-world hypothesis havedefended the possibility because of the demonstrated catalytic properties ofsome RNA molecules. Eugene Koonin and Yuri Wolf, two prominentscientists at the National Center for Bio-technology Information, recently
reviewed the results of research on the capacities of RNA catalysts in an
important article assessing the plausibility of an RNA-based translation
system.20 They note that in the last twenty years, molecular biologists havedocumented, or engineered, ribozymes that can catalyze “all three elementary
reactions”21 required for translation, including aminoacylation (the formationof a bond between an amino acid and an RNA), the peptidyl-transferase
reaction (which forms the peptide bond between amino acids), and aminoacid activation (in which adenosine monophosphate is attached to an aminoacid).

At first glance, these results may seem to support the feasibility of an
RNA-based translation system. Nevertheless, significant reasons to doubt this
aspect of the RNA-world hypothesis remain, as Koonin and Yuri note. First,
though ribozymes have demonstrated the capacity to catalyze representative
examples of the three main types of chemical reactions involved intranslation, they have not demonstrated the ability to catalyze anywhere near
all the necessary reactions that fall within these general classifications.
Moreover, the gap between “some” and “all” necessary reactions of a given
type remains significant. For example, ribozyme engineers have successfully
designed an RNA molecule that will catalyze the formation of an aminoacyl
bond between itself and the amino acids leucine and phenylalanine.22 But noone has yet demonstrated that RNA can catalyze aminoacyl bonds with theother eighteen protein-forming amino acids, still less with the specificity
required to make the resulting molecules useful for translation. Yet
establishing a genetic code requires molecules that can catalyze highly
specific aminoacylation for each of the twenty protein-forming amino acids.
To say that RNA can catalyze “aminoacylation” is true, but it obscures thedistinction between part of a group and the whole group, where having thewhole group of molecules is necessary to the function in question. Again, it
takes more than a hammer to build a house.

Figure 14.4. The main molecular components of the translation system:
twenty specific transfer-RNA molecules, twenty specific aminoacyl tRNA
synthetases, the ribosome with its two main subunits composed of fifty

proteins and ribosomal RNA, the messenger-RNA transcript and a supply of
amino acids.
Second, unlike RNA catalysts (ribozymes), the protein-based enzymes
involved in translation perform multiple functions, often in closely integrated
or choreographed ways. Ribozymes, however, are the one-trick ponies of themolecular world. Typically, they can perform one subfunction of the several
coordinated functions that a corresponding enzyme can perform. But theycannot perform the entire range of necessary functions, nor can they do sowith the specificity needed to execute the many sequentially coordinatedreactions that occur during translation.
Consider what ribozymes must do to rival the capacities of thesynthetases that catalyze aminoacylation, which occurs between tRNA
molecules and their “conjugate” amino acids during translation in actual cells.
Researchers have demonstrated that certain RNA molecules can bind aprotein-forming amino acid, phenylalanine, to itself, thus performing thefunction of aminoacylation. They have even isolated a version of the RNA
catalyst that binds only phenylalanine, achieving a specificity of sorts. But thesynthetase enzymes responsible for aminoacylation in life must catalyze acomplex two-stage chemical reaction involving three kinds of molecules:amino acids, ATP (adenosine triphosphate), and tRNAs.
In the first stage of this reaction, synthetases couple ATP to a specificamino acid, giving it the stored energy (in the form of adenosine
monophosphate, AMP) needed to establish a bond with a tRNA molecule.
Next, synthetases couple specific tRNA molecules to specific activated(AMP-charged) amino acids. These tRNAs have specific shapes andanticodon sites that enable them to bond to mRNA at the ribosome. Thus,
synthetases help form molecular complexes with a specificity of fit and withspecific binding sites that enable translation to occur in the context of a wholesystem of associated molecules.
The RNA catalyst proposed as a precursor to the synthetase cannot do
this. It does not couple ATP to amino acids as a precursor to catalyzing
aminoacylation. Instead, the ribozyme engineer provides “preadenylated”
amino acids (amino acids already linked to AMP molecules). Nor does the
RNA catalyst couple an amino acid to a specific tRNA with a specificanticodon. The more limited specificity it achieves only ensures that the RNA
catalyst will bind a particular amino acid to itself, a molecule that does notpossess the specific cloverleaf shape or structure of a tRNA. Moreover, thisRNA does not carry an anticodon binding site corresponding to a specificcodon on a separate mRNA transcript. Thus, it has no functional significancewithin a system of molecules for performing translation. Indeed, no othersystem of molecules has even been proposed that could confer functionalsignificance or specificity on the amino acid–RNA complexes catalyzed by
the aminoacyl ribozyme.
Thus, even in the one case where ribozyme engineers have produced an
RNA-aminoacyl catalyst, the ribozyme in question will not produce amolecule with a functional specificity, or capacity to perform coordinatedreactions, equivalent to that of the synthetases used in modern cells. Yet
without this specificity and capacity to coordinate reactions, translation—theconstruction of a sequence-specific arrangement of amino acids from thespecific RNA transcript—will not occur.23
Similar limitations affect the RNA catalysts that have been shown to becapable of peptidyl-transferase activity (i.e., catalyzing peptide bonds
between amino acids). These ribozymes (made of free-standing ribosomal
RNA) compare quite unfavorably with the capacities of the protein-dominatedribosomes that perform this function in extant cells. For example, researchers
have found that free-standing ribosomal RNA can only catalyze peptide-bondformation in the presence of another catalyst. More important, apart from theproteins of the ribosome, free-standing ribosomal RNA does not force aminoacids to link together into linear chains, which is essential to protein function.
(For more details, see the note.)24
Why RNA Catalysts Can’t Do What True Enzymes Can
There is a fundamental chemical reason for the limited functionality of RNA
catalysts—one that casts still further doubt on the RNA-world hypothesis andspecifically on its account of the origin of the translation system. Because of
the inherent limitations of RNA chemistry,25 single RNA molecules do not
catalyze the coordinated multistep reactions that enzymes, such assynthetases, catalyze. Even if separate RNA catalysts can be found thatcatalyze each of the specific reactions involved in translation (which is by nomeans certain), that would leave us very far short of a translation system.
Each pony of the RNA world does only its one trick. And even if all theponies were present together, each one would do only its particular trick
separately, decoupled from the others. That’s a problem, because producing
the molecular complexes necessary for translation requires coupling multiple
tricks—multiple crucial reactions—in a closely integrated (and virtuallysimultaneous) way. True enzyme catalysts do this. RNA and small-molecule
catalysts do not.
Here’s the chemical backstory. Enzymes couple energetically favorable
and unfavorable reactions together into a series of reactions that areenergetically favorable overall. As a result, they can drive forward tworeactions where ordinarily only one would occur with any appreciablefrequency. Water runs downhill because of favorable energetics provided bygravitational force. Water does not run uphill, however, unless there is somuch of it that it accumulates and slowly rises up the bank. Whether chemical
reactions will occur readily depends upon whether there is enough energy tomake them occur. Molecules with enough stored energy to establish new
chemical bonds will react readily with one another. Molecules withinsufficient stored energy will not react readily with each other unless vast
amounts of the reactants are provided (the equivalent of the rising water
flooding the banks).
Enzymes use a reaction that liberates energy to drive forward a reactionthat requires energy, coupling energetically favorable and unfavorable
reactions together. Enzymes can do this because they have a complex threedimensional geometry that enables them to hold all the molecules involved ineach step of the reaction together and to coordinate their interactions. But two
independent catalysts cannot accomplish what a compound catalyst (i.e., an
enzyme) can. And so far RNA ribozymes have demonstrated the capacity to
act only as independent catalysts, not true enzyme catalysts. RNA catalysts
might catalyze some energetically favorable reactions, but without thesophisticated active sites of enzymes, they can’t couple those favorable
reactions to energetically unfavorable reactions.26 (See Fig. 14.5.)

Figure 14.5. Enzymes couple energetically favorable and unfavorable
reactions together into a series of reactions that are energetically favorable
overall. Enzymes can accomplish this because they have a three-dimensional

specificity that allows them to sequester and correctly position all themolecules involved in a series of such reactions. RNA catalysts cannot do
this. The figure above shows an enzymatically mediated reaction called
aminoacylation. The diagram shows the specificity of fit between a tRNA
synthetase and a tRNA molecule during this two-stage chemical reaction. The
synthetase links the tRNA to a specific amino acid using energy from ATP,thus coupling energetically favorable and unfavorable reactions. Amino acids
and ATP molecules are not pictured. They would be enveloped by thesynthetase during the reactions represented by the chemical equations.
Thus, the demonstration that RNA can catalyze “all the elementary
reactions” of translation, but neither the suite of functions nor the coordinatedfunctions performed by the necessary enzyme catalysts of the extant
translation system, does little to establish the plausibility of ribozyme-based
protein synthesis, let alone the transition to enzyme-based protein synthesis,that the RNA-world scenario requires. The inability to account for the originof the translation system and genetic code, therefore, remains a formidable
barrier to the success of the RNA-world hypothesis.
Problem 4: The RNA World Doesn’t Explain the Origin of Genetic
Information
As I sifted through the primary scientific literature on the RNA-world
hypothesis, it did not take me long to realize that the hypothesis faced
significant problems quite apart from the central sequencing problem thatmost interested me. Yet I also realized that it did not resolve the mystery of
the origin of biological information—which I had, heretofore, called the DNA
enigma. Indeed, I now realized that I might just as easily have called thatmystery the “RNA enigma,” because the information problem looms just as
large in a hypothetical RNA world as it does in a DNA world. This is not
actually surprising. The RNA world was proposed not as an explanation forthe origin of biological information, but as an explanation for the origin of theinterdependence of nucleic acids and proteins in the cell’s informationprocessing system. And as I studied the hypothesis more carefully, I realizedthat it presupposed or ignored, rather than explained, the origin of sequencespecificity—information—in various RNA molecules.
Consider the step in the RNA-world scenario that I just examined—
getting from a primitive replicator to a system for building the first proteins.
Even if a system of ribozymes for building proteins had arisen from an RNA
replicator, that system of molecules would still need information-rich
templates for building specific proteins. RNA-world advocates give noaccount of the origin of that information beyond vague appeals to chance. But
as I argued in Chapters 8–10, chance is not a plausible explanation for the
information necessary for building even one protein of modest length, letalone a set of RNA templates for building the proteins needed to establish aprotein-based translation system and genetic code.
The need to account for these templates of information stands as aformidable challenge to the RNA world. Nevertheless, the hypothesis faces aneven more basic information problem: the first self-replicating RNA
molecules themselves would have needed to be sequence-specific in order toperform the function of replication, which is a prerequisite of both natural
selection and any further evolution toward cellular complexity.
Though the RNA world was originally proposed as an explanation for the“chicken and egg” functional interdependence problem, not the information
problem, some RNA-world advocates nevertheless appear to think that it cansomehow leapfrog the sequence-specificity requirement. They imagine shortchains (oligomers) of RNA arising by chance on the prebiotic earth. Then,after a sufficiently large pool of these molecules had arisen, some would haveacquired the ability to self-replicate. In such a scenario the capacity to selfreplicate would then favor the survival of those RNA molecules that could doso and thus would favor the specific sequences that the first self-replicating
molecules happened to have. Thus, self-replication arose again as kind of“accidental choice remembered.”27
But like Quastler’s DNA-first model discussed in the last chapter, this
scenario merely shifts the specificity problem out of view. First, for strands of
RNA to perform catalytic functions (including self-replication) they, like
proteins, must display specific arrangements of their constituent building
blocks (nucleotides in the RNA case). In other words, not just any sequence ofRNA bases will be capable of self-replication. Indeed, experimental studies
indicate that RNA molecules with the capacity to replicate themselves, if they
exist at all, are extremely rare among possible RNA base sequences. Although
no one has yet produced a fully self-replicating RNA molecule,28 someresearchers have engineered a molecule that can copy a part of itself—though
only about 10 percent of itself and then only if a complementary primer strand
is provided to the ribozyme by the investigator. Significantly, the scientists
selected this partial self-replicator out of an engineered pool of 1,000 trillion(1015) other RNA molecules, almost all of which lack even this limited
capacity for self-replication.29 This suggests that sequences with this capacity
are extremely rare and would be especially so within a random(nonengineered) sample.
Further, for an RNA molecule to self-replicate, the RNA strand must be
long enough to form a complex structure. Gerald Joyce and the late Leslie

Orgel are two prominent origin-of-life researchers who have evaluated theRNA-world scenario in detail. They consider, for the sake of argument, that areplicase could form in a 50-base RNA strand, though they are clearly
skeptical that an RNA sequence of this length would really do the job.30
Experimental results have confirmed their skepticism. Jack Szostak, aprominent ribozyme engineer, and his colleagues have found that it typicallytakes at least 100 bases to form structures capable of catalyzing simple
ligation (linking) reactions. He estimates that getting a ligase capable ofperforming the other functions that polymerases must perform—“proper
template binding, fidelity and strand separation”—may require between 200
and 300 nucleotides.31 The ribozyme mentioned above—the one that canpartially copy itself—required 189 nucleotide bases.32 It is presently unclear
how many bases would be needed to generate enough structural complexity toallow true polymerase function, since no molecule capable of both complete
and unassisted self-replication has yet been engineered. It may be as low as
189 bases, but it may be much higher, or it may simply be impossible.33
Moreover, the problem may be more basic than length. RNA, with its limitedalphabet of four bases, may not even have the capacity to form the complex
three-dimensional shapes and distributions of charge necessary to performpolymerase or replicase function.
In any case, even if we suppose that RNA-based RNA polymerases(replicases) are possible, experimental evidence indicates that they would
have to be information-rich—both complex and specified—just like modern
DNA and proteins. Yet explaining how the building blocks of RNA might
have arranged themselves into information-rich sequences has proven noeasier than explaining how the parts of DNA might have done so, given therequisite length and specificity of these molecules. As Christian de Duve has
noted in critique of the RNA-world hypothesis, “Hitching the components
together in the right manner raises additional problems of such magnitude thatno one has yet attempted to do so in a prebiotic context.”34
Certainly, appeals to chance alone have not solved the RNA information
problem. A 100-base RNA molecule corresponds to a space of possibilities
equal to 4100 (or 1060). A 200-base RNA molecule corresponds to 4200 (or10120) possibilities. Given this and the experiments mentioned above showing
the rarity of functional ribozymes (to say nothing of polymerases) within
RNA sequence space, the odds of a functional, self-replicating RNA sequencearising by chance are exceedingly small. Moreover, the odds against such an
event occurring are only compounded by the likely presence of destructive
cross-reactions between desirable and undesirable molecules within any
realistic prebiotic environment.
To make matters worse, as Gerald Joyce and Leslie Orgel note, for asingle-stranded RNA catalyst to produce an RNA identical to itself (i.e., to“self-replicate”), it must find an appropriate RNA molecule nearby to function
as a template, since a single-stranded RNA cannot function as both replicase
and template. Moreover, as they observe, this RNA template would have to be
the precise complement of the replicase. Once this chance encounteroccurred, the replicase molecule could make a copy of itself by making acomplement of its complement (i.e., by transcribing the template), using thephysics of nucleotide base pairing.35
This requirement, of course, compounds the informational problem facing
this crucial step in the RNA-world scenario. Even if an RNA sequence could
acquire the replicase function by chance, it could perform that function only if
another RNA molecule—one with a highly specific sequence relative to theoriginal—arose close by. (See Fig. 14.6.) Thus, in addition to the specificity
required to give the first RNA molecule self-replicating capability, a second
RNA molecule with an extremely specific sequence—one with essentially thesame specificity as the original—would also have to arise. RNA-world
theorists do not explain the origin of the requisite specificity in either theoriginal molecule or its complement. Orgel and Joyce have calculated that tohave a reasonable chance of finding two such complementary RNA moleculesof a length sufficient to perform catalytic functions would require an RNA
library of some 1048 RNA molecules.36 The mass of such a library vastly
exceeds the mass of the earth, suggesting the extreme implausibility of thechance origin of a primitive replicator system. They no doubt vastly
underestimate the necessary size of this library and the actual improbability ofa self-replicating couplet of RNAs arising, because, as noted, they assume
that a 50-base RNA might be capable of self-replication. (See note for
qualifying details.)37
Given these odds, the chance origin of even a primitive self-replicating
system—one involving a pair of sequence-specific (i.e., information-rich)replicases—seems extremely implausible. And, yet, invoking natural
selection doesn’t reduce the odds or help explain the origin of the necessary
replicators since natural selection ensues only after self-replication has arisen.
As Orgel and Joyce explain, “Without evolution [i.e., prebiotic natural
selection] it appears unlikely that a self-replicating ribozyme could arise, but
without some form of self-replication there is no way to conduct anevolutionary search for the first primitive self-replicating ribozyme.”38
Robert Shapiro has resorted to one of my old standbys—Scrabble letters—to illustrate why neither chance, nor chance and natural selection
combined, can solve the sequencing problem in the RNA world. While
speaking in 2007 at a private conference on the origin of life, he asked an elitescientific audience to imagine an enormous pile of Scrabble letters. Then hesaid, “If you scooped into that heap [of letters], and you flung them on thelawn there, and the letters fell into a line which contained the words, ‘To be or
not to be, that is the question,’ that is roughly the odds of an RNA molecule,given no feedback [natural selection]—and there would be no feedback,because it [the RNA molecule] wouldn’t be functional until it attained acertain length and could copy itself—appearing on earth.”39
If neither chance, nor chance and selection, can solve the RNA
sequencing problem, can self-organization do the trick? It can’t. RNA bases,
like DNA bases, do not manifest bonding affinities that can explain their
specific arrangements. Thus, no one has even attempted to solve the RNA
sequencing problem by proposing a “self-organizational RNA world
scenario.” Instead, the same kind of evidentiary and theoretical problems
emerge whether one proposes that genetic information arose first in RNA or
DNA molecules. And every attempt to leapfrog the sequencing problem bystarting with supposedly “information-generating” RNA replicators has only
shifted the problem to the specific sequences that would be needed to makesuch replicators functional.
Once again the pink stuff was spreading.

Figure 14.6. The minimal requirements for template-directed RNA selfreplication as envisioned by Joyce and Orgel. They insist that any RNA

replicase would need to come into close proximity to an exact complementarystrand, thus increasing the needed sequence specificity associated with getting
such self-replication (and natural selection) started.
Problem 5: Ribozyme Engineering Does Not Simulate Undirected
Chemical Evolution
Because of the difficulties with the RNA-world hypothesis and the limited
number of enzymatic functions that naturally occurring ribozymes canperform, a new cottage industry has sprung up in molecular biology.
Scientists sympathetic to the RNA world have sought to design new RNA
molecules with heretofore unobserved functions. In doing so, these scientists
hoped not only to learn more about RNA chemistry, but also to demonstrate
the plausibility of the RNA-world hypothesis and possibly even to synthesize
an artificial form of life.40
These ribozyme-engineering experiments typically deploy one of two
approaches: the “rational design” approach or the “directed evolution”
approach. In both approaches, biologists try to generate either more efficientversions of existing ribozymes or altogether new ribozymes capable ofperforming some of the other functions of proteins. In the rational-design
approach, the chemists do this by directly modifying the sequences ofnaturally occurring RNA catalysts. In the directed-evolution (or “irrational
design”) approach, scientists seek to simulate a form of prebiotic natural
selection in the process of producing ribozymes with enhanced functional
capacities. To manage this they screen pools of RNA molecules usingchemical traps to isolate molecules that perform particular functions. Afterthey have selected these molecules out of the pool, they generate variant
versions of these molecules by randomly altering (mutating) some part of thesequence of the original molecule. Then they select the most functional
molecules in this new crop and repeat the process several times until a
discernible increase in the desired function has been produced.
Most ribozyme-engineering procedures have been performed on ligases,
ribozymes that can link together two RNA chains (oligomers) by forming asingle (phosphodiester) bond between them. Ribozyme engineers want todemonstrate that these ligases can be transformed into true polymerases or“replicases.” These polymerases would not only link nucleotides bases
together (by phosphodiester bonds), but also would stabilize the exposed
template strands, and use the exposed bases as a template to make sequencespecific copies.
Polymerases are the holy grail of ribozyme engineering. According to theRNA-world hypothesis, once a polymerase capable of template-directed self-replication arose, then natural selection could have become a factor in thesubsequent chemical evolution of life. Since ligases can perform one, though
only one, of the several functions performed by true polymerases, RNA-world
theorists have postulated ligases as the ancestral molecular species fromwhich the first self-replicating polymerase arose. They have tried todemonstrate the plausibility of this conjecture by using ribozyme engineering
to build polymerases (or replicases) from simpler ligase ribozymes.
To date, no one has succeeded in engineering a fully functional RNAbased RNA polymerase, from either a ligase or anything else.41 Ribozyme
engineers have, however, used directed evolution to enhance the function ofsome common types of ligases. As noted, they also have produced a moleculethat can copy a small portion of itself. Leading ribozyme engineers such as
Jack Szostak and David Bartel have presented these results as support for anundirected process of chemical evolution starting in an RNA world.42 Popularscientific publications and textbooks have often heralded these experiments asmodels for understanding the origin of life on earth and as the leading edge of
research establishing the possibility of evolving an artificial form of life in atest tube.
Yet these claims have an obvious flaw. Ribozyme engineers tend tooverlook the role that their own intelligence has played in enhancing thefunctional capacities of their RNA catalysts. The way the engineers use theirintelligence to assist the process of directed evolution would have no parallelin a prebiotic setting, at least one in which only undirected processes drove
chemical evolution forward. Yet this is the very setting that ribozyme
experiments are supposed to simulate.
RNA-world advocates envision ligases evolving via undirected processes
into RNA polymerases that can replicate themselves from free-standing bases,
thereby establishing the conditions for the beginning of natural selection. In
other words, these experiments attempt to simulate a transition that, according
to the RNA-world hypothesis, would have taken place before natural
selection had begun to operate. Yet in order to improve the function of theligase molecules, the experiments actually simulate what natural selection
does. Starting from a pool of random sequences, the investigators create achemical trap to isolate only those sequences that evince ligase function. Thenthey select those sequences for further evolution. Next they use a mutagenesis
technique to generate a set of variant versions of these original ligases. Thenthey isolate and select the best sequences—those manifesting evidence ofenhanced ligase function or indications of future polymerase function—andrepeat the process until some improvement in the desired function has beenrealized.
But what could have accomplished these tasks before the first replicatormolecule had evolved? Szostak and his colleagues do not say. They certainly
cannot say that natural selection played this role, since the origin of natural
selection as a process depends on the prior origin of the self-replicating
molecule that Szostak and his colleagues are working so hard to design.
Instead, in their experiment, Szostak and his colleagues play a role that naturecannot play until a self-replicating system, or at least a self-replicating
molecule, has arisen. Szostak and his colleagues function as the replicators.
They generate the crop of variant sequences. They make the choices about
which of these sequences will survive to undergo another round of directed
evolution. Moreover, they make these choices with the benefit of a foresightthat neither natural selection nor any other undirected or unintelligent process
can—by definition—possess.43 Indeed, the features of the RNA moleculesthat Szostak and his colleagues isolate and select are not features that would,
by themselves in a precellular context, confer any functional advantage.
Of course, ligase enzymes perform functions in the context of modern
cells and in that setting might confer a selectable advantage on the cells thatpossess them. But prior to the origin of the first self-replicating protocell,
ligase ribozymes would not have any functional advantage over any otherRNAs. At that stage in chemical evolution, no self-reproducing system yetexisted upon which any advantage could be conferred.
The ability to link (ligate) nucleotide chains is, at best, a necessary but nota sufficient condition of polymerase or replicase function. Absent a moleculeor, what is more likely, a system of molecules possessing all of the features
required for self-replication, nature would not favor any RNA molecule overany other. Natural selection as a process selects only functionallyadvantageous features and only in self-replicating systems. It passes its blindeye over molecules possessing merely necessary conditions or possible
indicators of future function. Moreover, “it” does nothing at all whenmechanisms for replication and selection do not yet even exist. In ribozymeengineering experiments, engineers perform a role in simulating natural
selection that undirected natural processes cannot play prior to thecommencement of natural selection. Thus, even if ribozyme experiments
succeed in significantly enhancing the capacities of RNA catalysts, it does notfollow they will have demonstrated the plausibility of an undirected processof chemical evolution. Insofar as ribozyme-engineering experiments using arational-design approach (as opposed to a directed-evolution approach)involve an even more overt role for intelligence, they exemplify the sameproblem (for example, see note 28).
Conclusion

As I have investigated various models that combined chance and necessity, I
have noted an increasing sense of futility and frustration arising among thescientists who work on the origin of life. As I surveyed the literature, it
became clear that this frustration had been building for many years. In 1980,
Francis Crick lamented, “An honest man, armed with all the knowledge
available to us now, could only state that in some sense, the origin of life
appears at the moment to be almost a miracle, so many are the conditions
which would have had to have been satisfied to get it going.”44 In 1988, theGerman biochemist and origin-of-life researcher Klaus Dose followed suitwith an equally critical assessment of the state of the field. Dose explainedthat research efforts to date had “led to a better perception of the immensity ofthe problem of the origin of life on earth rather than to its solution. At present,
all discussions on principle theories and experiments in the field either end ina stalemate or a confession of ignorance.”45 After attending a scientificconference on the origin of life in 1989, one of my Cambridge supervisors
returned to report, “The field is becoming increasingly populated with cranks.
Everyone knows everybody else’s theory doesn’t work, but no one is willingto admit it about his own.”
As I reviewed and evaluated the scientific literature over the ensuing
years, I found no reason to amend these assessments. It was demonstrably
more reasonable to reject the chance hypothesis than to accept it. Theories
relying on necessity awaited the discovery of an oxymoron, namely, “a law
capable of producing information”—a regularity that could generate specified
irregularity. Meanwhile, theories combining law and chance repeatedly
begged the question as to the origin of the information they sought to explain.
Theorists just spread pink stuff from one place to another, hoping in vain to
make it disappear.
Moreover, framing the possibilities differently didn’t change the situation.
Protein-first theories had failed, giving way to DNA-first theories. DNA-first
theories then failed, giving way to RNA-first theories. And now RNA-first
theories, like their predecessors, had failed to explain the central question of
the origin of the information that living cells require. As Orgel and Joyce
concluded in 1993, “The de novo appearance of oligonucleotides [i.e.,
specifically sequenced RNA bases] on the primitive earth” would have been“a near miracle.”46
But origin-of-life researchers did not give up. Instead, they had
increasingly taken matters into their own hands. As it became more difficult
to envision how life could have arisen from the molecular constituents ofactual living cells, some evolutionary biologists decided to try to simulate thechemical evolutionary processes on a computer. If explaining how
information had first arisen in DNA, RNA, and proteins had provenprohibitively difficult, then, perhaps scientists could at least program
computers to show how chance and necessity alone might have done the job
in some other hypothetical context. Meanwhile, RNA chemists, frustrated by
the embarrassing paucity of proteinlike functions associated with the new
miracle molecule, decided that it was time to do a little engineering of theirown—“ribozyme engineering” to be precise. Ironically, they sought todemonstrate the power of chance and necessity by intervening—by carefully
isolating, selecting, and amplifying those specific features of RNA that could
conceivably enhance its capacities.
But as I examined these new approaches,47 I found them no moreconvincing than those they were seeking to supplement. Even apart from theirlimited success, the very fact that these experiments required so muchintervention seemed significant. By involving “programming” and“engineering” in simulations of the origin of life, these new approaches had
introduced an elephant into the room that no one wanted to talk about,especially not in the methods sections of scientific papers. I began to reflect
on the failure of these simulations to explain the DNA enigma apart from theguidance of intelligent scientists. I wondered if they hadn’t inadvertently
provided evidence for a radically different approach to the problem of
biological information. This led me back to where I had started—to the ideaof intelligent design and to a consideration of the scientific case in its favor—
a case that now I knew I could make.

15
The Best Explanation

My family enjoys a detective show called Monk, featuring an obsessivecompulsive detective with almost preternatural powers of observation. Near
the end of most episodes, there comes a point where Mr. Monk announces, “I
just solved the case. Here’s what happened.” It’s the moment when, in a flashof insight, he at last sees how all the pieces fit together, how all the clues canbe explained. The various competing explanations fall away, inadequate to thetask, and the one true explanation leaps forth.
When I was studying in Cambridge, I was repeatedly struck by thesimilarity between the historical method of scientific investigation and themethod used by sleuthing detectives, both real and fictional. While learning
about how historical scientists use abductive reasoning, I even found a bookby the Italian scholar Gian Capretti, who used the fictional Sherlock Holmes
detective stories to illustrate how this form of reasoning works.1
Historical scientists and detectives alike must gather clues, study eachone, and then form tentative hypotheses. With detectives, suspects are
identified; with historical scientists, possible causes. Both sleuths andscientists must then weigh the competing possibilities, favored and otherwise,
judging their plausibility against a variety of clues and against their own
knowledge of how the world works.
Did the murder suspect have the power to strangle the victim?
Is the proposed cause known to be adequate?
Did the suspect have opportunity?
Is there evidence that the cause was in fact present?
Which murder scenario best explains the relevant facts?
Which hypothesis best explains the evidence?
Now, historical scientists, like criminal investigators, are acutely aware thatthey were not at the “scene of the crime.” They did not witness the event or its
cause. Instead, they must depend upon circumstantial evidence, the lingering
indicators of what happened: the clues, the traces, the signs left behind.
As I first set out to investigate the DNA enigma I was intrigued by anoutside-the-box hypothesis that scientists either had long since set aside or
now considered verboten. Though the concept of design played a seminal rolein the foundation of modern science, no one in late-twentieth-century science
still regarded intelligent design as a live option for explaining the origin oflife. Or so I had assumed. But my encounters with Charles Thaxton, Dean
Kenyon, and others introduced me to a body of evidence that seemed—atleast—to point toward this radical possibility.
From everyday experience and from what I would soon learn about
historical scientific reasoning, I knew that circumstantial evidence could beinconclusive. The same effect can sometimes be produced by more than one
cause and, consequently, can be explained in several different ways. As
philosopher Charles Sanders Peirce explained, abductive reasoning “often
inclines our judgment so slightly toward its conclusion that we cannot say that
we believe [the conclusion] to be true; we only surmise that it may be so.”2
To address this uncertainty and to prevent premature leaps to judgment,
historical scientists developed a method known as the “method of multiplecompeting hypotheses,” in which they seek to infer the best causal
explanation of the evidence in question. As noted in Chapter 7, this method
requires a thorough search for and evaluation of various possible causes,
leaving—in the best of cases—only one that can explain the relevant facts.
Historical scientists proceed in the same manner as the fictional Sherlock
Holmes, who, according to Capretti, uses a method of “progressively
eliminating hypotheses.”3
As I investigated the DNA enigma, I consciously followed this method ofreasoning. Specifically, I followed and tested my ideas in light of a version of
the method that philosopher of science Michael Scriven calls “retrospective
causal analysis.” Scriven’s method distills the key insights of Darwin and
Lyell about how to identify past causes and about when a specificallyhistorical scientific explanation counted as the “best.”
Though inferences about the past can be initially fraught with uncertainty,
there nevertheless does comes a time in many historical and forensic
investigations when the investigator realizes that one hypothesis clearly
explains the facts better than any other. And so it was in my case. Yet unlike
Mr. Monk, the television detective with a savantlike intellect, I had no single“ah-hah” moment where I suddenly said, “I know what happened.” Instead, as
I learned more about the DNA enigma, I had a series of insights: about thenature of information; about why the information in DNA transcends physics
and chemistry; about why it exceeds the reach of chance; about the real takehome message of various simulation experiments; and about the criteria thatmake historical explanations best. As I used the historical scientific method toevaluate the evidence and competing explanations in light of these criteria, Ieventually came to realize that the design hypothesis met each of them. One
by one the clues fell into place, the list of suspects dwindled, and the pieces fit
together.
Characterizing the Effect
In my research I learned that before historical scientists can evaluate
competing explanations of an effect, event, or body of evidence, they usuallyneed to define precisely what it is that needs to be explained.4 Indeed, part of
the historical scientific method of reasoning involves first characterizing what
philosophers of science call the explanandum—the entity in need of
explanation.5 It’s no different for origin-of-life researchers. They need toclearly define what it is they are trying to explain the “origin of.”6
For this reason, the cell and its contents provide perhaps the mostimportant clues about what happened to cause life to arise. Contemporary
biology has shown that cells are a repository of information, and that the
information they contain is essential for even minimal biological function.
Consequently, origin-of-life researchers have realized that if they were to
explain the origin of the first life, they would need to explain how the
information in DNA or some other alternative molecular precursor arose.
But what kind of information does DNA possess? This was an importantquestion to answer, because the term “information” denotes at least two
distinct concepts: Shannon information, on the one hand, and functionallyspecified information, on the other.7 As I discussed in Chapter 4, Shannon’s
equations allow us to compute the information-carrying capacity of asequence; they do not determine whether the sequence is meaningful orfunctional.
DNA and other biological molecules do have large and measurable
amounts information-carrying capacity. But they do not contain just Shannon
information; they contain functional information. In virtue of their specificarrangements, the bases in coding regions of DNA and RNA and the aminoacids in proteins enable these molecules to perform biological functions. Like
the information in machine code or written language, biological information
is not just complex; it is also functionally specified.8
Thus, to avoid confusion and equivocation, I realized that it wasnecessary to distinguish:“information content” from mere “information-carrying capacity,”“specified information” from mere “Shannon information,” and“specified complexity” from mere “complexity.”

The first term in each of these pairs refers to the functional kind of
information that DNA possesses. That was the kind of information I needed to
explain the “origin of.”
Assessing the Competing Possible Causes
Having defined the effect in question as specified biological information, I
was now in a position to consider the competing explanations of this featureof life to see which explanation, if any, was best. Peter Lipton’s seminal
Inference to the Best Explanation had shown me that the best explanation ofan event—whether in the past or in the present—was one that cited a causal
difference.9 I had discovered, further, that historical scientists understandably
preferred explanations that posited causes that were known to be capable ofproducing the effects in question over explanations that posited either nocauses or causes lacking such power. Both an earthquake and a bomb can
explain the destruction of a building, but only a bomb can explain thepresence of charring and shrapnel in the rubble at the scene. Thus, a forensic
scientist would likely conclude, in the absence of other evidence, that thebomb best explains the pattern of destruction at the building site. Entities,
conditions, or processes that have the capability (or causal powers) to produce
the evidence in question constitute better explanations of that evidence thanthose that do not.10
Determining which, among a set of competing explanations, constitutes
the best thus depends on knowledge of the causal powers of competing
explanatory entities.11 It is for this reason that the deliberations of historicalscientists, like those of good detectives, are inherently comparative. The
process of determining the best explanation in the historical sciences
necessarily involves generating a list of possible hypotheses, comparing theirknown (or theoretically plausible) causal powers with respect to the relevant
data, and then progressively eliminating potential but inadequate
explanations, leaving, in the more illuminating cases, only a single causallyadequate explanation. To infer the best explanation necessarily implies theneed to examine competing explanations. Moreover, as geologist Charles
Lyell explained, historical scientists make these judgments of comparative“causal adequacy” based upon their uniform and repeated experience of cause
and effect in the present. Historical scientists explain past events “byreference to causes now in operation.”12
The Causal Adequacy of Intelligent Design
As I noted at the close of Chapter 7, I determined that intelligent design

should be considered as, at least, a possible scientific explanation for theorigin of the functionally specified information present in the cell. By
intelligent design I mean “the deliberate choice of a conscious, intelligent
agent or person to affect a particular outcome, end, or objective.” Clearly,
intelligent agents, by their powers of choice, can make a causal difference inthe outcome of events. Moreover, intelligent design qualifies as a presently
acting cause—a “cause now in operation”—of the origin of specified
information—the “effect here under study.”
I know by introspection of my own mind—my intentions, perceptions,
cognitive powers, and deliberations—that I am a conscious agent. I know
from experience that I can—by my own deliberation and choice—produce
specified information. On the entirely reasonable assumption that otherpeople have conscious minds and that they too can make choices that affectthe material world, I continually see evidence that minds can generate
specified information. Programmers write code. Students ask questions. My
wife leaves me a message about whom to pick up on the way home fromwork. Intelligent agents produce, generate, and transmit information all thetime. Experience teaches this obvious truth. Indeed, uniform and repeatedexperience confirms that intelligent agents—conscious, rational beings suchas ourselves—can produce information-rich systems, including systems
containing digitally encoded, functionally specified information. Intelligent
design is “causally adequate” to produce this effect.
Since intelligent agency has “demonstrated its capacity to produce”
specified information, the “effect of the sort here under study,” I concluded
that intelligent design must be considered as—at least—a possible
explanation for the origin of biological information. But was it the best?
I knew that in order to establish a cause as the best explanation, thehistorical scientist must do more than establish that a proposed cause could
have produced the effect in question. He must also provide “evidence that his
candidate [cause] was present” and show via “a thorough search” that there isan “absence of evidence” of “other possible causes.” In other words, inaddition to meeting a “causal adequacy” condition, a best explanation mustalso meet a “causal existence” and/or “causal uniqueness” condition.
Recall from Chapter 7 that, in practice, meeting the third condition (causal
uniqueness) effectively ensures that the second condition (causal existence)
will also be met. For this reason, there is a simpler way to think of the
problem of historical reconstruction: to qualify as best a historical explanationmust cite a uniquely adequate cause—a cause that has alone demonstrated thecapacity to produce the evidence in question. In such a case—where there is
only one known cause of a given effect or body of evidence—the presence ofthe effect or evidence establishes the past action (and existence) of the cause.
The anthropologists who discovered the ancient cave paintings in Lascaux,France, knew of only one cause capable of producing representational art.
Consequently, they inferred the past activity and presence of intelligent
agents. Moreover, they could make this inference confidently without anyother evidence that intelligent agents had been present, because the presenceof the paintings alone established the probable presence of the only known
type of cause—intelligence—of such a thing. Could there be a similarly
strong basis for concluding that an intelligent cause played a role in the originof biological information?
To answer this question, I made a thorough search for and evaluation ofother possible causes in accord with the established canons of historicalscientific reasoning, a search recounted in the preceding chapters. As I
evaluated the causal adequacy of the other “suspects,” I became progressively
more intrigued with the design hypothesis. It eventually became clear to me
that intelligent design stood as the only known cause of specified informationrich systems and, therefore, that ID provides the best, most causally adequate
explanation for the origin of the information necessary to produce the firstlife. I came to this conclusion for three main reasons.
Reason 1: No Other Causally Adequate Explanations
Despite the “thorough search” described in Chapters 8–14, I found no othercausally adequate explanations for the DNA enigma. In my search, I
examined the main theories of the origin of life (and/or biologicalinformation) exemplifying each of three mutually exhaustive categories of
explanation: chance, necessity, and the combination of the two.13 Of course,
there are many specific theories that exemplify one or the other of these three
approaches. Yet my examination of the individual theories representing theseapproaches did not reveal any cause or process capable of producing
biologically relevant amounts of specified information. Moreover, the failureof these specific models to explain the origin of biological information often
seemed to reflect deeper conceptual or logical limitations.
For example, I discovered that self-organizational laws or processes ofnecessity cannot generate—as opposed to merely transmit—new information.
Laws of nature, by definition, describe highly regular patterns or order, notthe aperiodic complexity that characterizes information-rich digital code. Thissuggested to me that the problems with self-organizational models do notreside in just the details of a specific model, but instead stem from a deeper
conceptual incoherence in the basic approach itself. Theories based uponchance face a different, though possibly equally permanent, kind of obstacle.
These theories fail because of an inherent limitation in the probabilistic
resources of the universe itself.

Theories that combine chance and necessity invariably face a similar
dilemma. Since the lawlike processes of necessity do not generate new
information, these combination models invariably rely upon chance events to
do most, if not all, of the work of producing new information. This problem
arises repeatedly for models invoking prebiotic natural selection in
conjunction with random events, whether Oparin’s theories or various RNAworld scenarios. Since natural selection “selects” for functional advantage,
and since functional advantage ensues only after the result of a successful
random search for functional information, combination models invariably rely
upon chance rather than selection to produce new information. Yet these
theories face formidable probabilistic hurdles, just as pure chance–basedmodels do.
Nor do these failures seem to be a function of the particular chemical
substances involved in the initial stages of information storage ortransmission. Neither protein-first, nor DNA-first, nor RNA-first models
solve the problem of the origin of specified information. Even pre-RNAworld models that envision information arising in the specific arrangement ofsmaller molecules only create other insoluble conceptual dilemmas. Either the
proposed small-molecule repositories of information are too small to performbiologically significant functions because they lack the ability to form
complex folded structures, or scenarios involving small molecules require
highly specific arrangements of those molecules. In the former case, nobiologically relevant information or processes arise. In the latter case, thescenarios just transfer the information problem into the chemical soup itself.
As I surveyed the landscape of explanatory failures, a clear pattern
emerged. Every attempt to explain the origin of biological information either
failed because it transferred the problem elsewhere or “succeeded” only by
presupposing unexplained sources of information. This displacement problem
was particularly evident in computer simulations where positive results
depend so obviously on the input of information from intelligent
programmers that computer scientists themselves formulated various “no freelunch” theorems and laws of conservation of information, asserting that the
information outputs of computer simulations do not exceed (beyond certain
probabilistic limits) the informational inputs of the intelligent programmers
who designed them.
Rather than treating these explanatory failures as an invitation to still
greater flights of theoretical fancy, I began to consider the possibility thatnature was telling us something. Perhaps specified information does not arisefor free. Perhaps natural processes tend to degrade information, rather than
generate it. Messages written in the sand are eventually erased by the waves;
old newspapers yellow and eventually crumble without care from archivists;
static on the line inevitably interrupts the flow of conversation. Information-

rich sequences or systems may maintain their original fidelity over time, but
most will show an overall loss as the arrow of time progresses. Information
inputs typically exceed (or at best equal) information outputs, unless, ofcourse, intelligent agents have intervened. Ordinary experience confirms thisintuition.
Significantly, so did origin-of-life theories and simulations. Every major
origin-of-life scenario—whether based on chance, necessity, or the
combination—failed to explain the origin of specified information. Thus,
ironically, origin-of-life research itself confirms that undirected chemical
processes do not produce large amounts of specified information starting frompurely physical or chemical antecedents. For this reason, it seemed entirely
sensible to think that the conservation laws that computer scientists had
devised to describe the flow of information in computational domains applied
equally to the larger domain of nature itself. If so, it seemed plausible to thinkthat the informational repositories of life—such as the DNA molecule—werepointing to a source of information beyond the realm of physics andchemistry.
In any case, my long investigation had turned up nothing in the way ofmaterialistic processes with the demonstrated capacity—the proven causal
efficacy—to produce the large amounts of specified information necessary to
generate a self-replicating organism. Nor was I alone in this conclusion.
Leading scientists—Francis Crick, Fred Hoyle, Paul Davies, Freeman Dyson,
Eugene Wigner, Klaus Dose, Robert Shapiro, Dean Kenyon, Leslie Orgel,
Gerald Joyce, Hubert Yockey, even Stanley Miller—had all expressed
skepticism either about the merits of leading theories, the relevance ofprebiotic experiments, or both. Even Richard Dawkins, not known for
rhetorical restraint in support of evolutionary orthodoxy, candidly admitted in2008 that “no one knows” how life arose in the first place.14
Reason 2: Experimental Evidence Confirms Causal Adequacy of ID
If attempts to solve the information problem only relocated it, and if neither
chance, nor physical-chemical necessity, nor the two acting in combination
explains the ultimate origin of specified biological information, what does?
Do we know of any entity that has the causal powers to create large amountsof specified information?
As I thought about this question, I realized that there was a more positive
reason for considering intelligent design as the best explanation for the originof biological information. Early on, I recognized, based on ordinary
experience, that intelligent human agents—in virtue of their rationality andconsciousness—have demonstrated the power to produce specifiedinformation. But as I investigated the DNA enigma further, I unexpectedly
found experimental evidence in support of the causal power of intelligent
design.
This evidence did not come from weird attempts to detect the paranormal
or supernatural. Instead, the evidence came from experiments that pointed to anormal, at least to us, causal power—in particular, the power of our own
minds. These experiments inadvertently demonstrated not only the power ofmind over matter, but also the necessity of a mind to arrange matter into
structures relevant to life. I refer, of course, to the simulation experiments ofvarious kinds that scientists have performed for now over fifty years in an
attempt to demonstrate the plausibility of some favored origin-of-lifescenario. If these experiments were fables, they would have a moral: minds
can produce biologically relevant structures and forms of information, but
without mind or intelligence little, if any, information arises. Three separate
types of experimental results confirm this lesson.
Prebiotic Simulation Experiments
When Stanley Miller conducted his first experiment attempting to simulate
the production of amino acids on the early earth, he inaugurated a new formof scientific inquiry. Since then, for over fifty years prebiotic chemists haveattempted to simulate the evolution of biologically relevant building blocks—
amino acids, sugars, phosphates, and nucleotide bases—from simpler
chemicals. Nevertheless, after geochemical evidence established that the early
earth probably did not have the reducing atmosphere that Miller first
assumed, the relevance of his and many subsequent experiments came into
question. If the early earth did not have a reducing atmosphere, then clearlyexperiments starting with a mixture of reducing gases did not simulate
chemical processes at work on the early earth.
Though these simulation experiments failed to demonstrate theplausibility of chemical evolution under realistic prebiotic conditions, they
inadvertently demonstrated something else. Assume for the moment that the
reducing gases used by Stanley Miller did actually simulate the conditions on
the early earth. Would his experimental results establish the plausibility of anundirected process of chemical evolution? Not necessarily. Prebiotic
simulation experiments invariably generate biologically irrelevant substances
as well as desirable building blocks such as nucleotide bases, sugars, andamino acids. But without investigator intervention, these undesirable byproducts react with desirable building blocks to form inert compounds, suchas a tar called melanoidin, the curse of the prebiotic chemist.15 Simulation
experiments have repeatedly shown that such destructive chemical processes

would have predominated in any realistic prebiotic chemical environment.
To prevent such “interfering cross-reactions,” chemists must intervene
using various traps and other techniques to isolate and remove chemicals16
that alter desirable building blocks. For example, as I noted in Chapter 14, theformose reaction that produces ribose sugar also produces many other
undesirable chemical by-products that, absent the intervention of a chemist,
will react destructively with ribose.17
Investigators also artificially manipulate the initial conditions in theirexperiments. In so doing, they take into account information about—knowledge of—the properties of their reagents. For example, prebiotic
chemists typically choose to radiate their chemical mixtures with shortwavelength ultraviolet light because they know that longer-wavelength lightdegrades the amino acids they are trying to produce.18
In these and many other ways, investigators must routinely manipulate
chemical conditions both before and after performing “simulation”
experiments in order to protect them from destructive naturally occurring
processes. These manipulations constitute “profoundly informativeintervention[s].”19 Every choice the investigator makes to actualize one
condition and exclude another—to remove one by-product and not another—
imparts information into the system. Therefore, whatever “success” theseexperiments have achieved in producing biologically relevant compounds
occurs as a direct result of the activity of the experimentalist—a conscious,
intelligent, deliberative mind—performing the experiments.
Thus, these experiments not only fail to simulate an undirected process ofchemical evolution, they actually provide positive evidence for the powers ofa guiding hand; they simulate the power of, if not the need for, an intelligent
agent to overcome the influences of natural chemical processes—processes
that otherwise lead inexorably to biochemical dead ends. In prebiotic
simulation experiments, intelligent agents impart information into chemicalsystems to produce biologically relevant molecules. Therefore, theseexperiments actually provide positive evidence for the causal adequacy ofintelligent design.
Evolutionary Algorithms Demonstrate the Causal Adequacy of ID
Demonstrations of the causal adequacy of intelligent design have
inadvertently come from another type of simulation experiment: computerbased evolutionary algorithms. As discussed in Chapter 13, evolutionary
algorithms allegedly simulate the creative power of mutation and selection
and their ability to generate functional information “from scratch.”20
Nevertheless, some of these programs succeed by the illicit expedient of

providing the computer with an information-rich “target sequence” and thentreating relatively greater proximity to future function (i.e., the target
sequence), not actual present function, as a selection criterion. The more
recent Avida algorithm produces its results without such obviously goaldirected selection. Yet to the extent it succeeds in modeling a realisticallybiological process (which is itself questionable—see Chapter 13, n. 46), it too
relies on several sources of preexisting information—including an
information-rich instruction set—all of which came from intelligent computer
programmers.
In Chapter 13, I argued that none of these of evolutionary algorithms
demonstrated the ability of undirected chance and necessity to produce
specified information. But the failure of these programs also provides—
however much their creators might have intended otherwise—evidence forthe causal power of intelligent design.
By selecting for proximity to future function, the Dawkins, Küppers, andSchneider algorithms all utilized a goal-directed search. Yet such foresighted
selection has no analogue in nature. In biology, where differential survival
depends upon maintaining function, selection cannot occur until new
functional structures or sequences actually arise. Natural selection cannot
select a nonfunctional sequence or structure based upon the “knowledge” thatit may prove useful in the future pending additional alterations.
Nevertheless, what natural selection and mutation lack, intelligent
selection—purposive or goal-directed design—provides. Intelligent agents
have foresight. They can fix distant goals and arrange both matter andsymbols with those goals in mind. They can devise or select material means
to accomplish those ends from among an array of possibilities and thenactualize those goals in accord with a preconceived design plan or set offunctional requirements. They can also actualize intermediate structures andsystems in order to execute such plans. Moreover, they can do so without
respect to whether such intermediate forms maintain or perform functions
along the way. Thus, insofar as evolutionary algorithms set distant goals(target sequences), actualize them using information about them, and do so ina stepwise fashion that ignores considerations of intermediate function, they
simulate not the power of undirected selection and mutation but, instead, thepowers of mind.
Beyond that, of course, intelligent agents can produce information. And
since all evolutionary algorithms require preexisting sources of information
provided by designing minds, they show the power—if not the necessity—of
intelligent design for a second reason. Rational agents can constrain
combinatorial space with information-rich outcomes in mind. In the processof thought, functional objectives precede and constrain the selection of words,
sounds, and symbols to generate functional (or meaningful) sequences from

among a vast ensemble of meaningless alternative combinations of sounds orsymbols.21 In so doing, minds produce information.
Neither computers by themselves nor the processes of selection andmutation that computer algorithms simulate can produce large amounts ofnovel information, at least not unless a large initial complement of
information is provided. Even Avida shows this. To produce a self-replicating
digital organism capable of natural selection and mutation, the Avida
programmers first provided a large digital genome with individual “genes”
specifically arranged to ensure the capacity to self-replicate. This
information-rich initial condition corresponds to what living organisms mustpossess to make natural selection possible. Natural selection depends upon thecapacity of the organism to replicate a system of different molecules, and thiscapacity, in turn, derives from preexisting sources of specified information.
Thus, not only do evolutionary algorithms fail to simulate how undirected
mutation and selection produce the information necessary to the first life; they
actually simulate the opposite. Indeed, computer simulations of the origin of
biological information expose limitations in the causal powers of natural
selection and mutation that correspond precisely to powers that intelligent
agents are known—uniquely—to possess. The causal powers that natural
selection lacks—foresight and creativity—are attributes of consciousness andrationality, of purposive intelligence. Where computer simulations depend onthese powers—as they surely do to generate functional information andoutcomes—they are provided by the knowledge and intelligence of the
programmer, not the computer. Thus, like prebiotic simulation experiments,
evolutionary algorithms demonstrate the causal power of intelligent design.
Ribozyme Engineering Demonstrates the Causal Adequacy of ID
Conscious intelligence plays the same essential role in ribozyme engineering.
Recall that ribozyme engineers attempt to enhance the capacity of RNA
catalysts in order to demonstrate the plausibility of the RNA world. In
particular, ribozyme engineers want to show that linking enzymes called RNA
ligases can acquire true polymerase function, making possible templatedirected self-replication.
Yet, as I noted in the previous chapter, ribozyme engineers using an“irrational-design approach” encounter a crucial lacuna that they must use
their intelligence to bridge. The irrational-design approach seeks to model aform of prebiotic natural selection to enhance the function of the ligases.
Incremental improvements in, or slight additions to, the function of these
enzymes are preserved, replicated, amplified, and then selected for further
mutation and selection in hopes of eventually producing a polymerase capableof template-directed self-replication. Yet before the emergence of true
polymerases, nothing in nature would perform these critical steps(preservation, replication, amplification), even poorly. Absent an enzyme
capable of true self-replication, natural selection is not yet a factor.
So what supplies this gap in ribozyme engineering experiments? Whatcauses a molecule possessing merely possible indicators of a future selectablefunction to be preserved? The investigators themselves—the ribozyme
engineers. The ribozyme engineers have the foresight to see that ligase
capacity, in conjunction with the other capacities of true polymerases, mightenable self-replication to proceed. So they select molecules with slightly
enhanced ligase capacity. Then they preserve and optimize these molecules.
They “enrich by repeated selection and amplification” as one paper puts it.22
Moreover, they intervene in this way before any of the other functions thattrue polymerases perform are fully present. Thus, the investigators anticipate
a future function not yet present in the emerging ligase itself. They choose
RNA sequences informed by knowledge of the conditions required toactualize that future function of template-directed self-replication. Since
nature lacks such foresight, the ribozyme engineer supplies what nature doesnot. The engineer acts as both replicator and selector—though no molecule
capable of acting as a replicator would have yet existed in the early stages of
the RNA world.
In the most successful ribozyme engineering experiments, theinvestigators help their ligases along in other ways. In nature, polymerases
have the capacity to unwind double-stranded DNA molecules before copying
them. Ligases cannot do this. So ribozyme engineers provide only singlestranded RNA molecules to the ribozyme so that they can catalyze the ligationof two such strands. The investigators also provide purified reagents, remove
chemical substances to prevent unwanted cross-reactions, and stabilize andposition the molecules upon which the ribozymes must act.
Each of these manipulations again constitutes an “informativeintervention,”23 since at every crucial stage ribozyme engineers select some
options or possible states and exclude others. By using their knowledge of therequirements of polymerase function to guide their search and selection
process, ribozyme engineers also impart what Robert Marks calls “active
information” with each iteration of replication. Thus, ribozyme-engineeringexperiments demonstrate the power—if not, again, the need for—intelligenceto produce information—in this case, the information necessary to enhance
the function of RNA enzymes.
Intelligence plays even more obvious roles in ribozyme experiments
exemplifying the rational-design approach. In one such experiment in 2002,24
investigators claimed to have produced a self-replicating RNA molecule,though upon close inspection, not an actual RNA polymerase. Instead, using
the familiar mechanism of complementary base pairing, the researchers foundthat they could get a ribozyme ligase to close the gap between two singlestranded pieces of RNA once the strands had bonded to the longer
complementary RNA strand provided by the ribozyme. Yet to get the
ribozyme to copy itself, even in this rather trivial sense, the scientists
themselves had to provide the two complementary sequence-specific strandsof RNA. In other words, the scientists themselves solved the specifiedinformation problem by sequencing two RNA strands to match thecomplementary sites on a longer piece of RNA.25
Certainly, the familiar mechanism of hydrogen bonding ensured that thestrands would bind to the correct section on their complements, at least if they
didn’t fold up on themselves or bind to other molecules first. But the specificsequence—the information—that allowed this bonding to occur was provided
by intelligent agents. In other words, to generate even this trivial form of selfreplication (in which a single molecule, not a system of different kinds ofmolecules, makes a complement of itself), intelligent agents had to provide
the critical sequence-specific information. Thus, ribozyme engineering—whether exemplifying “irrational” or “rational” design procedures—also
demonstrates the causal adequacy of intelligent design.
The Quiet Cause
Of course I am belaboring the argument for the causal powers of intelligent
agency. But I do so to underscore a point that is too often overlooked:
evidence for the causal adequacy of intelligence is all around us both insideand outside the lab. Clearly, we all know that intelligent agents can create
specified information and that information comes from minds. A computer
user who traces the information on a screen back to its source invariably
comes to a mind, that of a software engineer or programmer. The information
in a book or newspaper column or an ancient inscription ultimately derives
from a writer or scribe—from a mental, rather than a strictly material cause.
The case for the causal adequacy of intelligent design should be obvious fromour ordinary experience. But for those of us trained in the natural sciences,appeals to the mental realm sound perilously vague, immeasurable, andunscientific. We reflexively discount knowledge about what minds can doderived from introspection and ordinary experience and we instead credit only
what we have learned through experimental studies.
But dismissing evidence from common experience can be a mistake.
Children learn that fire burns without establishing rigorous experimental
controls. Archaeologists and forensic scientists can identify the evidence offire in the past without having first isolated every variable in a separate
experiment to establish that fire chars. Common experience often counts. If
the best explanations cite causes that are known to produce the effect inquestion, and if conscious intelligent agency is known by experience to cause
particular effects, then agency should qualify as a causally adequate andtherefore possible explanation of such effects. Logically, it should not matter
whether we learn about the true cause-and-effect structure of the world fromexperimental or other types of observations. Nevertheless, given modern
scientific sensibilities, experimental results will always seem weightier. And
so it seemed to me not only significant, but ironic, that a careful analysis ofprebiotic simulation experiments of various kinds invariably revealed that the
choices of intelligent agents played an indispensable role in whatever successthose experiments had achieved.
As Robert Marks has shown, scientists can now even measure the effectthat intelligence produces in these experiments. Recall that Marks himself
quantified the amount of active information that a computer program imparts
into a system with each iteration as the result of the knowledge provided to it
by the programmer. Clearly, ribozyme engineering and prebiotic simulations
were also making use of informational inputs (Marks’s “active information”)
as the experimenters made choices about which molecules to preserve anddiscard based upon their own knowledge of desired outcomes.
In sum, the case for the causal adequacy of intelligent agency no longer
depends solely on our ordinary experience of agents producing information insoftware codes or by using natural languages. Experiments attempting tosynthesize biologically relevant substances and information-rich molecules
have now established the power—and arguably the need—for intelligent
design. The fact that the experimenters were striving mightily to establish the
opposite point makes the demonstration all the more noteworthy since any
experimental bias would run in the opposite direction.
Reason 3: ID Is the Only Known Cause of Specified Information
The inability of genetic algorithms, ribozyme engineering, and prebiotic
simulations to generate information without intelligence reinforced what I had
discovered in my study of other origin-of-life theories.26 Undirectedmaterialistic causes have not demonstrated the capacity to generate significant
amounts of specified information. At the same time, conscious intelligencehas repeatedly shown itself capable of producing such information. It follows
that mind—conscious, rational intelligent agency—what philosophers call“agent causation,” now stands as the only cause known to be capable ofgenerating large amounts27 of specified information starting from a nonlivingstate.
But what reason do we have to suppose that an intelligent agent wasaround for the origin of life? More specifically, is there any way that
historical scientific reasoning would lead to such a conclusion. As we saw in
Chapter 7, historical scientists must establish not only that a given cause canproduce the effect in question, but that the cause was actually present in orderto do so.28 A contemporary detective might have eyewitnesses or a securitycamera video to establish the presence of a potential suspect (a proposed
cause). Historical scientists, of course, must depend on other means ofestablishing the presence of a cause. In Chapter 7, I described two ways that
historical scientists have of meeting this causal-existence criterion.
Of the two, the most direct involves making a thorough evaluation of thepossible causal explanations and showing that only one of the competing
causes has demonstrated the power to produce the main or salient effect inquestion. In that case, the historical scientist can infer the presence of thecause in the past.29 The presence of volcanic ash in the sedimentary record
establishes the past presence (and existence) of prior volcanic activity because
volcanoes, and only volcanoes, are known to produce such ash. When athorough study of various possible causes turns up only a single adequate
cause for a given effect, the candidate cause automatically meets the causalexistence criterion.
Consider two other examples of this form of reasoning, one drawn fromforensic science and one from a historical science, planetary geology. Several
years ago one of the forensic pathologists from the original Warren
Commission, which investigated the assassination of President Kennedy,
spoke out to quash rumors about a second gunman firing from “the grassyknoll” in front of the motorcade. The bullet hole in the back of President
Kennedy’s skull evidenced a distinctive beveling pattern that clearly indicatedthat the bullet had entered his skull from the rear. The pathologist called thebeveling pattern a “distinctive diagnostic,” because the pattern indicated asingle possible direction of entry. Since a rear entry was the only known causeof the beveling pattern, the pattern allowed the forensic pathologists todiagnose the trajectory of the bullet.30
Here’s another example of reasoning from an effect to single known
cause. The Martian landscape displays evidence of erosion—trenches and rills—that resemble those produced on earth by moving water. Though Mars
currently has no significant liquid water on its surface, planetary scientists
have nevertheless inferred that Mars once had a significant amount of water
on its surface in the past. Why? Geologists and planetologists have notobserved any cause other than moving water that can produce the kind oferosional features observed on Mars today.

One could fill a bookshelf with such examples, because the inferential
procedure of the historical sciences is reasonable and makes intuitive sense:
when a thorough search reveals only one type of cause with the power toproduce a given effect, investigators can infer that uniquely adequate causefrom the effect in question. Logically, one can infer the past existence of acause from its effect, when the cause is known to be necessary to produce theeffect in question. If there are no other known causes—if there is only one
known cause—of a given effect, then the presence of the effect points
unambiguously back to the (uniquely adequate) cause.
For this reason, the specified information in the cell establishes theexistence and past action of intelligent activity in the origin of life.
Experience shows that large amounts of specified complexity or information(especially codes and languages) invariably originate from an intelligent
source—from a mind or a personal agent.31 Since intelligence is the only
known cause of specified information (at least starting from a nonbiologicalsource), the presence of specified information-rich sequences in even thesimplest living systems points definitely to the past existence and activity of adesigning intelligence.32
The calculus underlying this inference follows the logically valid methodof inference used in the historical and forensic sciences. Since both common
experience and experimental evidence affirms intelligent design as anecessary condition (and cause) of information, one can detect (or retrodict)the past action of an intelligence from an information-rich effect—even if thecause itself cannot be directly observed.33 A pattern of flowers spelling“Welcome to Disneyland” allows visitors to the theme park to detect
intelligent activity, even if they did not see the flowers planted or arranged.
Similarly, the specified and complex arrangement of nucleotide sequences—
the information—in DNA implies the past action and existence of an
intelligent cause, even if the past action of the cause cannot be directly
observed.
Scientists in many fields recognize the connection between intelligenceand information and make inferences accordingly. Archaeologists assume thata scribe produced the inscriptions on the Rosetta Stone. Evolutionary
anthropologists establish the intelligence of early hominids from chipped
flints that are too improbably specified in form and function to have been
produced by natural causes. NASA’s search for extraterrestrial intelligence(SETI)34 presupposes that any specified information imbedded inelectromagnetic signals coming from space would indicate an intelligent
source.35 As yet, radio astronomers have not found any such informationbearing signals. But closer to home, molecular biologists have identified
information-rich sequences and systems in the cell, suggesting, by the same

logic, the past existence of an intelligent cause for those effects.
The Cell’s Information-Processing System, Revisited
In Chapters 3, 4 and 5, I described the main features of the cell that had to be
explained by any theory of the origin of life. In Chapter 4 and throughout thebook, I’ve especially stressed the importance of explaining the origin of thefunctionally specified information in DNA, RNA, and proteins. But in
Chapter 5, I also described another problem facing origin-of-life researchers:
the problem of explaining the origin of the functionally integrated
information-processing system in the cell.
Recall, for example, that specified information in DNA codes for proteins,
but specific proteins are necessary to transcribe and translate the informationon the DNA molecule. The proteins in the translation and transcription
systems even help to process the genetic information for building other copiesof themselves. Proteins are needed for protein synthesis. ATP is needed for
ATP synthesis. DNA is needed for ATP synthesis. ATP is needed for DNA
synthesis. Figure 5.9 depicted many of these interdependent functional
relationships in the cell’s information processing system.
In Chapter 14, I described how the RNA world hypothesis had beendevised in part to explain just one of the “chicken and egg” problems that
confront origin-of-life researchers as a result of such interdependent systems.
Since DNA is needed to make proteins and proteins are needed to process theinformation in DNA, perhaps RNA came first, functioning as both an enzyme
catalyst and a nucleic acid. Given its many difficulties, however, it now seems
clear that the RNA world has explained neither this nor any other functionallyinterdependent relationship in the cell’s information-processing system. In
addition to failing to explain the origin of the specific-sequencing necessary
to establish self-replication, the RNA world has failed to explain the origin of
the translation system and the genetic code, key elements in the cell’s overall
informational system. Similarly, self-organizational models have not only
failed to account for the origin of the genetic text, but also for the associations
between nucleotide triplets and their cognate amino acids that are the central
feature of the genetic code and, again, an indispensable part of information
processing in living systems. Further, as noted in Chapters 9 and 10, theprobability of building a self-reproducing system of molecules by chancealone is vastly lower than the already prohibitively small probabilities
associated with the chance origin of even a single modest-length protein.
Clearly, chance will not suffice to explain the origin of informationprocessing capability either.
But if neither chance nor necessity (self-organizational forces ofattraction) nor the combination of the two (as invoked in the RNA world) can
explain the origin of the cell’s information-processing system, what does? Is
there any other entity, or type of cause, that is known to produce the kind offunctionally integrated systems that we see in the cell? Is any other kind ofentity known to produce functionally integrated information-processing
systems?
Of course there is.
In the early 1990s, I first saw a schematic of the cell’s informationprocessing system that I later simplified for presentation as Figure 5.9. WhenI saw it, a nickel dropped. It reminded me of a diagram that a systems
engineer had placed on my desk years earlier when I worked for the oilcompany. That diagram was a flow chart choreographing how seismic data
(information about the earth’s subsurface) needed to be collected, processed,
analyzed, and interpreted. At each major step along the way in thisinformation-processing system, there were checkpoints, junctions, andfeedback loops determining how information was to be routed and passed
back and forth between one party (or computer) and another and what
functions were to be performed on it. At the time I called it “a spaghetti
diagram” because of all the lines going back and forth between the different
boxes, each line representing a functional dependence or interdependence
between the different functions, players, and/or computers in the system as awhole.
Of course, electrical wiring diagrams and schematics of computer
hardware and software provide even better examples of functionallyintegrated systems. A typical electronic system has multiple uniquecomponents as well as multipart gates, filters, converters, digital signal
processors, power supplies, switches, and/or functional “blocks.” In a typicalelectronic system, many of these components or subsystems will depend uponothers (directly or indirectly) for their proper function. Electronic systems andcircuitry are often characterized by what engineers call “many to one,” “oneto many” and/or “closed loop” functional interdependencies in which one partof a system is needed by many others and the reverse. Oddly, we find thesame kind of integrated functional relationships and coordination inside thecell, especially within its information-processing network.
So why does this matter? Again, it matters because—due to thematerialistic sensibilities of our intellectual culture—we often inadvertently
overlook obvious sources of knowledge about the cause-and-effect structureof the world. We know from uniform and repeated experience of a kind of
cause that is capable of producing functionally integrated multipart systems.
Engineers—by virtue of their powers of conscious deliberation and rationality—can produce systems that exemplify multipart functional integration. We
also know that intelligent agents can produce complex functionally integratedsystems specifically for processing information, whether that information isstored and transmitted in audio or digital form or processed by computers orother forms of electronic circuitry. We also know of no other type of causethat has these capacities. Intelligence is the only known cause of complex
functionally integrated information-processing systems. It follows, once
again, that intelligent design stands as the best—most causally adequate—
explanation for this feature of the cell, just as it stands as the best explanation
for the origin of the information present in DNA itself.
Conclusion
Since the intelligent-design hypothesis meets both the causal-adequacy andcausal-existence criteria of a best explanation, and since no other competing
explanation meets these conditions as well—or at all—it follows that thedesign hypothesis provides the best, most causally adequate explanation of
the origin of the information necessary to produce the first life on earth.
Indeed, our uniform experience affirms that specified information—whether
inscribed in hieroglyphics, written in a book, encoded in a radio signal, or
produced in a simulation experiment—always arises from an intelligent
source, from a mind and not a strictly material process. So the discovery of
the specified digital information in the DNA molecule provides strong
grounds for inferring that intelligence played a role in the origin of DNA.
Indeed, whenever we find specified information and we know the causal storyof how that information arose, we always find that it arose from an intelligent
source. It follows that the best, most causally adequate explanation for theorigin of the specified, digitally encoded information in DNA is that it too had
an intelligent source. Intelligent design best explains the DNA enigma.
By the late 1990s, I had become convinced—at least provisionally—that
intelligent design was the only known cause of specified information. As aresult, I began to sketch out the case for intelligent design as the best
explanation for the DNA enigma. In the years that followed (1998–2003), I
published a series of articles arguing that intelligent design provides a better
explanation than any competing chemical evolutionary model for the origin of
biological information.36 Since then I have continued to examine additional
hypotheses and simulations such as the RNA world and genetic algorithms.
The case for intelligent design has grown only stronger. Not only have these
new approaches failed to provide an adequate explanation for the origin of
biological information; they have strengthened the positive case for designthat I had previously formulated.
Though advocates of intelligent design have been labeled by some of theiropponents as creationists (even “creationists in cheap tuxedos”!), the case forintelligent design depends, ironically, upon a form of scientific reasoning—
namely, uniformitarian reasoning—that creationists have often bitterly
opposed. Indeed, the case for intelligent design depends on the uniformitarian
method of scientific reasoning that Darwin himself used in formulating hisargument in On the Origin of Species. In light of this and the evidence
considered in the preceding chapters, I eventually answered the question that
had first seized my attention back in 1985. I concluded that a rigorous
scientific argument for intelligent design could be formulated. This chapter
has described exactly how I came to that conclusion and why I think it best.

16
Another Road to RomeWhen I was teaching I used to use a combination lock to illustrate something
about how we detect intelligent causes, though I did not initially tell mystudents the point I was trying to make. Instead, I began my illustration byannouncing to the class that I wanted to show them that chance alone is not aplausible way of generating specified information and, therefore, that chance
is not a good explanation for the origin of the information present in DNA.
In the demonstration I asked students to try to open a lock by guessing the
combination. I even told them that they needed to turn the dial on the lockfirst to the right, then to the left, and then back again to the right past thesecond number. As I passed the lock around the class, and as student after
student failed to find the combination in three random trials, I acted
increasingly smug as the demonstration, was apparently, proving my point.
Then, as if on cue and just as I was becoming insufferable, a student (say“John”) nonchalantly turned the dial three times—right, left, right—and
popped the lock open.
The class reacted predictably with laughter and taunting—at least for awhile. I feigned shock at the outcome of my demonstration. I had been provenwrong. A random search through a space of possible combinations had
produced specified information and had done so rather quickly. Or had it?
Invariably, as students had a chance to think about what they had witnessed,
someone asked whether the student who opened the lock had really guessed
the combination by chance.
Then the accusations started. “Was that for real?” “Was he a plant?” “Are
you trying to trick us?”
“Who, me?” I replied. “Why would I do something like that?” As more
and more students expressed skepticism, I asked why they suspected me.
“After all, even though it was improbable that John would guess the
combination, he still could have. There was a chance,” I said, protesting my
ignorance.
“I understand that,” one student said, “but it still seems much more likelythat he knew the combination already.” A consensus would form as otherstudents began to suspect the same thing.
Eventually, I walked over to the student who had opened the lock andasked him to tell the truth. “Did I tell you the combination before classstarted?” The student stood up, smiled, and then began to fish a small slip ofpaper out of his pocket. The class erupted in more laughter as he held up the
combination for everyone to see.
After order was restored, I explained the real point of the demonstration.
“As you thought about what you saw,” I said, “most of you began to suspectsomething fishy. You rejected the chance hypothesis and, instead, began tosuspect that intelligent design had played a role. You suspected that John wasable to find the combination only because he already knew it. You suspectedthat he used information to generate information. But beyond that, you also
suspected that he and I had colluded in order to fool you in some way—though for what purpose you may not yet know.”“In other words, you inferred intelligent design—both on John’s part inusing his knowledge to open the lock and on my part in putting him up to it.
Even though I, your eminently trustworthy professor, had assured you thateach participant was guessing the combination at random, you still detected
intelligent design. Why?”
Of course, by this time my students already knew my answer to thisquestion. They knew that I would say that we infer intelligent design as thebest explanation for the origin of specified information, because intelligent
activity is the only known cause of it. But I wanted them to understand that
there was another way to reach the same conclusion. Specifically, I wanted to
introduce my students to the ideas of Bill Dembski, who had recently
developed a formal theory of design detection, one that my antics with the
combination lock and their own thought processes had—unbeknownst tothem—just illustrated. But for them to understand this, they needed to know
something of Dembski’s theory.
Dembski’s Method of Design Detection
In Chapter 8, I described how Dembski’s work on chance elimination
supplied me with an analytical framework for evaluating chance hypotheses
about the origin of biological information. In the years after we first met,
Dembski went on to develop a scientific method for detecting designed events—events that not only defied explanation by chance, but that could also
properly be attributed to intelligent activity. That is, he developed a formal
scientific method for detecting intelligent causes in the echo of their effects.
The case that I have developed for intelligent design as the best explanation
for the origin of biological information does not depend upon the use of
Dembski’s method of design detection. Nevertheless, his method does apply
to the analysis of biological information, and, when applied rigorously, itreinforces my conclusion. Dembski’s road also leads to Rome.
In The Design Inference, his groundbreaking book on design detection,
Dembski notes that rational agents often infer or detect the prior activity ofother minds by the character of the effects they leave behind.1 (This aspect of
his work builds on, but is distinct from, his work on probability, which I
discussed and applied in Chapters 8–10.) Archaeologists infer, for example,
that rational agents produced the inscriptions on the Rosetta Stone; insurance
fraud investigators detect certain “cheating patterns” that suggest intentional
manipulation of circumstances rather than natural disasters; cryptographers
distinguish between random signals and those that carry intelligently encoded
messages. Dembski argues that recognizing the activity of intelligent agents
constitutes a common and fully rational mode of inference.2 “We do it all thetime,” he used to say to me. In this chapter, I explain Dembski’s theory of
how we infer design, how it applies to DNA, and show why it reinforces theargument for intelligent design made in the preceding chapters.
The Indicators of Intelligent Design
The Design Inference and subsequent works identify two indicators ofintelligent activity by which rational agents recognize the effects of otherrational agents and distinguish those effects from the effects of purely
undirected material causes. Dembski notes that we invariably attribute events,
systems, or sequences that have the joint properties of “complexity” (or small
probability) and “specification” to intelligent causes—to design—not tochance or physical-chemical necessity.3 Complex events or sequences of
events are extremely improbable and exhibit an irregular arrangement that
defies description by a simple rule, law, or algorithm. A specification is amatch or correspondence between an observed event and a pattern or set offunctional requirements that we know independently of the event in question.
Events or objects are “specified” if they exhibit a pattern that matches anotherpattern that we know independently. I will return to this idea below.
Though Dembski developed his theory using a specialized terminology
and precise mathematical formalism, his theory is actually highly intuitiveand can be illustrated with many familiar examples of design detection—
including the example I described at the beginning of this chapter. As I
explained to my students, their suspicions of design were aroused when they
perceived an event that exhibited Dembski’s twin requirements of small
probability and specification.
After my student “plant” popped open the lock, the other students
recognized this as an improbable event. Given that the dial had 40 settings,
the probability of his generating the correct combination—R26, L28, R14—

by chance in three spins of the dial was just one in 64,000.
Though that is not very improbable compared to the probability of
producing a protein by chance, it was improbable in relation to the handful ofopportunities that I gave my students to find the correct combination.
But beyond that, my students recognized a functional significance in theevent—one that other equally improbable spins of the dial did not display.
When John (my plant) turned the dial three ways to pop the lock open, theother students realized that the event matched a set of independent functional
requirements—the requirements for opening the lock that were set when its
tumblers were configured. My students also realized that the sequence of
numbers that John had generated matched the combination. My students
perceived an improbable event that matched an independent pattern and met a
set of independent functional requirements. Thus, for two reasons, the event
manifested a specification as defined above. Since the event was also
improbable, my students correctly suspected intelligent design, just as
Dembski’s theory implies they should have.
Some other homespun examples of design detection illustrate Dembski’s
criteria and method. Visitors to Mt. Rushmore in South Dakota infer the pastaction of intelligent design upon seeing some unusual shapes etched in therock face. Why? The shapes on the hillside are certainly unusual andirregularly shaped, and thus, in this context, improbable. But beyond that,observers recognize a pattern in the shapes that they know from an
independent realm of experience, from seeing the faces of ex-presidents inphotographs or paintings. The patterns on the mountain match patterns theobservers know from elsewhere. Thus, according to Dembski’s theory, thefaces on Mt. Rushmore exhibit a specification (or are specified). Since theseparticular shapes are also complex, the faces on Mt. Rushmore point,
according to his theory, to intelligent design.
Or consider another example. A few years ago, I entered Victoria Harborin Canada from the sea and noticed a hillside awash in red and yellow
flowers. As I got closer, I naturally and correctly detected intelligent design.
Why? I recognized a pattern, an arrangement of flowers spelling “Welcome toVictoria.” I thus inferred the past activity of an intelligent cause—the carefulplanning of gardeners.
According to Dembski’s theory, two things triggered and justified myrecognition of intelligent design in this case. First, the flowers on the hillside
appeared to have been arranged in an exceedingly improbable way. The
arrangement of flowers did not, for example, exhibit a simple repetitive
pattern that might have revealed it to be the expected or likely expression ofan underlying natural process. Further, given all the different ways that thesame flowers could have been arranged on the hillside, the specificarrangement represented a highly improbable configuration.

In addition to the improbability of the arrangement, I also recognized apattern, namely the shapes of English letters, and functional significance in it.
Based upon my prior knowledge of English, I realized that the letters had
been arranged to meet the independent requirements of English grammar inorder to perform a communication function. Had the flowers been morehaphazardly scattered so as to defy pattern recognition, I might justifiably
have attributed the arrangement to chance—for example, to random gusts ofwind having previously scattered the seed. Had the pattern of colors been lesscomplex, with, for example, the red and yellow flowers being segregated byelevation, the pattern might have been explained by some natural necessity,
such as certain types of soils or environments favoring certain types of plants.
But since the arrangement was highly improbable and since it also conformed
to an independent pattern (it exhibited a “specification”), I inferred intelligent
design.
This judgment comports with Dembski’s theory. If an object or event isboth complex and specified, then we should attribute it to intelligent design.
By contrast, Dembski notes that we typically attribute to chance low-orintermediate-probability events that do not exhibit discernable patterns. We
typically attribute to necessity highly probable events that recur repeatedly ina regular or lawlike way.
Dembski notes that, as we reason about these different kinds of events, we
often engage in a comparative evaluation process that he represents with aschematic he calls the “explanatory filter.”4 The filter outlines a method bywhich scientists (and others) decide among three different types of
attributions or explanations—chance, necessity, and intelligent design—based
upon the probabilistic features or “signatures” of various kinds of events. (SeeFig. 16.1.) His “explanatory filter” constitutes, in effect, a scientific methodfor detecting the effects of intelligence. The explanatory filter works, heargues, because it reflects our knowledge of the way the world works. Since
experience teaches that complex and specified events or systems invariably
arise from intelligent causes, we can infer intelligent design from events thatexhibit the joint properties of complexity and specification.
From Chance Elimination to Intelligent Design
Dembski’s ideas about design detection developed out of his work on chanceelimination. In Chapter 8, I described how Dembski came to recognize that
low-probability events by themselves do not necessarily indicate thatsomething other than chance is at work. Improbable events happen all thetime and don’t necessarily indicate anything other than chance in play—asDembski had illustrated to me by pointing out that if I flipped a coin one

hundred times I would necessarily participate in an extremely improbable
event. In that case, any specific sequence that turned up would have aprobability of 1 chance in 2100. Yet the improbability of this event did notmean that something other than chance was responsible. To eliminate chance,
the observer of the event would need to discern a pattern, such as the cointurning up heads every time.
I made this same point in Chapter 8 with my illustration about “Slick” at
the roulette wheel. If Slick spins the roulette wheel 100 times, he necessarily
generates a series of outcomes that are highly improbable. Only if those spins
also induced recognition of a pattern—such as the ball landing in the same
pocket every time or Slick winning repeatedly—would an observer correctly
suspect something other than chance at work.

Figure 16.1. Dembski’s Original Explanatory Filter. Dembski’s filter provides
a rational reconstruction of common inferential processes. It suggests that we
commonly attribute high-probability events to natural laws (necessity); that
we attribute unspecified events of intermediate or low probability to chance;
and that we correctly attribute specified events of small probability (complex
specified information) to intelligent design. Dembski has since noted that toinfer design as the best explanation investigators sometimes must consider
explanations that combine chance and law (necessity), as shown in Chapters
13 and 14.
After Dembski recognized that pattern recognition played a key role inchance elimination, he began to suspect that it also played a key role in thedetection of intelligent activity. Though not all of the patterns that helped tojustify the elimination of chance also pointed to intelligent design, some did.

For example, in my roulette-wheel illustration, the observation that Slick won
with every spin of the roulette wheel did suggest that Slick was somehow
cheating. Nevertheless, the observation that the ball had landed in the exact
same pocket every time did not, by itself, necessarily suggest design.
The Importance of Independence
Dembski began to wonder what it was about some kinds of patterns that
helped trigger the recognition of intelligent activity. Why do some patterns
indicate a physical or material cause and some an intelligent cause? Whatdistinguishes the patterns that we attribute to lawlike necessity from thepatterns that help to indicate intelligent design? As he studied the scientific
literature on randomness and pattern recognition, an answer came.
Dembski’s answer can be summarized in one word: independence.
Patterns that we recognize from an independent realm of experience help us
to detect design. By contrast, patterns that do not match patterns that we know
from another realm of experience indicate nothing about design. Whenobservers see a pattern in an event that exists only in the event itself—or
when observers impute patterns to events that do not correspond to patterns
they know from elsewhere—the patterns tell us nothing about whether the
events in question reflect the activity of another mind. Dembski calls events
that match independently known patterns “specifications.”5 He calls patterns
that observers merely perceive in the event alone “fabrications.”
To see the difference between specifications and fabrications, let’s goback to Victoria Harbor. The pattern that I observed in flowers on the hillside
did exhibit a pattern that I knew from an independent realm of experience, inparticular, from my experience of English vocabulary and grammar. But
imagine, instead, that I had fabricated a pattern that did not match any known
pattern. Imagine, for example, that I saw several large green bushes randomly
scattered in the area just beneath the flower garden. Let’s say that I decided toregard the pattern of dots formed by those plants as another way of spellingmy name and that I then started to discuss how the gardeners had welcomedme personally to the harbor.
My traveling companions would regard me as irrational, if not crazy.
True, I had correctly inferred that the gardeners had intentionally designed themessage “Welcome to Victoria.” But I had incorrectly inferred that themessage was meant for me personally. Why? Rather than detecting an
independent pattern in the arrangement of green bushes—one that I
recognized from an independent realm of experience—I created a pattern in
my mind from my experience of the event—from observing the bushes. Using
Dembski’s terminology, I fabricated a pattern; I did not detect an independentone.6
Dembski devised a test to distinguish these two types of patterns. If
observers can recognize, construct, identify, or describe a pattern withoutobserving the event that exemplifies it, then the pattern qualifies as
independent from the event. If, however, the observer cannot recognize (orhas no knowledge of) the pattern apart from observing the event, then theevent does not qualify as independent. Dembski theorized that we reliably
detect intelligent activity when we observe events that are both improbableand specified, where events are specified when they conform to a particularkind of pattern, namely, a pattern that can be recognized independently of theoccurrence of the event in question.
My roulette-wheel example also illustrates how independent patterns help
us to detect design. When the ball repeatedly landed in the same pocket, apattern did emerge. But in this case the pattern was not complex enough toindicate design. Instead, the croupier observed a highly regular, repetitive
pattern in which the same event occurred over and over again with great
frequency. This led him to suspect that a physical process or cause wasproducing the repetitive pattern.7 And sure enough, he discovered a physicaldefect in the roulette wheel—an inordinately high lip near the pocket—that
was causing the ball to catch and fall into the red 16 pocket every time.
Observing a different type of pattern, however, triggered awareness of adifferent kind of cause. When Slick repeatedly won the game, despite the balllanding in a variety of different holes, the croupier again detected a pattern.
But this time he inferred that Slick was cheating. Why? What was different
about this second pattern? The second pattern was different from the first in
two ways.
First, rather than the same physical event recurring at the gaming table(with the ball repeatedly landing in the exact same pocket), the croupierobserved a complex series of events that defied description by any physicallaw or mathematical algorithm. Almost every outcome on the game table wasdifferent from the one before. The spinning of the wheel generated a complex
sequence or pattern, not a repetitive one. The specific sequence he observed
was extremely improbable, whereas the repeating sequence of red 16s was
highly probable given the defect he discovered in the wheel.
Additionally, the second pattern derived its importance from something
independent of the physical events at the roulette table. Observers at theroulette table recognized that the complex sequence of outcomes at the tablematched a complex pattern of events that they had noticed independently, inparticular, in the sequence of bets that Slick had made. They also recognized afunctional significance in the complex pattern of events at the table. They saw
that the series of outcomes advanced a goal or objective—namely, winningmoney in the game. Because they knew the rules of roulette, and because theyobserved the bets that Slick made, they recognized a pattern, and indeed afunctionally significant one, and detected design. But unlike the other case, inwhich the pattern was evident simply by observing the event itself, the patternin this case could be detected only by those who knew something about
events and conventions beyond the physical boundaries of the roulette wheel.
As Dembski examined cases of pattern recognition that led to thedetection of intelligent activity, he began to notice that the same elements
were invariably present. A highly improbable event had occurred. Its extreme
improbability or complexity defied explanation by reference to a simple
lawlike process. And yet, despite the absence of a regular pattern, the event
clearly seemed significant to the observers because of something else theyknew from some other realm of experience independent of the event itself.
Often the observers knew of other events that exemplified the same
identical pattern as the event in question, in which case, the occurrence of theevent made the observer aware of an uncanny coincidence—such as thepatterns on Mt. Rushmore matching the exact shape of the ex-presidents’faces from paintings or photographs or such as Slick’s bet always matching
the hole into which the roulette ball later fell. Other times, observers had
knowledge of independent functional requirements or conventions (such as
the rules of a game or conventions of grammar) that made them aware of thesignificance—and, typically, the functional significance—of the complex
event in question. For example, the croupier’s knowledge of the rules ofroulette helped trigger his awareness of the functional significance of thephysical outcomes and contributed to his suspicion of design. When I arrived
in Victoria Harbor, my knowledge of the conventions of English contributed
to my recognition of the pattern and the functional significance of thearrangement on the hillside. Either way, Dembski realized that design
detection depended upon observers using knowledge of an independent realmof experience to help them recognize something significant—either
functionality or conformity to a pattern—in a complex event.
Two Types of Specifications
Since specifications come in two closely related forms, we detect design in
two closely related ways. First, we can detect design when we recognize thata complex pattern of events matches or conforms to a pattern that we know
from something else we have witnessed. Tourists at Mt. Rushmore recognize
patterns etched in the rock that exactly match patterns that they know fromseeing faces of presidents on money or in paintings. Because the shapes
carved into the mountain are also highly complex, they correctly infer that

they are the product of intelligent activity, rather than undirected processes.
Second, we can detect design when we recognize that a complex patternof events has a functional significance because of some operational
knowledge that we possess about, for example, the functional requirements orconventions of a system. If I observe someone opening a combination lock on
the first try, I correctly infer an intelligent cause rather than a chance event.
Why? I know that the odds of guessing the combination are extremely low,
relative to the probabilistic resources, the single trial available. But beyond
that I recognize, based upon my knowledge of how locks work, that theperson has produced a functionally significant outcome. And so, I conclude
design.
Functional Specifications as Patterns
As Dembski developed his theory, he characterized specification in a generalmathematical formalism that subsumed both these cases—the case in which
an observer recognizes a pattern in an event and the case in which an observer
recognizes a functionally significant outcome in an event. Dembski’s
mathematical formalism applied to both cases, because there is a sense inwhich a functionally significant event conforms to a pattern, although one thatmust be perceived abstractly rather than observed directly.
When I introduced the concept of a specification a few pages back, I
described it as a match between an event and an independent pattern or as an
event that matches or actualizes an independent set of functional
requirements. Dembski more commonly uses the first of these two
descriptions. Nevertheless, in both his illustrations and his mathematicalformalism he treats independent functional requirements as “independent
patterns.” In Dembski’s way of thinking, a functional requirement representsa kind of target and a target is a kind of pattern.
This makes sense. If there are 64,000 three-numeral right-left-rightcombinations possible for a standard combination lock with 40 dial settings,
then the combination that will open the lock represents a tiny target within the
abstract space representing all the possibilities. When an event occurs that“hits” such a target, it produces a functionally significant pattern or outcome.
Functionally significant outcomes actualize functional requirements, or “hittargets” representing such requirements. And events that actualize
independent functional requirements or “hit functional targets” constitutespecifications.
Mathematically, Dembski’s theory treats events that hit abstract targets incombinatorial space and events that match, or conform to, other kinds of
independent patterns as identical. What matters in Dembski’s theory is notwhether a pattern or target is perceived abstractly or observed directly withthe senses. What matters is whether the target or pattern can be known
independently of the event that matches or conforms to it. If an abstract target
represents a set of outcomes that exemplify real functional requirements,
requirements that can be known independently of an event that actualizes
them, then the occurrence of such an event is specified.
Here’s an illustration that helps to explain why it makes sense to regard
functional targets as independent patterns in the sense defined by Dembski. It
also shows how Dembski’s theory applies to the analysis of specified
information and, indeed, identifies it as a reliable indicator of intelligent
design. Consider these two strings of characters:“iuinsdysk]idfawqnzkl,mfdifhs”“Time and tide wait for no man.”
Given the number of possible ways of arranging the letters, spaces, andpunctuation marks of the English language for sequences of this length, boththese two sequences constitute highly improbable arrangements. Thus, bothsequences are complex and meet Dembski’s first criterion of a designed
system.
Nevertheless, only the second of the two sequences exhibits a
specification according to Dembski. To see why, consider that within the setof combinatorially possible sequences, relatively few will convey meaning.
This smaller set of meaningful sequences, therefore, delimits a domain (ortarget or pattern) within the larger set of possibilities. (See Fig. 16.2.) Is thisdomain or target “independently given” in the way that Dembski’s theory
requires?
Recall that to qualify as independent, the domain or target mustcorrespond to a known or preexisting pattern or set of functional
requirements, not one contrived after the fact of observing the event inquestion.8 Notice that the first of the two sequences above—“iuinsdysk]idfawqnzkl,mfdifhs”—does not conform to an “independently
given” pattern, or at least any that I recognize. Of course, after observing thesequence “iuinsdysk]idfawqnzkl,mfdifhs” someone simply could declare thatit means something such as “It’s time to eat lunch.” But in that case, thefunctional significance of the sequence would not have existed independentlyof the event in question. Instead, the observer would have fabricated, rather
than detected, functional significance in the pattern of letters. For that reason,the “meaning” the observer imputed to the sequence would not signify
anything about the sequence having originated in another mind.

Figure 16.2. A meaningful English phrase or sentence can be thought of as
hitting a functional target within a much larger space of possible
arrangements and thus is specified in the technical sense defined by
Dembski’s theory of design detection.
But what about the second sequence above? Does it correspond to anindependent set of functional requirements or conventions? It does. It conveys
meaning. And to convey meaning in English one must employ preexisting or
independent conventions of vocabulary (associations of symbol sequenceswith particular objects, concepts, or ideas) as well as syntax and grammar(such as “Every sentence requires a subject and a verb”). Indeed, whenarrangements of symbols utilize, exemplify, or match independent vocabulary
and grammatical conventions (functional requirements), meaningfulcommunication can occur in English.
Clearly, all of the English sentences that define the smaller domain or
target in Figure 16.2 actualize or utilize existing conventions of vocabulary
and requirements of grammar. Since the smaller domain in Figure 16.2
distinguishes functional sequences (English sentences) from nonfunctional

sequences, and the functionality of alphabetic sequences depends on
independently existing conventions of English vocabulary and grammar, thesmaller domain defines an “independently given” target or pattern.
Since the second string of characters (“Time and tide wait…”) falls within
this independently given domain or matches one of the possible meaningful
sentences within it (and, therefore, hits a functional target), the second
sequence exhibits a specification. Moreover, the functional requirements
exemplified by this sequence were not contrived after the fact of observing itin order to impute a meaning or significance to it that it would not possessotherwise. Instead, the second sequence exemplifies a set of known andpreexisting requirements and conventions that allow it to convey a message.
Thus, this second sequence is specified, according to Dembski’s
independence condition. Since this second sequence is also complex, it points,
according to Dembski’s theory, to an intelligent source, just as our intuitions
and experience tell us.
In any case, in the most general mathematical expression of Dembski’s
theory, a specification occurs not only (a) when an object or event matches or
exemplifies an independent pattern, but also (b) when an event or object falls
within an independently defined domain or hits a functional target, typically,by actualizing an independent set of functional requirements.9
Dembski’s Criteria Apply to DNA
Dembski’s theory of design detection has achieved great prominence in thedebate about intelligent design. However, there has long been confusion about
whether it applies to biology and, if so, how.
Several years ago, a friend of mine with a background in computer
programming was intrigued by the case for intelligent design from DNA.
Nevertheless, he was troubled by his inability to make a connection between
Dembski’s theory of intelligent design and the digital code in DNA. Did DNA
manifest Dembski’s two criteria of a designed system? He could see that thebase sequences in DNA were vastly improbable and, therefore, highly
complex. He also understood that the base sequences in DNA performed a
critical function by directing protein synthesis in the living cell. But he could
not see how DNA was specified in the sense that Dembski had so carefully
articulated in The Design Inference. Do the base sequences in DNA match apattern from some other realm of experience? If so, where does that pattern
reside?
As I have made my case for intelligent design on university campuses I
have encountered this concern more than once. I have even encountered somecritics who have rejected the theory because of it. For some, Dembski’s work

has achieved such prominence that it defines the theory itself. If it does not
apply to biology and to DNA specifically, then there is no theory of, or casefor, intelligent design in biology. Case closed.
This is a reasonable concern. If Dembski’s theory provides a definitive
general account of how intelligent design can be detected, as advocates of thetheory claim, and if DNA shows evidence of intelligent design as we also
claim, then it is certainly reasonable to think that DNA would exemplify
Dembski’s criteria. So let’s look at Dembski’s theory in more detail in light ofthis objection.
Dembski argues that specifications in conjunction with low-probability
events reliably indicate design. He describes a specification as a match
between an event and a special kind of pattern—patterns that we know
independently of, or separately from, the event in question. Does DNA
exemplify such a pattern? Do the sequences of bases in DNA match a patternthat we know independently from some other realm of experience? If so,where does that pattern reside?
Based upon a superficial reading of Dembski’s work, the answer to thesequestions might seem to be no. DNA does not exhibit a pattern in its basesequence that matches a pattern scientists have observed from some otherrealm of experience in the same way, for example, that visitors to Mt.
Rushmore recognize a pattern that they have observed elsewhere in
photographs or paintings of former presidents.
While certainly we do not see any pattern in the DNA molecule that we
recognize from having seen such a pattern elsewhere, we—or, at least,
molecular biologists—do recognize a functional significance in the sequencesof bases in DNA based upon something else we know. As discussed in
Chapter 4, since Francis Crick articulated the sequence hypothesis in 1957,
molecular biologists have recognized that the sequences of bases in DNA
produce a functionally significant outcome—the synthesis of proteins. Yet as
noted above, events that produce such outcomes are specified, provided they
actualize or exemplify independent functional requirements (or “hit”
independent functional targets). Because the base sequences in the coding
region of DNA do exemplify such independent functional requirements (andproduce outcomes that hit independent functional targets in combinatorial
space), they are specified in the sense required by Dembski’s theory.
To see why, consider the following. The nucleotide base sequences in thecoding regions of DNA are highly specific relative to the independent
requirements of protein function, protein synthesis, and cellular life. To
maintain viability, the cell must regulate its metabolism, pass materials backand forth across its membranes, destroy waste materials, and do many otherspecific tasks. Each of these functional requirements, in turn, necessitates
specific molecular constituents, machines, or systems (usually made of

proteins) to accomplish those tasks. As discussed in Chapters 4 and 5,
building these proteins with their specific three-dimensional shapes depends
upon the existence of specific arrangements of nucleotide bases in the DNA
molecule.
For this reason, any nucleotide base sequence that directs the productionof proteins hits a functional target within an abstract space of possibilities. As
discussed in Chapters 4, 9, and 11, the chemical properties of DNA allow avast ensemble of possible arrangements of nucleotide bases. Yet within thatset of combinatorial possibilities relatively few will—given the way themolecular machinery of the gene-expression system works—actually producefunctional proteins.10 (See Fig. 16.3.) This smaller set of functionalsequences, therefore, delimits a domain (or target or pattern) within a larger
set of possibilities. Moreover, this smaller domain constitutes an independent
pattern or target, since it distinguishes functional from nonfunctionalsequences, and the functionality of nucleotide base sequences depends on the
independent requirements of protein function.

Figure 16.3. A functionally specified sequence of nucleotide bases or amino

acids can be thought of as hitting a functional target within a much larger
space of possible arrangements and thus is also specified in the technicalsense defined by Dembski’s theory of design detection. Axe has shown thatfor a 150-amino-acid-protein domain the ratio of the size of circle P to thesize of target T is roughly equal to the ratio of the size of the Milky Way
galaxy to the size of a single cotton ball.
Therefore, any actual nucleotide sequence that falls within this domain ormatches one of the possible functional sequences corresponding to it “hits afunctional target” and exhibits a specification. Accordingly, the nucleotide
sequences in the coding regions of DNA are not only complex, but also
specified. Therefore, according to Dembski’s theory, the specificarrangements of bases in DNA point to prior intelligent activity, just as I
argued in the previous chapter on different, though closely related, grounds.
The Functional Logic of Information Storage and Processing
Dembski’s theory applies to the cell’s information-processing system as wellas to DNA itself. As I noted in Chapters 1 and 4, DNA encodes information
by storing it in a digital form. It also uses machinery—indeed, a system ofmolecular machines—to copy, transmit, and process that information to
produce a functional outcome.
All cells use stored digital information to regulate and direct theexpression of other digital information (in particular, the information stored in
the “protein coding regions” of the DNA molecule). Indeed, as I discuss inmore detail in Chapter 18 and in the Epilogue, portions of the genome thatmany biologists previously regarded as “junk DNA” are now known toperform many important functions, including the regulation and expression of
the information for building proteins. In this respect, the nonprotein coding
regions of the genome function much like an operating system in a software
program, directing and regulating how other information in the system isprocessed.
In any case, the cell’s information-processing system has three key
elements: (1) digital storage and encoding of information, (2) machinery forprocessing that information to produce a functional outcome, and (3)encoding of higher-order (hierarchically arranged) regulatory information.
These three key elements for expressing biological information are also foundin computer-based information-processing systems. They too (1) encode
information digitally, (2) process information with machinery, and (3) use
hierarchically organized information to regulate the expression of otherinformation.

These three elements provide a good example of what software designers
and other engineers call a design pattern.11 A design pattern is a general wayof solving a design problem. A design pattern distills a functional logic thatcan be applied and modified in different contexts to solve different but related
engineering problems.
DNA and the cell’s information-processing system exemplify many
design patterns. At the highest level, the way DNA and its informationprocessing machinery encodes and processes digital information represents asolution to a general design problem: how to store information and transmit it
across a communication channel in order to produce a functional outcome. It
also represents a solution to many other more specific subproblems such as
how to convert one-dimensional digital information into useful threedimensional structural information, how to copy information with fidelity
from one medium to another, how to automate error correction during
information processing, how to organize information about related andunrelated functions, and how to access and utilize distributed information.
The design patterns exemplified in the cell’s information-processing
system also exhibit specifications. Why? Because we recognize design
patterns in the cell’s information-processing system that match ones we know
from an independent realm of experience, in particular, from our own
information technology. We recognize a match or, rather, several of them.
We see in the cell’s use of nucleotide bases as digital characters afunctional motif that we know well from our own information technology.
Recall Richard Dawkins’s observation that “the machine code of the genes is
uncannily computer-like” or Bill Gates’s observation that “DNA is like acomputer program.” We also recognize a design pattern in the way the cell
stores, transmits, and processes information. Recall Hubert Yockey’s
observation that the “genetic code is constructed to confront and solve theproblems of communication and recording by the same principles found…inmodern communication and computer codes.”12 We also recognize afunctional pattern in the way the cell uses digital characters to construct threedimensional mechanical parts. Recall the discussion in Chapter 5 of theairline manufacturing industry’s use of CAD-CAM (Computer Assisted
Design and Manufacture). And scientists familiar with the logic of software
design can recognize many other more specialized design patterns andstrategies in the subsystems of the cell’s information-processing system.
This point was made to me with particular poignancy one afternoon acouple of years ago. I dropped by to visit a software engineer who was
working closely with one of the molecular biologists from the Biologic
Institute, a laboratory that we, at the Discovery Institute, helped to start. The
software engineer had been studying how the cell processes information inorder to write a computer simulation of gene expression. He showed me abook called Design Patterns, a standard text for software engineers. The text
was full of different design strategies—strategies for processing, storing,copying, organizing, accessing, and correcting digitally encoded strings ofinformation.
My colleague told me that he recognized many of these specific design
patterns and strategies at work in the cell. He expressed his awe at the“sophistication of its design logic” and its resemblance to that used in thesoftware industry. He said the cell often employs a functional logic thatmirrors our own, but exceeds it in the elegance of its execution. “It’s like weare looking at 8.0 or 9.0 versions of design strategies that we have just begun
to implement. When I see how the cell processes information,” he said, “itgives me an eerie feeling that someone else figured this out before we got
here.”
According to Dembski’s theory, my colleague may have had more thanmerely an intuitive feeling about the design of the cell’s informationprocessing system. He had two good reasons—specification and low
probability—for suspecting design. My colleague recognized in the cell’s
information-processing system many design patterns that he knew from an
independent realm of experience. But he realized that the system of moleculesand machines that he had been studying was also extremely complex. Thus,
according to Dembski’s theory, the digitally encoded information andinformation-processing system in the cell points to intelligent design, just as
my friend’s intuition and his expertise had led him to suspect.
Convergence: Complexity and Specification Are Information
When I first met Dembski in the summer of 1992, he and I were attacking the
problem of design detection from what we thought were two different
directions. He was interested in the logic of chance elimination. As hedeveloped his ideas, he realized first that pattern recognition played a key rolein the elimination of chance and then, second, that particular types of patterns,
namely, independent patterns, could also help us detect design. His starting
point in this analysis was statistical hypothesis testing. Thus, he spoke in thelanguage of probability and kept talking about identifying “small probability
specifications” as a way of detecting design.
On the other hand, I had been first impressed in my discussions withCharles Thaxton by the uncanny applicability of information theory tomolecular biology. Though I soon realized that Shannon’s theory of
information could not be equated with meaning or function, I was convincedthat Shannon’s concept could be supplemented easily to make it useful tobiology. Just as English speakers can distinguish meaningful fromnonmeaningful arrangements of English letters in virtue of their knowledge ofestablished conventions of grammar and vocabulary, biologists have beenable to make determinations about the functional role of DNA base sequences
based on knowledge they had acquired of the genetic code and the geneexpression system. As noted in Chapter 4, molecular biologists recognized“specificity”13 in the arrangement in nucleotide bases and amino acids anddefined specificity as what’s necessary to produce a particular effect or
perform a function.
I suspected that the presence of the extra information-theoretic property of“function” or “specificity” in conjunction with a quantifiable amount of
Shannon information might well indicate design. Thus, in my discussions
with Dembski, I kept talking about the presence of information content orfunctional information as possible indicators of design in biology.
Nevertheless, Dembski was reluctant to consider information as an indicatorof design. He knew that many people confused Shannon information withmeaning or function. And he knew that improbability alone, which Shannon’s
theory measured, did not necessarily indicate design. Thus, Dembski kept
proposing the presence of “small probability specifications,” not information,
as the definitive indicator of an intelligent cause.
The next winter while I was staring at a snowstorm, the obvious
resolution to our conflict hit me. Small-probability specification was
information, at least, the functional kind of information that I was considering
as an indicator of design. If an improbable sequence produced a functional
outcome, then it was also specified in the sense that Dembski’s method
required. Dembski and I had been describing the same reality—the same
criterion—in different words. Small-probability specification was just anotherway of saying “Shannon information plus”—either Shannon information plus
functionality, or Shannon information plus meaning, or Shannon information
plus specification, which, in Dembski’s theory, subsumed both meaning andfunction.
This made perfect sense. After all, Shannon’s theory of information
provided a way to measure the complexity or improbability of a sequence ofcharacters in a communication channel. But since Shannon’s equations did
not measure or detect meaning or function, determinations about functionneeded to be made in other ways. I realized that. But I did not initially
understand how Dembski’s notion of specification related to function. As I
came to realize that function constituted a special case of specification—orthat specification subsumed function as an instance of it—I realized that we
had been talking past each other.
Dembski, who had been previously resistant to using the language of
information altogether, now made this equivalence explicit. He began to talkabout small-probability specifications and “complex specified information” asthe same thing. He used the term “complex” as well as “specified” to modify
information, to describe cases where the amount of specified information
exceeded available probabilistic resources and thus defied explanation by
chance. When I refer to “large amounts” of specified information as I havedone in previous chapters, I mean precisely what Dembski means by“complex specified information,” namely, that the amount of specified
information in the system exceeds the amount that can be best explained bychance given the available probabilistic resources.
In any case, the convergence between Dembski’s criterion of design
detection and mine is evident throughout the examples that I have used toillustrate his method. The flower arrangement in Victoria Harbor not only
exhibited complexity and specification, but also contained functional or
specified information. The sentence “Time and tide wait for no man”
represents not only an improbably specified sequence of characters, but also
one that clearly conveys information and performs a communication function.
This equivalence suggests a final way in which Dembski’s theory ofdesign applies to biology. Since functionally specified information exhibits
complexity and specification, events or objects that possess functionallyspecified information also necessarily exhibit Dembski’s twin indicators ofdesign. Dembski’s theory therefore applies to any system that has large
amounts of such functional information. Since DNA, RNA, and proteins dohave large amounts of functionally specified information, and since even thefirst simple self-replicating organism would have required large amounts of it,
Dembski’s theory also implies that the origin of the specified informationnecessary to build the first living cell is best explained by intelligent design.
All roads lead to Rome.

17
But Does It Explain?

Imagine a team of researchers who set out to explore a string of remoteislands near Antarctica. After many days at sea, they arrive on an icy,windswept shore. Shouldering their packs, the team hikes inland andeventually takes shelter from the bitter cold in a cave. There, by the light of asmall campfire built to cook their freeze-dried rations, they notice a curious
series of wedgelike markings vaguely reminiscent of Sumerian cuneiform. It
occurs to them that perhaps these scratches in the rock constitute some sort ofwritten language, but dating techniques reveal that the markings are morethan five hundred thousand years old, far older than any known humanwriting and, indeed, far older than anatomically modern human beings.
The researchers investigate other possibilities. Perhaps the markings are
animal scratchings. Perhaps they were left by some sort of leeching process orby glacial action, perhaps in conjunction with winds bringing sand throughgaps in ice at high speeds. After extensive research by investigators with abroad range of expertise, these and other explanations invoking purely
mindless undirected causes fail to explain the evidence. An additional
discovery reinforces this conclusion. In a broad cavern farther inside the cave,the explorers find a series of drawings on the walls of various fish, birds, andmammals; geological features such as mountains and valleys; and what
appear to be tools of modest sophistication. Each picture has beneath it asequence of markings, sequences also found among the markings along thewalls of the entrance cave. The markings, it seems, are words. The
investigators are unable to unravel the function of every word or sequence inthe cave writings, and there even appears to be possible misspelled words
among them, but eventually it becomes clear that the markings have beenarranged in a variety of complex patterns to describe hunting and fishingtechniques as well as methods for using the various creatures for food andfuel.
In the process of their painstaking investigation, the explorers make aninference. They note that, although the markings do not reveal the identity ofthe scribes, they do point to intelligent activity of some kind. The markings
reveal a sophisticated system for conveying information, and the only known
cause for such a thing is intelligence—conscious rational activity. They

conclude that the remote and barren islands were once settled by a group of
toolmakers and hunters who employed written, alphabetic language some five
hundred thousand years before modern humans were believed to haveinvented the technique.
The team expects to face skepticism over the dating of the cave markings,
and indeed the first wave of doubts focus on this. However, two independent
teams, including one using a new dating technique, confirm that the markings
are ancient, between four hundred and six hundred and fifty thousand years
old. The scientific community soon accepts these findings.
The original researchers think they have now established a solid scientificconclusion. But then some naysayers begin to level a series of philosophicaland methodological objections to their work. Some of the critics dispute theconclusion, because they claim it’s based merely on our ignorance of any
known natural process capable of generating inscriptions. Scientists discover
new things all the time, the naysayers point out. Surely it’s only a matter oftime before one of them discovers a natural cause that can explain theinscriptions. They claim that the research team has made a fallacious “scribeof the gaps” argument.
Another skeptic, a philosopher of science, insists that it’s fine to infer thatan intelligent agent was at work at times and in places where humans were
known to be present, but it’s not clear that any human agent was on the scene
when these markings were made. Since we don’t know of any nonhuman
examples of intelligent activity, we can’t say anything about the origin of themarkings in the cave. Still another skeptic, a famous biologist, poses what hesees as the ultimate stumper. He insists that the team hasn’t really explained
the origin of the markings at all. “If inscriptions point to an ancient scribe,then who designed the designer of the inscriptions?” he asks. Since
presumably the mind who designed the inscriptions was full of information,then invoking an intelligence as the explanation for the information on the
cave wall merely pushes the question of the ultimate origin of informationback in time, generating an unacceptable and possibly infinite regress.
For these and other reasons, skeptics question the logic of the research
team’s inference to design. Some insist that the inference to an intelligent
cause was not warranted, others that it explained nothing.
As I have presented the argument for intelligent design as the best
explanation for the DNA enigma at academic and scientific conferences, I
rarely encounter scientists who claim to have a better, or even adequate,
explanation for it. In response to the case for design from DNA, I frequently
have the experience of debate opponents actually conceding that they do notknow how to explain the origin of the information necessary to produce thefirst life, as indeed leading origin-of-life researchers have often done as well.
Instead, as I have made my case for design as the best explanation for theorigin of biological information, I have found that the scientists andphilosophers who reject it typically do so on philosophical grounds not unlike
the objections raised by the hypothetical naysayers in the preceding parable ofdiscovery. Critics insist that the argument to intelligent design is logicallyflawed, that it is unwarranted or that it explains nothing.
In this chapter, I defend my case for intelligent design against those who
claim that it is, in some way, unwarranted or logically flawed. In the process,I also show how making the case for intelligent design as an inference to thebest explanation inoculates it against several common philosophicalcriticisms.
Argument from Ignorance?
Over the years, I have participated in many debates about the theory ofintelligent design at scientific conferences, on university campuses, and on
television and radio programs. In nearly every debate, my debate partner has
claimed that the case for intelligent design constitutes an argument fromignorance. Arguments from ignorance occur when evidence against a
proposition is offered as the sole (and conclusive) grounds for accepting somealternative proposition.
Critics of intelligent design often assert that the case for intelligent designcommits this fallacy.1 They claim that design advocates use our present
ignorance of any natural or material cause of specified information as the solebasis for inferring an intelligent cause for the origin of biological information.
They accuse ID advocates of arguing for intelligent design based only uponevidence against the adequacy of various natural causes. Since we don’t yet
know how biological information could have arisen, we invoke themysterious notion of intelligent design. In this view, intelligent design
functions not as an explanation, but as a fig leaf for ignorance.
The inference to design as developed here does not commit this fallacy.
True, some of the previous chapters of this book do argue that, at present, alltypes of material causes and mechanisms fail to account for the origin of
biological information from a prebiotic state. And clearly this lack ofknowledge of any adequate material cause does provide part of the grounds
for inferring design from information in the cell, although it is probably moreaccurate to characterize this supposed “absence of knowledge” as knowledgeof absence, since it derives from a thorough search for alternative
materialistic causes and a thorough evaluation of the results of numerous
experiments performed over several decades.
In any case, the inadequacy of proposed materialistic causes forms only
part of the basis of the argument for intelligent design. We also know frombroad and repeated experience that intelligent agents can and do produce
information-rich systems: we have positive experience-based knowledge of acause that is sufficient to generate new specified information, namely,
intelligence. We are not ignorant of how information arises. We know fromexperience that conscious intelligent agents can create informationalsequences and systems. To quote Quastler again, “The creation of new
information is habitually associated with conscious activity.”2 Experienceteaches that whenever large amounts of specified complexity or information
are present in an artifact or entity whose causal story is known, invariably
creative intelligence—intelligent design—played a role in the origin of that
entity. Thus, when we encounter such information in the large biologicalmolecules needed for life, we may infer—based on our knowledge ofestablished cause-and-effect relationships—that an intelligent cause operated
in the past to produce the specified information necessary to the origin of life.
For this reason, the design inference defended here does not constitute anargument from ignorance. Instead, it constitutes an “inference to the best
explanation” based upon our best available knowledge.3 As noted in Chapter
7, to establish an explanation as best, a historical scientist must cite positive
evidence for the causal adequacy of a proposed cause. Indeed, unlike anargument from ignorance, an inference to the best explanation does not assert
the adequacy of one causal explanation merely on the basis of the inadequacyof some other causal explanation. Instead, it asserts the superior explanatory
power of a proposed cause based upon its proven—its known—causal
adequacy and based upon a lack of demonstrated efficacy among thecompeting proposed causes.
In Chapter 15 I provided evidence for the causal adequacy of intelligent
design to account for large amounts of specified information. There I showedthat we know from ordinary experience as well as from the results ofscientific experiments and computer simulations that intelligent agents do
produce large amounts of specified information. Since I had previously shown
via a thorough search that no known material process produces this effect, I
argued that we can infer design as the best explanation for the origin of
information in the cell. The inference to design, therefore, depends on present
knowledge of the demonstrated causal powers of material entities andprocesses (inadequate) and intelligence (adequate). It no more constitutes anargument from ignorance than any other well-grounded inference in geology,archaeology, or paleontology—where present knowledge of cause-and-effectrelationships guides the inferences that scientists make about the causes of
events in the past.
Formulated as an inference to the best explanation, the argument for
design from biological information exemplifies the standard uniformitariancanons of method employed within the historical sciences. The principle ofuniformitarianism states that “the present is the key to the past.” In particular,
it specifies that our knowledge of present cause-and-effect relationships
should govern how we assess the plausibility of inferences we make about thecause of events in the remote past. Determining which, among a set ofcompeting explanations, constitutes the best depends on knowledge of thecausal powers of the competing explanatory entities, knowledge that we
acquire through our repeated observation and experience of the cause-andeffect patterns of the world.4 Such knowledge, not ignorance, undergirds theinference to intelligent design from the specified information in DNA.
Arguments from ignorance make an obvious logical error. They omit anecessary kind of premise, a premise providing positive support for theconclusion, not just negative evidence against an alternative conclusion. The
case for intelligent design as an inference to the best explanation does notomit that necessary type of premise. Thus, it does not commit the fallacy.
Let’s take a closer look. In an explanatory context, arguments fromignorance have the form:
Premise One: Cause X cannot produce or explain evidence E.
Conclusion: Therefore, cause Y produced or explains E.
Critics of intelligent design claim that the argument for intelligent design
takes this form as well. As one of my frequent debating partners, Michael
Shermer, likes to argue, “Intelligent design…argues that life is too specificallycomplex (complex structures like DNA)…to have evolved by natural forces.
Therefore, life must have been created by…an intelligent designer.”5 In short,critics claim that ID proponents argue as follows:
Premise One: Material causes cannot produce or explain specified
information.
Conclusion: Therefore, an intelligent cause produced specified
biological information.
If proponents of intelligent design were arguing in the preceding manner,
they would be guilty of arguing from ignorance. But the argument made inthis book does not assume this form. Instead, it takes the following form:
Premise One: Despite a thorough search, no material causes havebeen discovered that demonstrate the power to produce large amountsof specified information.

Premise Two: Intelligent causes have demonstrated the power toproduce large amounts of specified information.
Conclusion: Intelligent design constitutes the best, most causallyadequate, explanation for the information in the cell.
Or to put it more formally, the case for intelligent design made here has theform:Premise One: Causes A through X do not produce evidence E.
Premise Two: Cause Y can and does produce E.
Conclusion: Y explains E better than A through X.
In addition to a premise about how material causes lack demonstrated
causal adequacy, the argument for intelligent design as the best explanation
also affirms the demonstrated causal adequacy of an alternative cause,
namely, intelligence. This argument does not omit a premise providing
positive evidence or reasons for preferring an alternative cause or proposition.
Instead, it specifically includes such a premise. Therefore, it does not commit
the informal fallacy of arguing from ignorance. It’s really as simple as that.
Science and Saying NeverSome might still deny the legitimacy of inferring intelligent design (even as abest explanation), because we are ignorant of what future inquiry may
uncover about the causal powers of other natural entities or material
processes. Some would characterize the design inference presented here as
invalid because it depends on a negative generalization—that is, purely
physical and chemical causes do not generate large amounts of specified
information—a generalization that future discoveries may later falsify. We
should “never say never,” they say; to do so is a science stopper.6
Yet science often says “never,” even if it can’t say so with absolutecertainty. Negative or proscriptive generalizations often play an importantrole in science. As many scientists and philosophers of science have pointed
out, scientific laws often tell us not only what does happen, but also what
does not happen.7 The conservation laws in thermodynamics, for example,
proscribe certain outcomes. The first law tells us that energy is never createdor destroyed. The second tells us that the entropy of a closed system will
never decrease over time. Those who claim that such “proscriptive laws” donot constitute knowledge because they are based on past but not future
experience will not get far if they try to use their skepticism to justify funding

for research on, say, perpetual motion machines.
Further, without proscriptive generalizations, without knowledge of what
various possible causes cannot or do not produce, historical scientists could
not determine things about the past. As we saw previously, reconstructing thepast requires making abductive inferences from present effects back to pastcausal events.8 Making such inferences requires a progressive elimination ofcompeting causal hypotheses. Deciding which causes can be eliminated fromconsideration requires knowing what effects a given cause can—and cannot—
produce. If historical scientists could never say that particular entities lackparticular causal powers, they could never eliminate them from consideration,even provisionally. Thus, they could never infer that a specific cause had
acted in the past. Yet historical and forensic scientists make such inferences
all the time.
As archaeology, cryptography, and criminal forensics show, we often infer
the past activity of an intelligent cause without worrying about committing
fallacious arguments from ignorance. And we do so for good reason. A vast
amount of human experience shows that intelligent agents have unique causal
powers that purely material processes lack. When we observe features or
effects that from experience we know only agents produce, we rightly infer
the prior activity of intelligence. To determine the best explanation, scientists
do not need to say “never” with absolute certainty. They need only say that a
postulated cause is best, given what we know at present about thedemonstrated causal powers of competing entities. That cause C can produce
effect E makes it a better explanation of E than some cause D that has neverproduced E (especially if D also seems incapable of doing so on theoreticalgrounds), even if D might later demonstrate causal powers of which we arepresently ignorant.9
Thus, the objection that the design inference constitutes an argument fromignorance reduces in essence to a restatement of the classical problem of
induction, the problem of not knowing whether our generalizations about
nature based upon past experience will be confirmed by future experience.
Yet one could make the same objection against any scientific law or
explanation or any historical inference that takes present knowledge, but notpossible future knowledge, of natural laws and causal powers into account. As
physicists John Barrow and Frank Tipler note, to criticize design arguments,
as Hume did, simply because they assume the uniformity and normative
character of natural law cuts just as deeply against “the rational basis of any
form of scientific inquiry.”10 Our knowledge of what can and cannot produce
large amounts of new information later may have to be revised, but so might
the laws of thermodynamics. Such possibilities do not stop scientists frommaking generalizations about the causal powers of various entities or from

using those generalizations to identify probable or most plausible causes inparticular cases. Inferences based on past and present experience constituteknowledge (albeit provisional), not ignorance. Thus, those who object to suchinferences are objecting not only to the design inference, but to scientificreasoning itself.
The Human Factor
Some critics of intelligent design marshal a more subtle version of thepreceding argument. They admit that we may justifiably infer a past human
intelligence operating (within human history) from an information-rich
artifact or event, but only because we already know that human minds existed
during that time. But, they argue, since we do not know (we are ignorant of)whether any intelligent agent existed prior to humans, inferring a designing
intelligence that predates humans cannot be justified, even if we observe
effects that typically arise only from intelligent agents.11 This objection
asserts, in effect, that since we do not have independent knowledge of theexistence of intelligent agents prior to the advent of human beings, the casefor intelligent design does not meet the causal-existence requirement of a best
explanation.
Though this objection reflects an understanding of the historical scientificmethod, it reflects only a partial understanding of that method. It also
overlooks the strong logical basis for the design inference as formulated in
Chapter 15. True, historical scientists must meet both adequacy and pastexistence conditions in order to establish a causal claim. But, as noted in
Chapter 7, one way to meet that causal-existence requirement is to show that
there is only one known cause of a given effect. In such a case, theobservation of the effect automatically establishes the past existence of thecause and satisfies the causal-existence requirement. Since, as argued in
Chapters 8 through 15, intelligence is the only known cause of large amountsof specified information, the presence of such information in the cell points
decisively back to the action of a designing intelligence.
In the parable of discovery at the beginning of this chapter, my fictional
anthropologists reasoned in this manner. They inferred the existence of a past
intelligence prior to the existence of humans because they had discovered aneffect that, based upon their repeated experience, had only one known cause.
Thus, they inferred the existence and activity of intelligent agents, eventhough the evidence of that activity predated the origin of anatomicallymodern humans. Did this evidence provide a basis for affirming an earlier
origin for modern humankind? Perhaps. Did it provide the basis for inferring
the existence of a nonhuman form of intelligence? They couldn’t be sure. But

based on their repeated experience—and their introspective awareness of thecapacities that we possess that allow us to generate information—they could
affirm that a conscious and intelligent mind or minds had acted to produce thespecified information—the inscriptions—of interest.
My hypothetical explorers aren’t the only scientists to reason in thismanner. Actual anthropologists have often revised their estimates for the
beginning of human history or particular civilizations, because they havediscovered information-rich artifacts dating from times that predated theirprevious estimates of human or cultural origins. Such inferences to design
establish the existence and activity of human agents operating in a time orplace in which they were previously unknown. In making such inferences,anthropologists do not initially have independent knowledge of the existenceof humans from those times or places. Instead, they have only artifacts
displaying features that intelligence alone produces. That alone, however,enables them to establish the existence of a prior intelligent cause. The
anthropologists did not need independent knowledge of the existence ofhumans from those earlier times or locales.
Similarly, the scientists searching for extraterrestrial intelligence do notalready know that extraterrestrial intelligence exists. Yet they assume that thereceipt of specified information or complexity from an extraterrestrial source
would indicate the existence of an intelligence in space. In the science-fiction
novel Contact, scientists detect extraterrestrial intelligence in radio signals
carrying the first one hundred prime numbers. In actual SETI research,scientists are looking for more subtle indicators of intelligence, namely,
unnaturally modulated and focused radio signals.12 Either way, SETI does
presume that the presence of a complex and specified pattern would provide
grounds for suspecting the existence of an intelligence. Moreover, SETI seeks
precisely to establish the activity of an intelligent cause in a remote place andfrom a remote time in which intelligence is currently unknown. If scientificmethods can—in principle, at least—detect the presence of an extraterrestrial(and nonhuman) intelligence in a faraway galaxy, why can’t methods ofdesign detection be used to establish the activity of nonhuman intelligence inthe remote past as the cause of the specified complexity in the cell?
Hume’s Objection: A Failed Analogy?
Students of philosophy know about another common objection to intelligent
design, since they usually encounter it in their freshmen textbooks. According
to many philosophy textbooks, the debate about the design argument was
settled by the skeptical philosopher David Hume (1711–76).13 Hume refuted
the classical design argument in biology by showing that it depends on aflawed analogy between living forms and human artifacts. In his Dialogues
Concerning Natural Religion, Hume admits that artifacts derive from
intelligent artificers and that biological organisms have certain similarities tocomplex human artifacts. Eyes and pocket watches both depend on thefunctional integration of many precisely configured parts. Nevertheless, heargues, biological organisms also differ from human artifacts—they reproduce
themselves, for example—and the advocates of the design argument fail totake these dissimilarities into account. Since uniform experience teaches thatorganisms always come from other organisms, Hume argues that analogicalarguments really ought to suggest that organisms ultimately come from an
infinite regress of earlier organisms or from some eternally existent primevalorganism (perhaps a giant spider or vegetable), not a transcendent mind.
Despite the assertions of some critics,14 Hume’s objections to the classicaldesign argument fail to refute the argument of this book for several reasons.
First, we now know that organisms come from organisms, because organisms
possess information-rich macromolecules and a complex information-richsystem for processing and replicating the information stored in thosemolecules. Thus, his argument that uniform experience suggests thatorganisms necessarily arise from an infinite regress of primeval organisms (oran eternally self-existent one) fails. Repeated experience about the origin of
information-rich systems suggests two possibilities, not one. Either
information-rich systems arise from preexisting systems of information via amechanism of replication, or information-rich systems arise from minds. We
have repeated experiences of both. Even so, our experience also affirms—
based on cases in which we know the cause of such systems—that systems
capable of copying and processing other information ultimately arise from
intelligent design. After all, the computer hardware that can copy and process
information in software originated in the mind of an engineer.
Beyond that, advances in our understanding of planetary and cosmic
evolution have ruled out the possibility that biological life has always existed,either on earth or in the cosmos. At some point in the remote past, theconditions on earth and in the larger cosmos were simply incompatible withlife. The big-bang theory alone implies that the cosmos itself is finite. Thus,
scientifically informed people generally don’t argue that biological life
always existed or even that it always existed on earth. The question is whether
life originated from a purely undirected material process or whether a mind
also played a role. Between these two options uniform experience affirms
only the latter as an adequate cause for information-rich systems capable ofprocessing and copying information. Since we know that organisms capableof reproduction constitute information-rich systems, a Humean appeal touniform experience actually suggests intelligent design, not undirected
processes, as the explanation for the origin of the first life.

Second, the contemporary case for intelligent design (such as the one
made in this book) is not an analogical argument, even though many
interesting similarities do exist between living organisms and human
information technology. If, as Bill Gates says, “DNA is like a computer
program,” it makes sense, on analogical grounds, to consider inferring that
DNA also had an intelligent source. Nevertheless, although the digitally
encoded information in DNA is similar to the information in a computer
program, the case for design made here does not depend upon mere similarity.
Here’s why.
Classical design arguments in biology typically seek to draw analogies
between whole organisms and machines based on similar features present inboth systems, reasoning from similar effects back to similar causes. Thesearguments are a bit like those sixth-grade math problems in which students
are given a ratio of known quantities on one side of the equation and a ratio ofan unknown to a known quantity on the other and then asked to “solve for x,”
the unknown quantity. In analogical design arguments, two similar effects arecompared. In one case, the cause of the effect is known. In the other case thecause is unknown, but is presumed to be knowable because of the alleged
similarity between the two effects. The analogical reasoner “solves for x,” inthis case, the unknown cause.
The status of such design arguments inevitably turns on the degree ofsimilarity between the systems in question. If the two effects are very similar,
then inferring a similar cause will seem more warranted than if the two effects
are less similar. Since, however, even advocates of these classical design
arguments admit there are dissimilarities as well as similarities between living
things and human artifacts, the status of the analogical design argument has
always been uncertain. Advocates argued that similarities between organisms
and machines outweighed dissimilarities. Critics claimed the opposite.
But the DNA-to-design argument does not have an analogical form.
Instead, it constitutes an inference to the best explanation. Such arguments do
not compare degrees of similarity between different effects, but instead
compare the explanatory power of competing causes with respect to a single
kind of effect.
As noted, biological information, such as we find in DNA and proteins,comprises two features: complexity and functional specificity. Computercodes and linguistic texts also manifest this pair of properties (“complexity”
and “specificity”), what I have referred to throughout this book as specified
information. Although a computer program may be similar to DNA in many
respects and dissimilar in others, it exhibits a precise identity to DNA insofar
as both contain specified complexity or specified information.
Accordingly, the design argument developed here does not rely on acomparison of similar effects, but upon the presence of a single kind of effect—specified information—and an assessment of the ability of competing
causes to produce that effect. The argument does not depend upon thesimilarity of DNA to a computer program or human language, but upon thepresence of an identical feature in both DNA and intelligently designed codes,
languages, and artifacts. Because we know intelligent agents can (and do)
produce complex and functionally specified sequences of symbols andarrangements of matter, intelligent agency qualifies as an adequate causal
explanation for the origin of this effect. Since, in addition, materialistic
theories have proven universally inadequate for explaining the origin of suchinformation, intelligent design now stands as the only entity with the causal
power known to produce this feature of living systems. Therefore, thepresence of this feature in living systems points to intelligent design as thebest explanation of it, whether such systems resemble human artifacts in otherways or not.
Information as Metaphor: Nothing to Explain?
A related objection is answered in much the same way. Though most
molecular biologists see nothing controversial in characterizing DNA andproteins as “information-bearing” molecules, some historians andphilosophers of biology have recently challenged that description. The latehistorian of science Lily Kay characterized the application of information
theory to biology as a failure, in particular because classical information
theory could not capture the idea of meaning.15 She suggests that the term
“information” as used in biology constitutes nothing more than a metaphor.
Since, in Kay’s view, the term does not designate anything real, it follows thatthe origin of “biological information” does not require explanation.16 Instead,
only the origin of the use of the term “information” within biology requires
explanation. As a social constructivist, Kay explains this usage as the result ofvarious social forces operating within the “Cold War Technoculture.”17 In adifferent but related vein, philosopher Sahotra Sarkar has argued that theconcept of information has little theoretical significance in biology because it
lacks predictive or explanatory power.18 He, like Kay, seems to regard theconcept of information as a superfluous metaphor.
Of course, insofar as the term “information” connotes semantic meaning,
it does function as a metaphor within biology. That does not mean, however,that the term functions only metaphorically or that origin-of-life biologists
have nothing to explain. Though information theory has a limited applicationin describing biological systems, it has succeeded in rendering quantitative
assessments of the complexity of biomacromolecules. Further, experimental
work has established the functional specificity of the base sequences in DNA

and amino acids in proteins. Thus, the term “information” as used in biology
refers to two real and contingent properties: complexity and functional
specificity.
Since scientists began to think seriously about what would be required to
explain the phenomenon of heredity, they have recognized the need for somefeature or substance in living organisms possessing precisely these two
properties together. Thus, Erwin Schrödinger envisioned an “aperiodic
crystal”;19 Erwin Chargaff perceived DNA’s capacity for “complex
sequencing”;20 James Watson and Francis Crick equated complex sequenceswith “information,” which Crick in turn equated with “specificity”;21 Jacques
Monod equated irregular specificity in proteins with the need for “a code”;22and Leslie Orgel characterized life as a “specified complexity.”23 The
physicist Paul Davies has more recently argued that the “specificrandomness” of DNA base sequences constitutes the central mysterysurrounding the origin of life.24 Whatever the terminology, scientists haverecognized the need for, and now know several locations of, complex
specificity in the cell, information crucial for transmitting heredity andmaintaining biological function. The incorrigibility of these descriptive
concepts suggests that specified complexity constitutes a real property ofbiomacromolecules—indeed, a property that could be otherwise, but only tothe detriment of cellular life. Indeed, recall Orgel’s observation that “Livingorganisms are distinguished by their specified complexity. Crystals…fail toqualify as living because they lack complexity; mixtures of random polymers
fail to qualify because they lack specificity.”25
The origin of specified complexity, to which the term “information” inbiology commonly refers, therefore does require explanation, even if theconcept of information connotes only complexity in Shannon information
theory, and even if it connotes meaning in common parlance, and even if it
has no explanatory or predictive value in itself. Instead, as a descriptive(rather than an explanatory or predictive) concept, the term “information”(understood as specified complexity) helps to define an essential feature oflife that origin-of-life researchers must explain “the origin of.” So, only where
information connotes subjective meaning does it function as a metaphor in
biology. Where it refers to complex functional specificity, it defines a featureof living systems that calls for explanation every bit as much as, say, amysterious set of inscriptions on the inside of a cave.
But Who Designed the Designer?
Once when I was explaining the theory of intelligent design on a radio talkshow, a caller challenged me with another, now common, objection to thedesign inference—one that the caller clearly considered a knockdown
rebuttal: “If an intelligence designed the information in DNA,” he demanded,“then who designed the designer?” I asked for clarification. “Are you arguingthat it is illegitimate to infer that an intelligence played a role in the origin ofan event unless we can also give a complete explanation of the nature andorigin of that intelligence?” Yes, he said, that was exactly what he meant. I
then answered as best I could in the available time, but I remember thinking
later how facile I thought the objection was. It reminded me of the three-yearold child in the neighborhood where I grew up who used to follow older
children around asking them “why” questions. “Why are you goingswimming?” “Because it’s hot.” “Why is it hot?” “Because the sun’s out.”“Why is the sun out?” “Because there are no clouds today.” “Why are
there…” No matter how you answered, he would ask “why” again, as if in sodoing he had somehow invalidated the answer you had just given.
But does the ability to ask about the cause of a cause of an event
invalidate a causal explanation? That had always seemed such an obviously
flawed idea that I never bothered to refute it in print. Imagine my surprise,then, to learn that Professor Richard Dawkins, holder of the Charles Simony
Professorship in the Public Understanding of Science at Oxford University,
had advanced precisely that argument as the centerpiece of his case against
intelligent design in his bestselling book The God Delusion.26
There Dawkins argues that the design hypothesis fails to explain anything,because it evokes an infinite regress of questions. If complexity points to thework of a designing intelligence, then who designed the designer? According
to Dawkins, the designer would need to be as complex (and presumably as
information-rich) as the thing designed. But then he argues, by the logic of the
ID advocates, the designer must also be designed. But that would settle
nothing, because we then would have to explain the origin of a designing
intelligence by reference to a previous designing intelligence, ad infinitum,always leaving unexplained something as mysterious as we started with.
Thus, “the design hypothesis” fails, says Dawkins, because it “immediately
raises the larger problem of who designed the designer. The whole problem
we started out with was the problem of explaining statistical improbability. It
is obviously no solution to postulate something even more improbable.”27
When I read Dawkins’s version of this argument, I could see why itsounded plausible to some people. As I thought about it more, I became
intrigued by the fascinating philosophical issues it raised. I realized that
Dawkins had posed a serious philosophical objection to intelligent design,
even though his objection failed for several obvious and fundamental reasons.
Dawkins’s objection fails, first, because it does not negate a causal
explanation of one event to point out that the cause of that event may also

invite a causal explanation. To explain a particular event, historical scientists
often cite or infer past events as causes (see Chapter 7). But the events that
explain other events presumably also had causes, each of which also invites a
causal explanation.28 Is the original explanation thereby vitiated? Of coursenot. Pointing out that the past event cited in a causal explanation also has aprior cause—typically, another event—does not render the explanation void,nor does it negate the information it provides about past conditions or
circumstances. It merely raises another separate question. Clearly, the young
inquisitor in our neighborhood could decide to ask why it was hot after I told
him I was going swimming on account of the heat. But his decision to do so
did not negate the information he received about my reasons for goingswimming or my prior state of mind. A proximate explanation of one event is
not negated by learning that it does not supply a comprehensive or ultimate
explanation of all the events in the causal chain leading up to it.
Nevertheless, Dawkins’s objection to the design hypothesis presupposes
precisely the opposite principle, namely, that causal explanations of specific
events count as explanations only if there is a separate and comprehensive
causal narrative that explains how the cause cited in the explanation itself
came into existence from something simpler, and only then if the narrative
does not involve an infinite regress of other past causes. Yet Dawkins cannot
seriously apply that principle in any other case without absurdity. If appliedconsistently, Dawkins’s principle would, for example, prevent us frominferring design in cases where no one, not even Dawkins, questions thelegitimacy of such inferences.29 One needn’t explain who designed thebuilders of Stonehenge or how they otherwise came into being to infer that
this complex and specified structure was clearly the work of intelligent
agents. Nor would someone need to know how the scribes responsible forcave markings in my opening parable came into being in order to attribute
those inscriptions to a designing intelligence.
Imagine you have traveled to Easter Island to view the famous Moaistatues. A child beside you asks no one in particular, “Who carved thesestatues?” A man standing next to the kid looks over the top of his glasses andasks, “Why do you assume they’re sculpted?” Dumbfounded by the question,
the kid has no reply, so you rush to his aid. “The carvings manifest a patternthat conforms to the shape of a human face. The match in the patterns is too
close and the figures are too intricate, for it to be mere coincidence.” The man
scoffs. “Don’t tell me you’ve been reading intelligent-design propaganda, allof that rubbish about specified complexity? Let me ask you this: Who
sculpted the sculptor? Who designed the designer? Do you see the problem?
Your reasoning leads to an infinite regress. Who designed the designer’s
designer’s designer’s designer’s…” The child, appropriately unimpressed bythis display of erudition, rolls his eyes and mutters under his breath, “Yeah.
But I know someone carved these.” And, indeed, someone did.
The absurdity that results from consistently applying Dawkins’s implicit
principle of reasoning has invited parody from various quarters. In a popular
YouTube mock interview, the fictional Dr. Terry Tommyrot argues that
Richard Dawkins is himself a delusion, despite the extensive textual evidence
for his existence in his many books. As Dr. Tommyrot asks his interviewer,“If Dawkins designed the books, then who designed the Dawkins? Just tell me
that!”30
Of course, Dawkins insists that the problem of regress does not afflict
properly scientific (read: materialistic) explanations, even explanations
involving ordinary human designers. Why? Because as a scientific materialist,
Dawkins assumes that physical and chemical processes provide completematerialistic explanations for the thoughts and actions of human agents, andthat Darwinian evolution can provide a comprehensive and fully materialistic
account of the origin of Homo sapiens from earlier and simpler forms of life.
Thus, the materialists’ answer to the question, “Who designed Dawkins?” is,“No one.” Dawkins descended by material processes from a series of humanparents, the first of whom evolved by natural selection and random mutationfrom lower animal forms, which in turn did the same. Further, the evidence ofintelligence in Dawkins’s books that points proximately to the activity of amind, points ultimately to simpler physical and chemical processes in his
brain. These processes make his conscious mind—like all human minds—
either an illusion or a temporary “epiphenomenon” that has no ability to affectthe material world. Material processes can explain everything simply andcompletely without any appeals to mysterious immaterial minds and without
any regress equivalent to that implied in appeals to intelligent design.
But is this true? Is there really such a seamless and fully materialistic
account of reality available? Oddly, Dawkins himself has admitted that that
there is not. As noted in the previous chapter, Dawkins has acknowledged thatneither he nor anyone else possesses an adequate explanation for the origin of
the first life.
Yet the Darwinian explanation holds that every living thing ultimately
evolved from the first self-replicating life-form. Thus, by Dawkins’s own
logic, one could vitiate the entire edifice of Darwinian explanation simply bydemanding an explanation for the cause of the cause it cites—that is, thecause of the process of natural selection itself, the origin of the first selfreplicating organism. If human life evolved from simpler forms of life, and if
biological evolution commences only once a self-replicating organism has
arisen, couldn’t the skeptic ask, “What evolved the evolver? How did the firstself-replicating organisms arise?”
Of course, the lack of a materialistic explanation for the origin of life does

not invalidate Darwinian explanations of the origin of higher life-forms.
Logically, it’s perfectly possible that some unknown, non-Darwinian cause
produced the first life, but then natural selection and random mutation
produced every living form thereafter.31 But Dawkins’s criterion of asatisfying explanation seems to imply otherwise.
There is an additional problem. Suppose scientists did formulate acompletely adequate materialistic explanation for the origin of life. Couldn’t askeptic of Dawkins’s materialism still ask for an account of the origin ofmatter itself? If every material state arose because of the laws of nature acting
on a previous material state, then materialistic causal narratives would seemto have their own problems with infinite regress. From whence came the firstmaterial state? No physical cosmology now provides a causal explanation of
how matter and energy came into being. But suppose one did. How would it
do so without invoking a regress of prior material (and/or energetic) states?
But what then would become of Dawkins’s insistence that causal explanationsof particular events fail unless all such regresses are eliminated?
There is still another difficulty with Dawkins’s argument. Part of the forceof his objection lies in its implicit accusation of inconsistency in the case forintelligent design. If specified complexity always points to intelligent design,then the existence of a designing mind in the past would, by Dawkins’s
understanding of the logic of the design inference, necessarily point to a stillprior designing mind, ad infinitum. In asserting this, Dawkins assumes thatdesigning minds are necessarily complex (and, presumably, specified) entities(itself a questionable proposition).32 He then argues that advocates ofintelligent design can escape the need for an infinite regress only by violating
the rule that specified (or irreducible) complexity always points to a priorintelligent cause. Inferring an uncaused designer, he seems to be arguing,
would represent an unjustified exception to the principle of cause and effectupon which the inference to design is based.
But positing an uncaused designer would not constitute an unjustified
exception to this principle, if it constitutes an exception at all.33 In every
worldview or metaphysical system of thought something stands as theultimate or prime reality, the thing from which everything else comes. All
causal explanations either involve an infinitive regress of prior causes, or theymust ultimately terminate with explanatory entities that do not themselves
require explication by reference to anything more fundamental or primary. If
the latter, then something has to stand as the ultimate or primary causal
principle at the beginning of each causal chain. If the former—if all
explanations inevitably generate regresses—then all explanations fail to meet
Dawkins’s implicit criterion of explanatory adequacy, including his own.
Since, however, most cosmological theories now imply that time itself had abeginning, and further imply that life itself first arose sometime in the finite
past, it seems likely that every chain of effect back to cause must terminate atsome starting point. Either way, materialistic explanations as well as those
involving mind are subject to these same constraints. If so, why couldn’t animmaterial mind function as the ultimate starting point for causal explanation
just as well as matter and energy?
In Dawkins’s worldview, matter and energy must stand as the prime
reality from which everything else comes. Thus, Dawkins simply assumes
that a material process must function as the fundamental explanatory principle
or first cause of biological complexity and information. His “who designed
the designer” objection shows this. Why? Dawkins assumes that explanations
invoking intelligent design must either generate a regress of designing minds
or that such explanations must eventually account for mind by reference to an
undirected material process. Either way, Dawkins simply presupposes thatmind cannot function as the ultimate explanation of biological complexity andinformation. For Dawkins and other philosophical materialists, matter alone
can play this role. But that begs that fundamental question at issue in thedebate about the origin of life.
A more philosophically neutral way to frame the issue would be to simply
ask: What is a better candidate to be that fundamental explanatory principle,
the thing from which specified complexity or information ultimately comes?
What is a better candidate to be the first cause of this phenomenon: mind or
matter?
Based upon what we know from our own experience, as opposed todeductions from materialistic philosophical doctrine, the answer to thatquestion would seem to be mind. We have first-person awareness of our own
minds. We know from direct introspection what attributes our minds possess
and what they can do. Our uniform experience shows that minds have thecapacity to produce specified information. Conversely, experience has shownthat material processes do not have this capacity. This suggests—with respect
to the origin of specified information, at least—that mind is the better
candidate to be the fundamental explanatory entity, the thing from which suchinformation comes in the first place.
In any case, explanations invoking intelligent design do not necessarily
imply either an infinite regress or the need for further reductionistic accounts
of intelligence as a cause. A self-existent immaterial mind might well function
as the ultimate cause of biological information, just as prior to the acceptanceof the big-bang theory matter functioned as the self-existent entity from which
everything else came for philosophical materialists. Intelligent design—
defined as a choice of a rational agent to actualize a possibility—might well
be a fundamental cause that requires no prior explanatory cause of itself.
Agents have the power by their choices to initiate new sequences of cause andeffect. Most human agents reflexively assume (and intuitively know) they
have this power. Perhaps an uncaused agent with similar powers generated thefirst biological information. Dawkins cannot foreclose that possibility without
first assuming an answer to the question at issue, namely, whether mind or
matter stands as the ultimate explanation of biological information. Thus, his“who designed the designer” objection commits, among other errors, thelogical fallacy of begging the question. It, therefore, fails as an objection to
the design inference based upon DNA.
Conclusion
Over the years, I have encountered each of the objections discussed in thischapter. Initially, I responded to them in great earnest, hoping to persuade theobjector. I continue to make every effort to do so, but I’m no longer surprised
or disappointed when I don’t. Eventually, I realized something odd about allof these objections. None of the alleged logical errors involved in inferring
intelligent design from DNA would prevent any reasonable person frominferring or detecting the activity of intelligent agents in any other realm ofexperience. Even in exotic situations like the one limned in my opening
parable or in the Search for Extra-Terrestrial Intelligence, most reasonablepeople would not dispute the possibility of detecting intelligence. In the caseof SETI, many scientists doubt that the program will ever discoverextraterrestrial intelligence, since they doubt that our galaxy holds othertechnological civilizations, but few would question the premise of the search,
namely, that we should treat information-rich radio signals as a signature ofintelligence. The discovery of specified information alone would suggest
antecedent intelligence as the best explanation for the origin of that
information, without independent evidence of designing agents existing in therelevant places or times, remote though they might be. Nor when we detect
intelligence in more ordinary situations do we worry about making argumentsfrom ignorance, or generating infinite regresses, or running afoul of Hume’s
critique of analogical reasoning. Neither would we deny that something as
interesting as specified digitally encoded information requires explanation.
Instead, in hypothetical and real-world cases, the inference to intelligent
design as the best explanation for the origin of specified information isstraightforward and unproblematic—except, for some, when considering theorigin of life. This suggested to me that perhaps many of these objections to
the design inference actually constituted a form of special pleading, perhaps
on behalf of a favored idea. Someone once said, “Behind every double
standard lies a single hidden agenda.” What might that agenda be? As it turns
out, I did not have to wait long or do much detective work to find out.

18
But Is It Science?

In December 2005, Judge John E. Jones III ruled that a Dover, Pennsylvania,
school district could not tell its biology students about a book in the school
library that explained the theory of intelligent design. The judge based his
decision on the testimony of expert witnesses—two philosophers, RobertPennock and Barbara Forrest—who argued that the theory of intelligent
design is not scientific by definition.1 Since it is not scientific, the judge
reasoned, it must be religious. As he put it in his ruling, “Since ID is notscience the conclusion is inescapable that the only real effect of the ID Policy
is the advancement of religion.”2 Therefore, he ruled, telling students about
the theory of intelligent design would violate the establishment clause of the
U.S. Constitution.
Many people have heard about the theory of intelligent design only fromnews reports about the Dover trial in 2005.3 Naturally, such reports about thetrial and the judge’s decision have strongly influenced public perceptions of
the theory. For many people, if they know anything at all about the theory,
they know—or think they know—that intelligent design is “religion
masquerading as science.”
I encounter this perception nearly every time I speak about the evidence
for intelligent design, whether on university campuses or in the media. As
noted previously, as I present the evidence for intelligent design, critics do not
typically try to dispute my specific empirical claims. They do not dispute thatDNA contains specified information, or that this type of information always
comes from a mind, or that competing materialistic theories have failed toaccount for the DNA enigma. Nor do they even dispute my characterization of
the historical scientific method or that I followed it in formulating my case forintelligent design as the best explanation for the evidence. Instead, in addition
to the philosophical objections described in the previous chapter, critics
simply insist that intelligent design “is just not science,” sometimes evenciting Judge Jones as their authority.
Since Jones is a lower-level district judge who entered the trial with noapparent background in either science or the history and philosophy ofscience, and since he made several clear factual errors4 in his ruling, it would
be easy to dismiss his opinion. Jones rendered this response all the more

tempting by telling one reporter, apparently in all seriousness, that during thetrial he planned to watch the old Hollywood film Inherit the Wind forhistorical background.5 Inherit the Wind is a thinly veiled fictional retelling of
the 1925 Scopes “Monkey Trial.” But as historian of science Edward Larson
has shown in his Pulitzer Prize–winning Summer for the Gods, the drama isgrossly misleading and historically inaccurate. Clearly, Jones had little, if any,
relevant expertise from which to make a judgment about the merits orscientific status of intelligent design.
His opinion, however, reflected a much broader consensus amongscientific and academic critics of intelligent design. Indeed, it was later
discovered that Jones lifted more than 90 percent of his discussion of“Whether ID Is Science” in his lengthy opinion, virtually verbatim, from an
American Civil Liberties Union brief submitted to him before his ruling. The
ACLU brief, in turn, recapitulated the most common reasons for challenging
the scientific status of intelligent design based upon the testimony of theirown expert witnesses.6 Thus, the Jones opinion and the witnesses who
influenced it effectively expressed an entrenched view common not only
among members of the media, but within the scientific establishment at large.
I first discovered how deeply entrenched this view is in 1993. I was
attending a private conference of scientists interested in the case forintelligent design. In attendance was Dean Kenyon, the evolutionary biologist
who had pioneered the self-organizational approach to the origin of life (seeChapter 11). After he repudiated his own theory in the late 1970s, Kenyon
eventually came to favor the case for intelligent design. He later announced
his change of view in 1985 at the conference in Dallas that first sparked myinterest in the origin-of-life debate.
Eight years later, Kenyon was in trouble. He had been removed fromteaching introductory biology at San Francisco State University after heexplained to his students why he had rejected chemical evolutionary theoriesof the origin of life—including his own—and why he now favored the design
hypothesis. Kenyon explained that the presence of digital information in DNA
seemed to provide compelling evidence for the actual, not just apparent,
design of the first life. But colleagues insisted that his discussion of intelligent
design was inappropriate. Why? Because, in their view, intelligent design did
not qualify as a scientific hypothesis.
Reasoning much like Judge Jones in the Dover case, Kenyon’s colleagues
accused him of forsaking science and bringing religion into the scienceclassroom. They then removed him—a senior, highly published professor
with a Stanford Ph.D. in biophysics and a world-class reputation in origin-oflife research—from his introductory biology class and reassigned him tosupervising labs.

After hearing about Kenyon’s predicament, I wrote an opinion article inhopes of publicizing the attack on his academic freedom. I submitted thearticle to the Wall Street Journal in the fall of 1993. Though the paper
accepted it, it held it for nine weeks before deciding to publish it. Then one
Friday afternoon in early December, long after I had given up on ever seeing
it published, I got a call from an editor at the Journal. The article would run
on the editorial page the next Monday morning. I called Professor Kenyon
with the news. He was shocked. He had just heard that the San FranciscoState University faculty senate had decided to hear his case the following
Tuesday. On Monday the article was published. On Tuesday, Kenyon won his
appeal before the faculty senate by a vote of 25–8—albeit with therepresentatives of the biology department voting unanimously against him.
Despite the favorable outcome of Kenyon’s case, other ID-friendly
scientists have experienced similar treatment.7 Like the judge in the Dovercase, many scientists regard the design hypothesis as “unscientific” bydefinition. In their view, any scientist who advocates the theory of intelligent
design has, in their view, broken a fundamental rule of scientific inquiry andmay deserve censure, denial of tenure, or termination.
But why isn’t the theory of intelligent design scientific? On what basis docritics of the theory make that claim? And is it justified?
In the previous chapters, I argued that intelligent design provided the best
explanation for the origin of biological information, and I sought to refute
those who claim that intelligent design doesn’t provide a logically valid or
adequate explanation of this phenomenon. Now I address another objection,
perhaps the most pervasive objection of all to intelligent design: the claim that
intelligent design doesn’t provide a specifically scientific explanation—that
the theory isn’t scientific at all.
A Matter of Definitions?
As a philosopher of science, I’ve always thought there was something odd
and even disingenuous about the objection that intelligent design is notscientific. The argument shifts the focus from an interesting question of truth
to a trivial question of definition. To say that an idea or theory does or does
not qualify as science implies an accepted definition of the term by which to
make that judgment. But to say that a claim about reality “is not science”
according to some definition says nothing about whether the claim is true—
unless it can be assumed that only scientific theories are true. A definition ofscience does not, by itself, tell us anything about the truth of competingstatements, but only how to classify them (whether as scientific or somethingelse, such as philosophical, historical, or religious statements).

So, at one level, I regarded the debate about whether intelligent design
qualifies as science as essentially a semantic dispute, one that distracts
attention from significant questions about what actually happened in the past
to cause life to arise. Does life exhibit evidence of intelligent design or justapparent design? Did life arise by undirected processes, or did a designing
intelligence play a role? Surely such questions are not settled by defining oneof the competing hypotheses as “unscientific” and then refusing to consider it.
At another level the debate is tacitly a debate about the basis of the theory
itself. Since the term “science” connotes a rigorous experimental or empiricalmethod for studying nature, denying that an idea is scientific implies thatrigorous empirical methods played no role in its formulation. To emphasize
this impression, many critics of intelligent design insist that the theory is not
testable and, for this reason, is neither rigorous nor scientific.8 Because many
people assume that only “the” scientific method produces justified
conclusions, the charge that the theory isn’t science seems to justify
dismissing it as merely a subjectively based opinion or belief. The objection“ID isn’t science” is code for “It isn’t true,” “It’s disreputable,” and “There isno evidence for it.”That is why the claim that intelligent design is not science—repeatedoften and with great presumed authority—has led many to reject it before
considering the evidence and arguments for it. I realized that in order to makemy case—and open minds to the evidence in favor of it—I needed to defend
the theory of intelligent design against this charge. To do so, indeed to defend
any theory against this charge and to do so with intellectual integrity, requires
one to navigate some treacherous philosophical waters. To claim that
intelligent design is science implicitly invokes a definition of science—someunderstanding of what science is. But which definition?
Because of my background, I knew that historians and philosophers ofscience—the scholars who study such questions—do not agree about how todefine science.9 Many doubt there is even a single definition that cancharacterize all the different kinds of science. In the philosophy of science
this is known as the “demarcation problem,” the problem of defining scienceand distinguishing (or “demarcating”) it from “pseudoscience,” metaphysics,
history, religion, or other forms of thought or inquiry.
Typically philosophers of science have tried to define science anddistinguish it from other types of inquiry (or systems of belief) by studying
the methods that scientists use to study nature. But that’s where the trouble
started. As historians and philosophers of science studied the methods thatscientists use, they realized that scientists in different fields use different
methods.
This, incidentally, is why historians and philosophers of science aregenerally better qualified to adjudicate the demarcation question thanscientific specialists, such as inorganic chemists, for example. As they say ofthe catcher in baseball, the philosopher and historian of science has a view ofthe whole field of play, meaning he or she is less likely to fall into the error ofdefining all of science by the practices used in one corner of the scientificworld. I already had some inkling of this from my work as a geophysicist. I
was aware that historical and structural geology use distinct (if partiallyoverlapping) methods. But as I delved into the demarcation question, I
discovered that different sciences use a wide variety of methods.
Some sciences perform laboratory experiments. Some do not. Some
sciences name, classify, and organize natural objects; some sciences seek todiscover natural laws; others seek to reconstruct past events. Some sciences
seek to formulate causal explanations of natural phenomena. Some provide
mathematical descriptions of natural phenomena. Some sciences constructmodels. Some explain general or repeatable phenomena by reference tonatural laws or general theories. Some study unique or particular events andseek to explain them by reference to past (causal) events.
Some sciences test their theories by making predictions; some test their
theories by assessing their explanatory power; some test their theories byassessing both explanatory power and predictive success. Some methods ofscientific investigation involve direct verification; some employ more indirect
methods of testing. Some test theories in isolation from competing
hypotheses. Some test theories by comparing the predictive or explanatory
success of competing hypotheses. Some branches of science formulate
conjectures that cannot yet be tested at all. Some sciences study only what canbe observed. Some sciences make inferences about entities that cannot be
observed. Some sciences reason deductively; some inductively; someabductively. Some use all three modes of inference. Some sciences use thehypothetico-deductive method of testing. Some use the method of multiplecompeting hypotheses.
This diversity of methods has doomed attempts to find a single definition(or set of criteria) that accurately characterizes all types of science byreference to their methodological practices. Thus, philosophers of science
now talk openly about the “demise” of attempts to demarcate or definescience by reference to a single set of methods.10
To say that an idea, theory, concept, inference, or explanation is or isn’tscientific requires a particular definition of science. Yet if different scientists
and philosophers of science could not agree about what the scientific method
is, how could they decide what did and did not qualify as science? And how
could I argue that the theory of intelligent design is scientific, if I could not
say what I meant by “science”? Conversely, how could critics of intelligent
design assert that intelligent design is not science without articulating thestandard by which they made this judgment? How could any headway in thisdebate be made without an agreed-upon definition?
I discovered that though it was difficult to define science by reference to asingle definition or set of methodological criteria, it was not difficult to definescience in such a way that either acknowledged the diversity ofmethodological practices or refused to specify which method made a
discipline scientific. Such an approach allows science to be defined more
broadly as, for instance, “a systematic way of studying nature involving
observation, experimentation, and/or reasoning about physical phenomena.”
So far, so good. The difficulty has come when scholars tried to equate science
with a particular systematic method of studying nature to the exclusion ofother such methods.
The situation was not hopeless, however. I discovered that although it was
impossible to describe the rich variety of scientific methods with a single
definition, it was possible to characterize the methodological practices of
specific disciplines or types of science. This made sense. It was precisely thediversity of scientific methods that made defining science as a whole difficultin the first place. Focusing on a single established scientific method as therelevant standard of judgment eliminated the practical problem of deciding
how to assess the scientific status of a theory without an established definitionof science. Furthermore, from my own studies, I knew the methodologicalpractices of the sciences directly relevant to the questions I was pursuing—the
sciences that investigate the causes of particular events in the remote past.
Stephen Jay Gould called these sciences the historical sciences.11 I knew thatthe inference to design followed from a rigorous application of the logical andmethodological guidelines of these disciplines. As shown in Chapter 15, I
carefully followed these guidelines in constructing my own case for design,
so I concluded that there was a good (if definition-dependent) reason toregard intelligent design as a scientific—and, specifically, historicallyscientific—theory. In fact, there are several such reasons.
Reason 1: The Case for ID Is Based on Empirical Evidence
The case for intelligent design, like other scientific theories, is based uponempirical evidence, not religious dogma. Contrary to the claims of RobertPennock,12 one of the expert witnesses in the Dover trial, design theorists
have developed specific empirical arguments to support their theory. In thisbook, to name just one example, I have developed an argument for intelligent
design based on the discovery of digital information in the cell. In addition,other scientists now see evidence of intelligent design in the “irreducible
complexity” of molecular machines and circuits in the cell,13 the pattern ofappearance of the major groups of organisms in the fossil record,14 the finetuning of the laws and constants of physics,15 the fine-tuning of our terrestrial
environment,16 the information-processing system of the cell, and even in thephenomenon known as “homology” (evidence previously thought to provide
unequivocal support for neo-Darwinism).17 Critics may disagree with theconclusions of these design arguments, but they cannot reasonably deny thatthey are based upon commonly accepted observations of the natural world.
Since the term “science” commonly connotes an activity in which theories aredeveloped to explain observations of the natural world, the empirical,
observational basis of the theory of intelligent design provides a good reason
for regarding intelligent design as a scientific theory.
Reason 2: Advocates of ID Use Established Scientific Methods
The case for intelligent design follows from the application of not one, but
two separate systematic methods of scientific reasoning—methods that
establish criteria for determining when observed evidence supports ahypothesis. In Chapter 15, I discussed the primary method, the method ofmultiple competing hypotheses, by which I inferred and justified the inference
to intelligent design as the best explanation for the origin of biologicalinformation. As noted there, this method is a standard method of scientificreasoning in several well-established scientific disciplines. I discuss in moredetail below how this method makes it possible to test intelligent design.
Additionally, and as discussed in Chapter 16, advocates of intelligent design
have developed another method that complements the method of multiplecompeting hypotheses.
In The Design Inference (and in subsequent works), William Dembski
established criteria by which intelligently designed systems can be identified
by the kinds of patterns and probabilistic signatures they exhibit. On the basisof these criteria, Dembski developed a comparative evaluation procedure—
his explanatory filter18—to guide our analysis and reasoning about naturalobjects and artifacts and to help investigators decide among three different
types of explanations: chance, necessity, and design.19 As such, it constitutes
a rigorous, systematic, evidence-based method for detecting the effects ofintelligence, again suggesting a good reason to regard intelligent design as
scientific in accord with common definitions of the term.
Reason 3: ID Is a Testable Theory
Most scientists and philosophers of science think that the ability to subject

theories to empirical tests constitutes an important aspect of any scientificmethod of study. But for a theory to be testable, there must be some evidential
grounds by which it could be shown to be incorrect or inadequate. And,
contrary to the repeated claims of its detractors, the theory of intelligent
design is testable. In fact, it is testable in several interrelated ways.
First, like other scientific theories concerned with explaining events in the
remote past, intelligent design is testable by comparing its explanatory power
to that of competing theories. Darwin used this method of testing in On theOrigin of Species. In my presentation of the case for intelligent design in
Chapters 8 through 16, I tested the theory in exactly this way by comparing
the explanatory power of intelligent design against that of several otherclasses of explanation. That the theory of intelligent design can explain theorigin of biological information (and the origin of the cell’s interdependent
information-processing system) better than its materialistic competitors showsthat it has passed an important scientific test.
This comparative process is not a hall of mirrors, a competition without
an external standard of judgment. The theory of intelligent design, like theother historical scientific theories it competes against, is tested against ourknowledge of the evidence in need of explanation and our knowledge of thecause-and-effect structure of the world. As noted in Chapters 7 and 15,
evaluations of “causal adequacy” guide historical scientific reasoning andhelp to determine which hypothesis among a competing group of hypotheses
has the best explanatory power. Considerations of causal adequacy provide anexperience-based criterion by which to test—accept, reject, or prefer—competing historical scientific theories. When such theories cite causes thatare known to produce the effect in question, they meet the test of causal
adequacy; when they fail to cite such causes, they fail to meet this test. To
adapt my example from Chapter 7, the earthquake hypothesis fails the test of
causal adequacy because we do not have evidence that earthquakes cause
layers of volcanic ash to accumulate, whereas the volcanic eruption
hypothesis passes the test of causal adequacy because experience has shownthat eruptions do cause this phenomenon.20
Since empirical considerations provide grounds for rejecting historicalscientific theories or preferring one theory over another, such theories areclearly testable. Like other historical scientific theories, intelligent design
makes claims about the cause of past events, thus making it testable against
our knowledge of cause and effect. Moreover, because experience shows thatan intelligent agent is not only a known, but also the only known cause of
specified, digitally encoded information, the theory of intelligent design
developed in this book has passed two critical tests: the tests of causal
adequacy and causal existence (see Chapter 15). Precisely because intelligent
design uniquely passed these tests, I argued that it stands as the best

explanation of the DNA enigma.
Finally, though historical scientific theories typically do not makepredictions that can be tested under controlled laboratory conditions, they dosometimes generate discriminating predictions about what we should find in
the natural world—predictions that enable scientists to compare them to otherhistorical scientific theories. The theory of intelligent design has generated anumber of such discriminating empirical predictions. These predictions not
only distinguish the theory of intelligent design from competing evolutionary
theories; they also serve to confirm the design hypothesis rather than its
competitors.
Consider the case of so-called junk DNA—the DNA that does not codefor proteins found in the genomes of both one-celled organisms andmulticellular plants and animals. The theory of intelligent design andmaterialistic evolutionary theories (both chemical and biological) differ intheir interpretation of so-called junk DNA. Since neo-Darwinism holds thatnew biological information arises as the result of a process of mutational trial
and error, it predicts that nonfunctional DNA would tend to accumulate in the
genomes of eukaryotic organisms (organisms whose cells contain nuclei).
Since most chemical evolutionary theories also envision some role for chanceinteractions in the origin of biological information, they imply that
nonfunctional DNA would have similarly accumulated in the first simple(prokaryotic) organisms—as a kind of remnant of whatever undirected
process first produced functional information in the cell. For this reason, mostevolutionary biologists concluded upon the discovery of nonprotein-coding
DNA that such DNA was “junk.” In their view, discovery of the nonproteincoding regions confirmed the prediction or expectation of naturalistic
evolutionary theories and disconfirmed an implicit prediction of intelligent
design.
As Michael Shermer argues, “Rather than being intelligently designed, thehuman genome looks more and more like a mosaic of mutations, fragmented
copies, borrowed sequences, and discarded strings of DNA that were jerrybuilt over millions of years of evolution.”21 Or as Ken Miller argues: “Thecritics of evolution like to say that the complexity of the genome makes it
clear that it was designed…. But there’s a problem with that analysis, and it’sa serious one. The problem is the genome itself: it’s not perfect. In fact, it’s
riddled with useless information, mistakes, and broken genes…. Molecularbiologists actually call some of these regions ‘gene deserts,’ reflecting theirbarren nature.”22 Or as philosopher of science Philip Kitcher puts it, “If you
were designing the genomes of organisms, you would not fill them up withjunk.”23
ID advocates advance a different view of nonprotein-coding DNA.24 Thetheory of intelligent design predicts that most of the nonprotein-coding
sequences in the genome should perform some biological function, even if
they do not direct protein synthesis. ID theorists do not deny that mutational
processes might have degraded or “broken” some previously functional DNA,but we predict that the functional DNA (the signal) should dwarf thenonfunctional DNA (the noise), and not the reverse. As William Dembski
explained and predicted in 1998: “On an evolutionary view we expect a lot of
useless DNA. If, on the other hand, organisms are designed, we expect DNA,as much as possible, to exhibit function.”25 The discovery in recent years that
nonprotein-coding DNA performs a diversity of important biological
functions has confirmed this prediction. It also decisively refutes prominent
critics of intelligent design—including Shermer, Miller, and Kitcher—who
have continued to argue (each as recently as 2008) that the genome iscomposed of mostly useless DNA.26
Contrary to their claims, recent scientific discoveries have shown that thenonprotein-coding regions of the genome direct the production of RNA
molecules that regulate the use of the protein-coding regions of DNA. Celland genome biologists have also discovered that these supposedly “useless”
nonprotein-coding regions of the genome: (1) regulate DNA replication,27 (2)regulate transcription,28 (3) mark sites for programmed rearrangements ofgenetic material,29 (4) influence the proper folding and maintenance ofchromosomes,30 (5) control the interactions of chromosomes with the nuclear
membrane (and matrix),31 (6) control RNA processing, editing, andsplicing,32 (7) modulate translation,33 (8) regulate embryologicaldevelopment,34 (9) repair DNA,35 and (10) aid in immunodefense or fighting
disease36 among other functions. In some cases, “junk” DNA has even beenfound to code functional genes.37 Overall, the nonprotein-coding regions of
the genome function much like an operating system in a computer that candirect multiple operations simultaneously.38 Indeed, far from being “junk,” asmaterialistic theories of evolution assumed, the nonprotein-coding DNA
directs the use of other information in the genome, just as an operating system
directs the use of the information contained in various application programs
stored in a computer. In any case, contrary to the often heard criticism that thetheory makes no predictions, intelligent design not only makes adiscriminating prediction about the nature of “junk DNA”; recent discoveries
about nonprotein-coding DNA confirm the prediction that it makes.39
Appendix A describes several other discriminating predictions that the theoryof intelligent design makes.
Reason 4: The Case for ID Exemplifies Historical Scientific ReasoningThere is another good—if convention-dependent—reason for classifying
intelligent design as a scientific theory, one already hinted at in the previous
section on testability and explored at length in Chapter 15. Not only do
scientists use systematic methods to infer intelligent design; the specificmethods they use conform closely to established patterns of inquiry in thehistorical sciences. Indeed, the theory of intelligent design and the patterns ofreasoning used to infer and defend it exemplify each of the key features of ahistorical science.
During my doctoral studies I discovered several distinctive characteristics
of historical scientific disciplines—disciplines that try to reconstruct the pastand explain present evidence by reference to past causes rather than trying toclassify or explain unchanging laws and properties of nature (see Chapter 7). I
found that historical sciences generally can be distinguished fromnonhistorical scientific disciplines by reference to four criteria. And thetheory of intelligent design (and the modes of inference used to establish andtest it) provides a good example of each of the key features of a historicalscience.
A Distinctive Historical Objective
Historical sciences focus on questions of the form, “What happened?” or“What caused this event or that natural feature to arise?” rather than questionsof the form, “How does nature normally operate or function?” or “Whatcauses this general phenomenon to occur?”40 Those who postulate the past
activity of an intelligent designer do so as an answer, or a partial answer, todistinctively historical questions. The theory of intelligent design attempts toanswer a question about what caused certain features in the natural world tocome into existence—such as the digitally encoded, specified information
present in the cell. It attempts to answer questions of the form “How did thisnatural feature arise?” as opposed to questions of the form “How does naturenormally operate or function?”
A Distinctive Form of Inference
The historical sciences use inferences with a distinctive logical form. Unlike
many nonhistorical disciplines, which typically infer generalizations or laws
from particular facts (induction), historical sciences employ abductive logic toinfer a past event from a present fact or clue. Such inferences are also called“retrodictive.” As Gould put it, the historical scientist infers “history from its
results.”41
Inferences to intelligent design exemplify this abductive and retrodictive

logical structure. They infer a past unobservable cause (in this case, an
instance of creative mental action or agency) from present facts or clues in thenatural world, such as the specified information in DNA, the irreducible
complexity of certain biological systems, and the fine-tuning of the laws andconstants of physics.42
A Distinctive Type of Explanations
Historical sciences usually offer causal explanations of particular events, notlawlike descriptions or theories describing how certain kinds of phenomena—such as condensation or nuclear fission—generally occur. In historicalexplanations, past causal events, not laws or general physical properties, dothe main explanatory work.43 To explain a dramatic erosional feature ineastern Washington called the Channeled Scablands, a historical geologist
posited an event: the collapse of an ice dam and subsequent massive flooding.
This and other historical scientific explanations emphasize past events ascauses for subsequent events and/or present features of the world.
The theory of intelligent design offers such a distinctively historical formof explanation. Theories of design invoke the act or acts of an agent andconceptualize those acts as causal events, albeit ones involving mental rather
than purely physical entities. Advocates of design postulate past causal events(or a sequence of events) to explain the origin of present evidence or clues,
just as proponents of chemical evolutionary theories do.
Use of the Method of Multiple Competing Hypotheses
Historical scientists do not mainly test hypotheses by assessing the accuracyof the predictions they make under controlled laboratory conditions. Using themethod of multiple competing hypotheses, historical scientists test hypothesesby comparing their explanatory power against that of their competitors. And
advocates of the theory of intelligent design use this method (as I have donein this book). (For more on this, see Chapter 15.)
In sum, the theory of intelligent design seeks to answer characteristicallyhistorical questions, it relies upon abductive/retrodictive inferences, itpostulates past causal events as explanations of present evidence, and it istested indirectly by comparing its explanatory power against that ofcompeting theories. Thus, the theory of intelligent design exhibits each of themain features of a historical science, suggesting another reason to regard it as
scientific.
Reason 5: ID Addresses a Specific Question in Evolutionary Biology

There is another closely related reason to regard intelligent design as ascientific theory. It addresses a key question that has long been part ofhistorical and evolutionary biology: How did the appearance of design inliving systems arise? As noted in Chapter 1, both Darwin and contemporary
evolutionary biologists such as Francisco Ayala, Richard Dawkins, andRichard Lewontin acknowledge that biological organisms appear to havebeen designed.44 Nevertheless, for most evolutionary theorists, theappearance of design is considered illusory, because they are convinced thatthe mechanism of natural selection acting on random variations (and/or othersimilarly unguided mechanisms) can fully account for the appearance ofdesign in living organisms.45
In On the Origin of Species, Darwin sought to show that natural selection
has creative powers comparable to those of intelligent human breeders. In
doing so, he sought to refute the design hypothesis by providing amaterialistic explanation for the origin of the appearance of design in livingorganisms. Following Aleksandr Oparin, chemical evolutionary theorists havesought to provide similarly materialistic accounts for the appearance of designin the simplest living cells.
Is the appearance of design in biology real or illusory? Clearly, there are
two possible answers to this question. Neo-Darwinism and chemical
evolutionary theory provide one answer, and competing theories of intelligent
design provide an opposite answer. By almost all accounts the classicalDarwinian answer to this question—“The appearance of design in biology
does not result from actual design”—has long been considered a scientificproposition. But what is the status of the opposite answer? If the proposition“Jupiter is made primarily of methane gas” is a scientific proposition, then the
proposition “Jupiter is not made primarily of methane gas” would seem to bea scientific proposition as well. The negation of a proposition does not makeit a different type of claim. Similarly, the claim “The appearance of design inbiology does not result from actual design” and the claim “The appearance ofdesign in biology does result from actual design” are not two different kindsof propositions; they are two different answers to the same question, aquestion that has long been part of evolutionary biology and historicalscience. If one of these propositions is scientific, then it would seem that theother is scientific as well.46
Reason 6: ID Is Supported by Peer-Reviewed Scientific Literature
Critics of the theory of intelligent design often claim that its advocates havefailed to publish their work in peer-reviewed scientific publications. For thisreason, they say the theory of intelligent design does not qualify as a scientifictheory.47 According to these critics, science is what scientists do. Since ID
scientists don’t do what other scientists do—namely, publish in peer-reviewed
journals—they are not real scientists and their theory isn’t scientific either.
Critics of the theory of intelligent design made this argument before andduring the Dover trial in support of the ACLU’s case against the Doverschool-board policy. For example, Barbara Forrest, a philosophy professor
from Southeastern Louisiana State University and one of the expert witnesses
for the ACLU, asserted in a USA Today article before the trial that design
theorists “aren’t published because they don’t have any scientific data.”48 In
her expert witness report in support of the ACLU, Forrest also claimed that“there are no peer-reviewed ID articles in which ID is used as a biologicaltheory in mainstream scientific databases such as MEDLine.”49 Judge Jones
apparently accepted such assertions at face value. In his decision, he stated
not once, but five separate times, that there were no peer-reviewed scientificpublications supporting intelligent design.50
But Dr. Forrest’s carefully qualified statement gave an entirely misleading
impression. In 2004, a year in advance of the trial, I published a peerreviewed scientific article advancing the theory of intelligent design in amainstream scientific journal. As I mentioned in the Prologue, the publicationof the article evoked a huge backlash at the Smithsonian Institution, where thejournal, The Proceedings of the Biological Society of Washington, waspublished. Moreover, controversy about the editor’s decision and his
subsequent treatment spilled over into both the scientific and the mainstreampress, with articles about it appearing in Science, Nature, the Wall Street
Journal, and the Washington Post among other places.51 Both Dr. Forrest andJudge Jones had every opportunity to inform themselves about the existenceof at least one peer-reviewed scientific article in support of intelligent design.
In any case, as my institute informed the court in an amicus curiae (friendof the court) brief, my article was by no means the only peer-reviewed or
peer-edited scientific publication in support of the theory of intelligent
design.52 By 2005, scientists and philosophers advocating the theory ofintelligent design had already developed their theory and the empirical casefor it in peer-reviewed scientific books published both by trade presses53 andby university presses.54 Michael Behe’s groundbreaking Darwin’s Black Box
was published by the Free Press in New York. William Dembski’s The Design
Inference was published by Cambridge University Press. Both were peerreviewed. In addition, design proponents have also published scientificarticles advancing the case for intelligent design in peer-reviewed scientificbooks and anthologies published by university presses55 and in scientificconference proceedings published by university presses and trade presses.56

Advocates of intelligent design have also published work advancing their
theory in peer-reviewed philosophy of science journals and other relevantinterdisciplinary journals.57 Moreover, since the publication of my article in2004, several other scientific articles supporting intelligent design (ordescribing research guided by an ID perspective) have been published inmainstream peer-reviewed scientific journals.58
Of course, critics of intelligent design may still judge that the number ofpublished books and articles supporting the theory does not yet make it
sufficiently mainstream to warrant teaching students about it. Perhaps.59 But
that is a judgment about educational policy distinct from deciding thescientific status, or still less, the merits of the theory of intelligent design
itself. Clearly, there is no magic number of supporting peer-reviewed
publications that suddenly confers the adjective “scientific” on a theory; nor isthere a tribunal vested with the authority to make this determination. If there
were a hard-and-fast numerical standard as low as even one, no new theory
could ever achieve scientific status. Each new theory would face an
impossible catch-22: for a new theory to be considered “scientific” it musthave appeared in the peer-reviewed scientific literature, but anytime ascientist submitted an article to a peer-reviewed science journal advocating a
new theory, it would have to be rejected as “unscientific” on the grounds thatno other peer-reviewed scientific publications existed supporting the newtheory.
Critics of intelligent design have actually used a similarly circular kind ofargument to claim that ID is not science. Before 2004, critics argued that thetheory of intelligent design was unscientific, because there were no publishedarticles supporting it in peer-reviewed scientific journals (ignoring the various
peer-reviewed books that existed in support of ID). Then once a peerreviewed scientific journal article was published supporting intelligent design,
critics claimed that the article should not have been published, because thetheory of intelligent design is inherently unscientific.60 Indeed, critics accused
the editor who published my article of editorial malfeasance, because they
thought he should never have considered sending the article out for peer
review in the first place.61 Why? Because, according to these critics, theperspective of the article should have immediately disqualified it fromconsideration. In short, critics argued that “intelligent design is not scientificbecause peer-reviewed articles supporting the theory have not been
published” and that “peer-reviewed articles supporting intelligent design
should not be published because the theory is not scientific,” apparently neverrecognizing the patent circularity of this self-serving, exclusionary logic.
Logically, the issue of peer review is a red herring—a distracting
procedural side issue. The truth of a theory is not determined or guaranteed by

the place of, or procedures followed, in its publication.62 As noted in Chapter
6, many great scientific theories were first advanced and published without
undergoing formal peer review. Though modern peer-review procedures often
do a good job of catching and correcting factual mistakes, they also canenforce ideological conformity, stifle innovation, and resist novel theoreticalinsights. Scientific experts can make mistakes in judgment and, being human,
they sometimes reject good new ideas because of prejudicial attachments toolder, more familiar ones. The history of science is replete with examples of
established scientists summarily dismissing new theories that later proved
able to explain the evidence better than previously established theories. In
such situations, proponents of new theories have often found traditionalorgans of publication closed to them. Thus, it is neither surprising nor
damning to intelligent design that currently many scientific journals are
implacably opposed to publishing articles supporting the theory.
Yet if science is what scientists do, and if publishing peer-reviewed
scientific books and articles is part of what scientists do that makes their
theories scientific (as critics of ID assert), then there is another good,
convention-dependent reason to regard intelligent design as scientific. The
scientists who have developed the case for intelligent design have begun toovercome the prejudice against their ideas and have published their work inpeer-reviewed scientific journals, books, conference volumes, andanthologies.63
Conclusion
As I examined the question of whether intelligent design qualified as ascientific theory, it was clear to me that the answer to this question depended
upon the definition of science chosen to decide the question. But as I
considered both common definitions of science and what I had learned about
the specialized methodological practice of the historical sciences, it seemed
equally clear that there were many good—if definition-dependent—reasonsfor considering intelligent design as a scientific theory.
But maybe there was some other, better definition of science that I shouldhave considered. Perhaps there was some specific feature of a scientifictheory that intelligent design did not possess, or some specific criterion ofscientific practice that its advocates did not follow. I knew that the theory ofintelligent design met the criterion of testability, despite what many critics ofthe theory asserted, but perhaps there were other criteria that it could not
meet. If so, then perhaps these definitional criteria would establish a goodreason for disqualifying intelligent design from consideration as science after
all. Certainly, many critics of intelligent design have argued that the theory

lacks many key features of a bona fide scientific theory—that it fails to meetcriteria by which science could be defined and distinguished from nonscience,
metaphysics, or religion. In the next chapter, I examine why critics of thetheory—including the judge in the Dover case—have insisted that, despite thearguments developed in this chapter, intelligent design does not qualify as ascientific theory.

19
Sauce for the Goose

In 1992, the year after I received my Ph.D., I was invited to an academic
conference to respond to a paper by Michael Ruse, a well-known British
philosopher of science. Ruse had long ago made his reputation as a prolificdefender of Darwinian evolution and an archnemesis of young-earth
creationism, the idea that God created the world roughly in its present form
between six and ten thousand years ago. In 1981, in Mclean v. Arkansas
Board of Education, opponents of creationism, represented by attorneys
working for the American Civil Liberties Union (ACLU), sued the state of
Arkansas, arguing that a law that required teachers to teach creationism
alongside Darwinian evolution in public-school science classrooms was
unconstitutional. Ruse testified for the ACLU against the Arkansas law.1
In his Arkansas testimony, Ruse did not just argue that creation science(as it was called in the statute)2 was wrong. Instead, he argued that it did not
qualify as a scientific theory by definition. To make this case, he offered afivefold definition of science. According to Ruse, to qualify as scientific atheory must be: (1) guided by natural law, (2) explanatory by reference tonatural law, (3) testable against the empirical world, (4) tentative in its
conclusions, and (5) falsifiable.3 He argued that these demarcation criteria
could distinguish science from pseudoscience, metaphysics, and religion, andthat creation science failed to meet them.
Now, nine years later, Ruse was turning his attention to intelligent design
at a conference convened to assess the merits of this (then) new theory of
biological origins. Having just started my academic career, I found theprospect of critiquing such a well-known figure in my field rather daunting.
But as I read the advance copy of Ruse’s essay coming off the fax machine in
our college library, my concern subsided. Ruse was making a demarcationargument based upon a conception of science that I had already refuted in my
Ph.D. thesis. I knew his argument didn’t work and knew what I needed to say.

Figure 19.1. Professor Michael Ruse. Courtesy of Ray Stanyard, Florida State
University and Review Magazine.
Ruse was, at this point, completely unaware of my work characterizing
the methods of the historical sciences. So he didn’t know why I thought that
intelligent design provided a good example of a historical scientific theory.
Instead, he had his own definition of science in mind—the one he had
promulgated in the Arkansas trial.
In the trial, Ruse had used this definition of science to define creationism
as an unscientific or “pseudoscientific” idea. Now, in 1992, he wanted to use
one prong of his definition—one of his demarcation criteria—to argue that
intelligent design did not qualify as a scientific theory either.
In philosophy, such negative arguments are often called “defeaters,”
because they are intended to “defeat” or refute a positive argument for a given
claim. My encounter with Professor Ruse made me aware that there was ahost of such proposed defeaters. I realized that I needed to tackle thesenegative arguments head-on if I was going to open minds to the strong
evidential case for intelligent design.
In the previous chapter, I enumerated several positive—if definitiondependent—reasons for considering intelligent design a scientific theory. In
this chapter, I respond to several of the most common demarcation argumentsthat have been used to deny or “defeat” its scientific status. I show why thesearguments don’t do the work that critics of intelligent design suppose—why
they don’t establish that intelligent design is any more or less scientific thanrival evolutionary theories and why they don’t provide good reasons fortreating intelligent design as inherently unscientific.
So, “Is intelligent design scientific?” remains the topic of this chapter, but
now the context of the discussion shifts. Think of it as a trial before the courtof reason (not to be confused, as we will see, with Judge Jones’s court). In
Chapter 18, I gave an opening argument for the scientific status of intelligent

design by appealing to various definitions of science, the most important of
which was derived from a study of the methods of the historical sciences.
Here I defend intelligent design against various defeaters, in particular,arguments that purport to establish that intelligent design does not qualify as ascientific theory.
At the close of this chapter, I’ll return to central Pennsylvania to take acloser look at the trump card—the supposedly ultimate defeater—that Judge
Jones played to justify his much publicized 2005 ruling against intelligent
design. Before I do, I need to start where my own thinking about thesenegative demarcation arguments began, back in 1992, at a conference on thecampus of Southern Methodist University in an encounter with the eminent
Professor Ruse.
Intelligent Design and Explanation by Natural Law
At the conference, Ruse made one of the most common demarcationarguments against intelligent design. He argued that science must assume that“there are no powers, seen or unseen, that interfere with or otherwise makeinexplicable the normal working of material objects.”4 Since the scientificenterprise is characterized by a commitment to “unbroken regularity” or“unbroken law,”5 scientific theories must explain events or phenomena byreference to natural laws.6 And since intelligent design invokes an event—the
conscious activity of a designing agent—rather than a law of nature to explain
the origin of biological form and information, Ruse argued that it wasscientifically “inappropriate.” Ruse also seemed to think that if an intelligent
designer had acted during the history of life, then its actions would havenecessarily violated the laws of nature, since intelligent agents typicallyinterfere with the otherwise “normal workings of material objects.” Since, for
Ruse, the activity of an intelligent designer violates the laws of nature,
positing such activity—rather than a law—would violate the rules of science.
In response,7 I pointed out that the activity of a designing intelligence
does not necessarily break or violate the laws of nature. Human agents design
information-rich structures and otherwise interfere with the “normal workingsof material objects” all the time. When they do, they do not violate the laws ofnature; they alter the conditions upon which the laws act. When I arranged themagnetic letters on my metallic chalkboard to spell the message “Biology
Rocks!” I altered the way in which matter is configured, but I did not alter orviolate the laws of electromagnetism. When agents act, they initiate new
events within an existing matrix of natural law without violating those laws.
(For a quick definition of a law of nature and a primer on the difference
between a law and an event, see this note.)8

I also pointed out that Ruse’s key demarcation criterion, if applied strictly,
cut just as much against Darwinian and chemical evolutionary (and manyother scientific) theories as it did against intelligent design. I showed, forexample, that natural laws often describe, but do not explain natural
phenomena. Newton’s law of universal gravitation described, but did not
explain, what caused gravitational attraction. A strict application of Ruse’s
second criterion would therefore imply that Newton’s law of gravity had been
“unscientific,” since it did not offer an explanation by natural law.
I also showed that many historical scientific theories do not offer an
explanation by natural law. Instead, they postulate past events (or patterns ofevents) to explain other past events as well as presently observable evidence.
Historical theories explain mainly by reference to events or causes, not laws.
For example, if a historical geologist seeks to explain what caused theunusual height of the Himalayas, he or she will cite particular events orfactors that were present in the case of the Himalayan mountain-building
episode that were not present in other such episodes. Knowing the laws ofphysics that describe the forces at work in all mountain-building events will
not aid the geologist in accounting for the contrast between the Himalayas
and other mountain ranges. To explain what caused the Himalayas to rise tosuch heights, the geologist does not need to cite a general law, but instead
evidence of a distinctive set of past events or conditions.9 Evolutionary
theories, in particular, often emphasize the importance of past events in their
explanations.10 For example, Aleksandr Oparin’s chemical evolutionary
theory postulated a series of events (a scenario), not a general law, in order toexplain how the first living cells arose.
Of course, past events and historical scenarios are assumed to take place
in a way that obeys the laws of nature. Moreover, our knowledge of causeand-effect relationships (which we can sometimes formulate as laws) willoften guide the inferences that scientists make about what happened in thepast and will influence their assessment of the plausibility of competing
historical scenarios and explanations. Even so, many historical scientifictheories make no mention of laws at all. Laws, at best, play only a secondaryrole in historical scientific theories. Instead, events play the primary
explanatory role.
In my response to Ruse, I pointed out that the theory of intelligent design
exemplified the same style of scientific explanation as other historicalscientific theories. Intelligent design invoked a past event—albeit a mental
event—rather than a law to explain the origin of life and the complexity of thecell. As in other historical scientific theories, our knowledge of cause andeffect (“Information habitually arises from conscious activity”) supports theinference to design. A law (conservation of information) also helps to justify

the inference of an intelligent cause as the best explanation. Advocates ofintelligent design use a law (“Complex specified information always arises
from an intelligent source in a nonbiological context”) to infer a past causal
event, the act of a designing mind. But that act or event explains the evidence
in question. Though laws play a subsidiary role in the theory, a past event (orevents) explains the ultimate origin of biological information.
But if explaining events primarily by reference to prior events, rather thanlaws, does not disqualify other historical scientific theories, including
evolutionary theories, from consideration as science, then by the same logic itshould not disqualify the theory of intelligent design either. Oddly, in a
discussion of population genetics—part of the explanatory framework ofcontemporary Darwinian theory—Ruse himself noted that “it is probably a
mistake to think of modern evolutionists as seeking universal laws at work inevery situation.”11 But if laws can play no role or only a subsidiary role inother historical theories, then why was it “inappropriate” for a law to play
only a supportive role in the theory of intelligent design?
Conversely, if invoking a past event, rather than a law, made intelligent
design unscientific, then by the same token it should make materialistic
evolutionary theories unscientific as well. Either way, I concluded that Ruse’s
key criterion for scientific status did not provide a basis for discriminating thescientific status of the two types of theories. Both were equivalent in theircapacity to meet Ruse’s definitional standard.
Ruse’s reaction to my critique of his paper surprised me. On the podium
as we discussed our differences, he seemed genuinely interested in how I had
come to my position and curious to understand it. When I talked with him
privately afterward, I found him to be genial and kindly. He offered me somewell-intended career advice and asked me about people that we both knew inCambridge. During our private conversation he also shocked me by admitting
his own reservations about the validity of using demarcation arguments tosettle the debate about biological origins. The following year in a muchpublicized talk before the American Association for the Advancement of
Science, he made some of these doubts public (see below).
Defeaters Defeated
Encouraged by my discussion with Professor Ruse, I began to examine the“intelligent design isn’t science” objection in more detail. As I did, I graduallycame to a radical conclusion: not only were there many good—if conventiondependent—reasons for classifying intelligent design as a historical scientifictheory (as I had concluded based upon my doctoral research), but there were
no good—non–question begging—reasons to define intelligent design asunscientific.
Typically those who argued that “intelligent design isn’t science” invokedvarious demarcation criteria, as Ruse had done in the Arkansas trial. Since myconversation with Michael Ruse, I have encountered numerous sucharguments. Critics claim that intelligent design does not qualify as a scientifictheory because: (1) it invokes an unobservable entity,12 (2) it is not testable,13(3) it does not explain by reference to natural law,14 (4) it makes no
predictions,15 (5) it is not falsifiable,16 (6) it cites no mechanisms,17 and (7) it
is not tentative.18
As I studied these arguments I discovered a curious pattern. Invariably, if
the critics applied their definitional criteria—such as observability, testability,or “must explain by natural law”—in a strict way, these criteria not only
disqualified the design hypothesis from consideration as science; they also
disqualified its chief rivals—other historical scientific theories—each ofwhich invoked undirected evolutionary processes.
Conversely, I discovered that if these definitional criteria were applied ina less restrictive way—perhaps one that took into account the distinctive
historical aspects of inquiry into the origin of life—then these criteria not only
established the scientific bona fides of various rivals of intelligent design;
they confirmed the scientific status of the design hypothesis as well. In nocase, however, did these demarcation criteria successfully differentiate thescientific status of intelligent design and its competitors. Either science wasdefined so narrowly that it disqualified both types of theory, or it was definedso broadly that the initial reasons for excluding intelligent design (or its
competitors) evaporated. If one theory met a specific criterion, then so did theother; if one theory failed to do so, then the rival theory did so as well—provided the criteria were applied in an evenhanded and non–question
begging way. Intelligent design and its materialistic rivals were equivalent intheir ability to meet various demarcation criteria or methodological norms. (Ilater coined the not so catchy phrase “methodological equivalence” todescribe how these competing theories compared in their ability to measure
up to various demarcation criteria.) Given this equivalence, and given thatmaterialistic evolutionary theories were already widely regarded as scientific,
I couldn’t see any reason to classify intelligent design as unscientific. The
defeaters didn’t work.
Because these “defeaters” are used against intelligent design all the time,it’s important to see why they fail. So, in what follows, I examine some
additional demarcation arguments that are commonly used against intelligent
design. (I don’t provide in these pages an exhaustive demonstration of theequivalence I discovered. That would require a book in itself and a level ofdetail that only philosophers of science would happily stomach. But thoseinterested in a more detailed analysis of other demarcation arguments against
intelligent design might consult the Web site for this book and the referencesin this note.19)
Observability
During the controversy over the treatment of Dean Kenyon at San FranciscoState University, I noticed his critics using a second common demarcationargument against ID. After the biology department removed Kenyon fromteaching his biology class, some of Kenyon’s colleagues argued that thetheory of intelligent design did not qualify as a scientific theory because it
invoked an unobservable entity, in particular, an unseen designing
intelligence. In making this argument, Kenyon’s colleagues assumed thatscientific theories must invoke only observable entities. Since Kenyon
discussed a theory that violated this convention, they insisted that neither thetheory he discussed, nor he himself, belonged in the biology classroom.20
Others who defended the action of the biology department, such as
Eugenie Scott of the National Center for Science Education, used a similarrationale. She insisted that the theory of intelligent design violated the rules ofscience because, “you can’t put an omnipotent deity in a test tube (or keep it
out of one).”21 At the conference at SMU where I had met Ruse the year
before, I also encountered this complaint about the theory of intelligent
design. There, molecular biologist Fred Grinnell argued that intelligent design
can’t be a scientific concept, because if something “can’t be measured, orcounted, or photographed, it can’t be science.”22 According to these critics ofintelligent design, the unobservable character of a designing intelligence
renders it inaccessible to empirical investigation and, therefore, makes it
unscientific.
But was that really the case? Does a reference to an unobservable entity
provide a good reason for defining a theory as unscientific? Does mypostulation of an unobservable intelligence make my case for intelligent
design unscientific?
The answer to that question depends, again, upon how science is defined.
If scientists (and all other relevant parties) decide to define science as anenterprise in which scientists can posit only observable entities in their
theories, then clearly the theory of intelligent design would not qualify as ascientific theory. Advocates of intelligent design infer, rather than directly
observe, the designing intelligence responsible for the digital information inDNA.
But it didn’t take me long to realize that this definition of science would
render many other scientific theories, including many evolutionary theories ofbiological origins, unscientific by definition as well. Many scientific theories
infer or postulate unobservable entities, causes, and events. Theories ofchemical evolution invoke past events as part of the scenarios they use to
explain how the modern cell arose. Insofar as these events occurred millionsof years ago, they are clearly not observable today. Darwinian biologists, fortheir part, have long defended the putatively unfalsifiable nature of theirclaims by reminding critics that many of the creative processes to which theyrefer occur at rates too slow to observe in the present and too fast to havebeen recorded in the fossil record. Further, the existence of many transitional
intermediate forms of life, the forms represented by the nodes on Darwin’s
famous branching tree diagram, are also unobservable.23 Instead,
unobservable transitional forms of life are postulated to explain observablebiological evidence—as Darwin himself explained. But how is this different
from postulating the past activity of an unobservable designing intelligence toexplain observable features of the living cell? Neither Darwinian transitional
forms, neo-Darwinian mutational events, the “rapid branching” events ofStephen Jay Gould’s theory of punctuated equilibrium, the events comprisingchemical evolutionary scenarios, nor the past action of a designing
intelligence are directly observable. With respect to direct observability, eachof these theories is equivalent.
Thus, if the standard of observability is applied in a strict way, neither
intelligent design nor any other theory of biological origins qualifies as ascientific theory. But let’s consider the flip side. What if the standard ofobservability is applied in a more flexible and, perhaps, realistic way? What if
science is defined as an enterprise that examines the observable natural world,but does not necessarily explain empirical observations by reference toobservable entities?
Does it make sense to define science in this more flexible way? It does.
Many entities and events posited in scientific theories cannot be observed
directly either in practice, or sometimes even in principle. Instead, scientists
often must infer unobservable entities to explain observable events, evidence,or phenomena. Physical forces, electromagnetic or gravitational fields, atoms,
quarks, past events, subsurface geological features, biomolecular structures—all are unobservable entities inferred from observable evidence. In 2008
under the border between France and Switzerland, European scientists
unveiled the Large Hadron Collider. This supercollider will enable physicists
to “look” for various elementary particles including the elusive Higgs boson.
Yet none of these particles are observable in any direct sense. Instead,physicists try to detect them by the energetic signatures, traces, or decay
products they leave behind.
Scientists in many fields detect unobservable entities and events in theireffects. They often infer the unseen from the seen. Nevertheless, such entitiesand events are routinely considered to be part of scientific theories. Those
who argue otherwise confuse the event or evidence in need of explanation(which in scientific investigations is nearly always observable in some way)with the event or entity doing the explaining (which is often not).
The presence of unobservable entities in scientific theories creates aproblem for those who want to use observability as a demarcation criterion bywhich to disqualify intelligent design from consideration as scientific. Many
theories—theories that are widely acknowledged to be scientific—invoke
unobservable entities. But if other scientific theories, including materialistic
theories of biological origins, can invoke unobservable entities or events to
explain observable evidence and still qualify as scientific, then why can’t thetheory of intelligent design do so as well?
Testability Revisited
In the previous chapter, I showed that the theory of intelligent design is
testable by reference to empirical evidence. I described in general terms how
scientists can subject intelligent design to various kinds of empirical tests, andI provided an example of a testable prediction that the theory makes. But for
years, I have talked to people—scientists, theologians, philosophers, lawyers,
journalists, callers on talk shows—who purport to know that the theory ofintelligent design cannot be tested. (Oddly, some of these same people also
claim that the theory has been tested and found wanting).24
Sometimes critics say that intelligent design is untestable because thedesigning intelligence is unobservable, thus combining two demarcation
criteria, observability and testability. Other critics assert that intelligent design
cannot be tested, because they assume that ID advocates are necessarily
positing an omnipotent deity. Some critics say that intelligent design isuntestable because the actions of intelligent agents (of any kind) are
inherently unpredictable, and testability depends upon the ability to makepredictions.
These common objections to the testability and, thus, the scientific status
of intelligent design have dissuaded many people from considering evidence
for intelligent design. So what should we make of these “defeaters”? Do these
specific demarcation arguments provide a good reason for denying that
intelligent design is a scientific theory? Do they show, despite my arguments
to the contrary in the previous chapter, that intelligent design cannot be
tested? Let’s take a closer look.
Unobservables and Testability

Robert Pennock, one of the witnesses in the Dover trial, argued that theunobservable character of a designing intelligence precludes the possibility of
testing intelligent design scientifically because, as he explained, “science
operates by empirical principles of observational testing; hypotheses must be
confirmed or disconfirmed by reference to…accessible empirical data.”25
Eugenie Scott also seemed to argue that intelligent design cannot be tested
because it invokes an unobservable entity. In the article I cited above, inwhich she defended the actions of Kenyon’s detractors at San Francisco State,
Scott also linked the criterion of observability to testability. After saying,“You can’t put an omnipotent deity in a test tube,” she went on to say: “Assoon as creationists invent a ‘theo-meter,’ maybe then we can test for
miraculous intervention. You can’t (scientifically) study variables you can’t
test, directly or indirectly.”26
In this version of the argument, critics insist that the unobservablecharacter of a designing intelligence renders the theory inaccessible toempirical investigation, making it both untestable and unscientific. Both“observability” and “testability” are asserted as necessary to scientific status
and the converse of one (unobservability) is asserted to preclude thepossibility of the other (testability). Superficially this version of the argument
seems a bit more persuasive than demarcation arguments that simply invoke
observability by itself to disqualify design. Yet it does not stand up to close
inspection either.
In the first place, there are many testable scientific theories that refer tounobservable entities. For example, as we saw in Chapter 3, during the race toelucidate the structure of the genetic molecule, both double helix and triple
helix models were considered, since both could explain the X-ray images of
DNA crystals.27 Although neither structure could be observed directly, thedouble helix of Watson and Crick eventually won out, because it could
explain other observations that the triple helix model could not. The inference
to one unobservable structure (the double helix) was accepted because it was
judged to possess a greater explanatory power than its competitor.
Claims about unobservables are routinely tested in science indirectlyagainst observable evidence. In many fields the existence of an unobservable
entity is established or detected by testing the explanatory power that would
result if a given hypothetical entity (i.e., an unobservable) were accepted as
actual. Many sciences infer to the best explanation—where the explanation
presupposes the reality of an unobservable entity—including theoreticalphysics, geology, molecular biology, genetics, cosmology, psychology,
physical and organic chemistry, and evolutionary biology.
Second, as I showed in Chapters 7 and 15, historical sciences, inparticular, commonly use indirect methods of testing, methods that involveassessing the causal powers of competing unobservable events to determine
which would, if true, possess the greatest explanatory power. Recall thatDarwin defended the scientific status of his theory by pointing out thatassessing the relative explanatory power of his theory of common descent—atheory about the unobservable past—was a perfectly legitimate and accepted
method of scientific testing.28
Third, as I showed in Chapters 15 and 18, intelligent design is testable inprecisely this fashion—by examining its explanatory power and comparing itto that of competing hypotheses. The unobservable intelligence referred to in
the theory of intelligent design does not preclude testing the theory, if indirect
methods of testing hypotheses—such as evaluating comparative explanatory
power—are allowed as scientific. If, however, science is defined morenarrowly so that only the direct observation of a causal factor counts as aconfirmatory test of a causal hypothesis, then neither intelligent design nor ahost of other theories would qualify as scientific.
Either way the theory of intelligent design and various evolutionary
theories of origins are equivalent in their ability to meet the joint criteria ofobservability and testability. If critics of intelligent design construe thesecriteria to forbid inferring the existence of unobservables and indirect testingfor them, then both intelligent design and its potential competitors fail. If
critics construe these criteria to allow inferences to, and indirect testing of,unobservable entities and events, then both intelligent design and many
competing evolutionary theories qualify as scientific theories. Either way,these criteria fail to discriminate between intelligent design and many othertheories that are already accepted as scientific. Thus, they fail to provide agood reason for disqualifying intelligent design from consideration as ascientific theory.
Testability, Omnipotence, and the Supernatural
Robert Pennock argues that there is something else about the unobservable
designing intelligence posited by intelligent design that makes it untestable.
Specifically, Pennock claims that intelligent design is untestable because it
invokes an unobservable supernatural being with unlimited powers. He
argued that since such a being has powers that could be invoked to “explain
any result in any situation,” all events are consistent with the actions of such abeing. Therefore, no conceivable event could disprove the hypothesis ofintelligent design. As Ken Miller asserts, “The hypothesis of design iscompatible with any conceivable data, [and] makes no testable predictions.”29
This argument fails for two reasons. First, it misrepresents the theory ofintelligent design. The theory of intelligent design does not claim to detect asupernatural intelligence possessing unlimited powers. Though the designing
agent responsible for life may well have been an omnipotent deity, the theoryof intelligent design does not claim to be able to determine that. Because theinference to design depends upon our uniform experience of cause and effectin this world, the theory cannot determine whether or not the designing
intelligence putatively responsible for life has powers beyond those on
display in our experience. Nor can the theory of intelligent design determine
whether the intelligent agent responsible for information in life acted from thenatural or the “supernatural” realm. Instead, the theory of intelligent design
merely claims to detect the action of some intelligent cause (with power, atleast, equivalent to those we know from experience) and affirms this because
we know from experience that only conscious, intelligent agents produce
large amounts of specified information. The theory of intelligent design does
not claim to be able to determine the identity or any other attributes of that
intelligence, even if philosophical deliberation or additional evidence fromother disciplines may provide reasons to consider, for example, a specificallytheistic design hypothesis.30 (I discuss the possible implications of the theoryof intelligent design in the next chapter.)Pennock’s argument also fails because the theory of intelligent design issubject to empirical testing and refutation. Indeed, intelligent design actually
makes a much stronger claim than the caricature of it he critiqued during thetrial. Pennock critiques the hypothesis that “an omnipotent deity could explain
the origin of life.” But the theory of intelligent design developed in this bookdiffers from that hypothesis. My theory of intelligent design does not merely
affirm that intelligence constitutes a possible explanation of certain features oflife. Instead, the design hypothesis developed here asserts that intelligent
design constitutes the best explanation of a particular feature of life becauseof what we know about the cause-and-effect structure of the world—
specifically, because of what we know about what it takes to produce large
amounts of specified information. For this reason, the design hypothesis is not“compatible with any conceivable data” or observations whatsoever.
If it were shown, for example, that the cause-and-effect structure of theworld were different than what advocates of intelligent design claim—if, forexample, someone successfully demonstrated that “large amounts offunctionally specified information do arise from purely chemical and physicalantecedents,” then my design hypothesis, with its strong claim to be the best(clearly superior) explanation of such phenomena, would fail. Intelligent
design would remain as a possible explanation (much as chance does now).
But the claim that intelligent design provides the best (most causallyadequate) explanation for the origin of biological information would berefuted. Similarly, if it could be shown that key indicators of intelligence—such as specified information—were not present in living systems, the basis

of the design hypothesis in its present strong form would evaporate. Thus,
Pennock and Miller incorrectly portray the theory of intelligent design as
being consistent with any empirical situation. The theory of intelligent design
is, in fact, testable—just as I argued in the previous chapter.
Testability and Predictability
I have learned that when critics of intelligent design are confronted withrefutations of a particular demarcation argument, they typically shift their
ground and formulate other arguments either by invoking a different
demarcation criterion or by applying the original criterion in a moredemanding way. For example, after explaining how intelligent design can betested and how it does make certain kinds of predictions, I commonly hear theobjection that the theory of intelligent design is not scientific, because it
cannot make other kinds of predictions. Critics correctly point out, forexample, that we cannot predict with complete accuracy what intelligent
agents will do, since, presumably, intelligent agents possess the capacity to
act freely of their own volition. Since ID invokes the action of an
unpredictable intelligent agent, and since scientific theories must makepredictions, theories invoking the activity of intelligent agents are notscientific—or so the argument goes.
Yet standard materialistic theories of evolution (whether chemical or
biological) do not make predictions of this kind either. Specifically,
evolutionary theory does not make predictions about the future course ofevolution. It makes no prediction about the kind of traits or species that
random mutations and natural selection will produce in the future. As Ken
Miller notes, “The outcome of evolution is not predictable.”31 Even so, mostevolutionary biologists think that these theories are scientific—and for good
(if convention-dependent) reasons. Evolutionary theories provide
explanations of past events and present evidence, and they make predictions
about the patterns of evidence that scientists should find in their future
investigations of, for example, the genome or the fossil record.
In the same way, the theory of intelligent design does not makepredictions about when (or whether) the designing intelligence responsible forlife will act in the future. Yet it does explain past events and present evidence,and it also makes discriminating predictions about the kind of evidence
scientists should find in their future investigations (see Chapter 18 andAppendix A).32 Thus, neither type of origins theory qualifies as scientific if
the “ability to generate predictions” is treated as a condition of scientificstatus and interpreted in a strict way, though both types of theories qualify as
scientific if this criterion is equated with scientific status and interpreted in a

more flexible way.
As I studied the various demarcation arguments against intelligent design,
I repeatedly found this same pattern. Invariably, the criteria that supposedly
showed that intelligent design is inherently unscientific either disqualified
both intelligent design and its materialistic rivals, or, if the criteria wereapplied more flexibly, legitimated both types of theories—provided, that is,that the criteria were not applied in a question-begging way.
As this pattern became more pronounced with each of the definitional
criteria I examined, I became more convinced that there was no good reason
to exclude intelligent design from consideration as a scientific explanation forthe origin of biological information. Since—by convention—materialistic
theories of biological origin were considered scientific, and since the theoryof intelligent design met various criteria of scientific status just as well as
these rival theories, it seemed clear that the theory of intelligent design, by thesame conventions, must be considered scientific as well. Nevertheless, JudgeJohn E. Jones disagreed.
The Demise of Demarcation Arguments
In 2005, before the Dover trial, I knew that the ACLU would try to persuadeJudge Jones that “intelligent design isn’t science.” Yet I wondered how it wasgoing to do this. I knew the long history of attempts to use specificdemarcation criteria to discredit various ideas and theories. And I knew thatmost philosophers of science, some of whom the ACLU would need to use as
expert witnesses, did not regard these arguments as valid. Philosophers ofscience who had studied the history of the demarcation question already knew
what I had discovered: it was not only difficult to define science by reference
to a single set of methodological practices; it was difficult to find demarcation
criteria that could differentiate the scientific status of competing theories
without applying a double standard or using question-begging logic.
I also knew that using these arguments in court had a checkered history.
Though the ACLU won the Mclean v. Arkansas case in 1981, leading
philosophers of science,33 none sympathetic to creationism, later severely
criticized Ruse’s use of demarcation arguments in his testimony. They pointed
out that many of the definitional criteria that Ruse had used to establish
creation science as pseudoscience could actually be used to establish creation
science as a scientific theory. They also pointed out that the same criteria, if
applied strictly, could have the effect of disqualifying Darwinian evolution
from that same honorific designation. For more detail on this curious result,
see this note.34
As a result of such difficulties, several leading philosophers of science,such as Larry Laudan, Philip Quinn, and Philip Kitcher,35 argued that thequestion, “What distinguishes science from nonscience?” is both intractableand uninteresting. Instead, they and most other philosophers of science haveincreasingly realized that the real issue is not whether a theory is “scientific”
according to some abstract definition, but whether a theory is true, or
supported by the evidence.
Scientists do not decide these questions using abstract criteria that purport
to tell in advance how all good scientific theories are constructed or what they
will, in general, look like. Instead, scientists look to the evidence to decide themerits of competing theories. Theories are not rejected with definitions, butwith evidence. Thus, in a now famous article called “The Demise of the
Demarcation Problem,” Larry Laudan shows that demarcation criteria of thekind that Ruse had proposed in 1981 do not do the work Ruse wanted them todo. Specifically, these criteria will not decide the merits of, or discriminate
between, competing theories.
Ruse himself later publicly acknowledged this—at least in part. During atalk to the American Association for the Advancement of Science (AAAS) in
1993, Ruse repudiated his previous support for the demarcation principle byadmitting that Darwinism (like creationism) “depends upon certain
unprovable metaphysical assumptions.”36 In his more recent scholarship Rusehas gone further, arguing that evolutionary theory has often functioned as akind of “secular religion.”37 In any case, by the early 1990s a consensus had
developed in the philosophy of science about the use of demarcationarguments. As one philosopher, Martin Eger, summarizes: “Demarcationarguments have collapsed. Philosophers of science don’t hold them anymore.
They may still enjoy acceptance in the popular world, but that’s a different
world.”38
How Dover Was Decided
In 2005 I wondered, “Could the ACLU pull it off?” Could its representatives
convince a judge to rule that intelligent design isn’t science by definition? If
so, on what basis? What criterion would they invoke to discriminate between
the scientific status of intelligent design and Darwinian or chemical evolution,
both of which are routinely taught in public high-school science textbooks?
Observability? Testability? Falsifiability? Given what I had discovered about
the inability of such criteria to discriminate the scientific status of competing
origins theories, I wondered: What criterion could a judge use to deny that
intelligent design qualified as a scientific theory?
As it turned out, Judge Jones did a clever thing. He didn’t reject
intelligent design as science because it failed to meet a neutral definition of

science or methodological norm. At the urging of the ACLU, he circumvented
the whole demarcation problem by defining science as the exclusion of
intelligent design—only he didn’t call it that. Instead, following the ACLU’s
expert witnesses and brief, he called the exclusionary principle“methodological naturalism.”39 He then equated science with adherence tothat principle and rejected intelligent design because it violated it.
But what is the principle of methodological naturalism? Methodologicalnaturalism asserts that to qualify as scientific, a theory must explain all
phenomena by reference to purely material—that is, non-intelligent—causes.
As philosopher Nancey Murphy explains, methodological naturalism forbids
reference “to creative intelligence” in scientific theories.40
So, did the judge find a demarcation criterion or methodological normthat could discriminate between intelligent design and materialistic theories of
evolution? Clearly, he did. If science is defined as Judge Jones defined it,
intelligent design does not qualify as a scientific theory. But should science bedefined that way? Did the judge offer a good reason for excluding intelligent
design from consideration as science?
He did not. Instead, he provided an entirely arbitrary, circular, andquestion-begging justification for the exclusion of design. I knew, as did
many other philosophers of science, that demarcation arguments based uponneutral methodological norms such as testability could not justify aprohibition against intelligent causes in science. The judge in the Dover case
supposedly offered a reason for this prohibition, but his reason turned out tobe just a restatement of the prohibition by another name. According to Judge
Jones, the theory of intelligent design cannot be part of science, because it
violates the principle of methodological naturalism. But that principle turns
out to be nothing more than the claim that intelligent causes41—and thus thetheory of intelligent design—must be excluded from science. According tothis reasoning, intelligent design isn’t science because it violates the principleof methodological naturalism. What is methodological naturalism? A ruleprohibiting consideration of intelligent design in scientific theories.
Thus, despite appearances to the contrary, Judge Jones did not offer agood reason—a theoretically neutral norm or definition of science—by which
to justify the exclusion of intelligent design “from science.” Instead, hesimply asserted a prohibition against the consideration of intelligent design,
invoked the same prohibition by another name, and then treated it as if it werea reason—a methodological principle—justifying the prohibition itself.
Fortunately we don’t look to federal judges to settle great questions ofscientific and philosophical import. Did life arise as the result of purely
undirected material causes or did intelligence play a role?42 Surely a courtpromulgated definition of science, especially one so logically problematic asmethodological naturalism, does not answer that question.43
No doubt Judge Jones felt justified in offering such a thin and circularjustification for his definition of science because he knew many scientists
agreed with him. And, indeed, the majority of scientists may well accept the
principle of methodological naturalism. So, if science is what scientists do,
and if many or most scientists do not think that hypotheses invoking
intelligent causes have a place in their theories, then perhaps intelligent
design doesn’t qualify as a scientific theory after all. According to this line ofthinking, Judge Jones did not impose an arbitrary definition of science.
Instead, his ruling merely expressed a preexisting consensus about proper
scientific practice from within the scientific community. As the judge himself
wrote in the ruling, methodological naturalism is simply a “centuries-old
ground rule” of science.44
So why shouldn’t scientists continue to accept methodological naturalism
as a strict rule governing scientific practice? Maybe we should just accept thisconvention and move on. Of course, some scientists may decide to do exactly
that. But if they do, it’s important to recognize what that decision would andwould not signify about the design hypothesis. Scientists who decide to define
explanations involving creative intelligence as unscientific cannot then treat
the failure of such hypotheses to meet their definition of science as a tacit
refutation of, or reason to reject, such hypotheses. Why? It remains logicallypossible that an “unscientific” hypothesis (according to methodologicalnaturalism) might constitute a better explanation of the evidence than thecurrently best “scientific” hypothesis. Based upon the evidence presented inthis book, I would contend that, whatever its classification, the design
hypothesis provides a better explanation than any of its materialistic rivals forthe origin of the specified information necessary to produce the first life.
Reclassifying an argument does not refute it.
In any case, there is no compelling reason for the currently dominantconvention among scientists to continue. Conventions are just that. Without agood reason for holding them, they may do nothing more than express anunexamined prejudice and block the path of inquiry. When good reasons forrejecting conventions come along, reasonable people will set them aside—and
there are now good reasons to set this convention aside.
First, scientists have not always restricted themselves to naturalistic
hypotheses, contrary to the claims of one of the expert witnesses in the Dovertrial. Newton, for example, made design arguments within his scientificworks, most notably in the Principia and in the Opticks. Louis Agassiz, adistinguished paleontologist and contemporary of Darwin, also made design
arguments within his scientific works, insisting that the pattern of appearance
in the fossil record strongly suggested “an act of mind.” Defenders ofmethodological naturalism can claim, at best, that it has had normative force
during some periods of scientific history. But this concedes that canons ofscientific method change over time—as, indeed, they do. From Newton until
Darwin, design arguments were a common feature of scientific research. After
Darwin, more materialistic canons of method came to predominate. Recently,
however, this has begun to change as more scientists are becoming interested
in the evidence for intelligent design.
Second, many scientific fields currently posit intelligent causes as
scientific explanations. Design detection is already part of science.
Archaeologists, anthropologists, forensic scientists, cryptographers, andothers now routinely infer intelligent causes from the presence of
information-rich patterns or structures or artifacts. Further, astrobiologists
looking for extraterrestrial intelligence (SETI) do not have a rule against
inferring an intelligent cause. Instead, they are open to detecting intelligence,but have not had evidence to justify making such an inference. Thus, theclaim that all scientific fields categorically exclude reference to creative
intelligence is actually false.
Even some biologists now contest methodological naturalism. Granted,
many evolutionary biologists accept methodological naturalism as normative
within their discipline. Nevertheless, biologists intrigued by the design
hypothesis reject methodological naturalism, because it prevents them fromconsidering a possibly true hypothesis. Indeed, a central aspect of the current
debate over design is precisely about whether methodological naturalism
should be regarded as normative for biology today. Most evolutionary
biologists say it should remain normative; scientists advocating intelligent
design disagree. But critics of intelligent design cannot invoke
methodological naturalism to settle this debate about the scientific status of
intelligent design, because methodological naturalism is itself part of what thedebate is about.
Third, defining science as a strictly materialistic enterprise commits
scientists to an unjustified—and possibly false—view of biological origins. It
is at least logically possible that a personal agent—a conscious goal-directed
intelligence—existed before the appearance of the first life on earth.
Moreover, as shown in Chapters 15 and 16, there are now rigorous scientificmethods by which the activity of intelligent agents can be inferred or detectedfrom certain kinds of effects. Thus, if a personal agent existed before theadvent of life on earth, then it is also at least possible that the activity of suchan agent could be detected using one of these methods. If so, then prohibitions
against the design hypothesis in investigations of the origin of life amount toan assumption that no intelligence of any kind existed or could have acted
prior to that event. But this assumption is entirely unjustified, especially given
the absence of evidence for a completely materialistic account of abiogenesis.

Finally, allowing methodological naturalism to function as an absolute“ground rule” of method for all of science would have a deleterious effect on
the practice of certain scientific disciplines, especially the historicalsciences.45 In origin-of-life research, for example, methodological naturalism
artificially restricts inquiry and prevents scientists from exploring andexamining some hypotheses that might provide the most likely, best, or
causally adequate explanations. To be a truth-seeking endeavor, the questionthat origin-of-life research must address is not, “Which materialistic scenario
seems most adequate?” but rather, “What actually caused life to arise on
earth?” Clearly, one possible answer to that latter question is this: “Life wasdesigned by an intelligent agent that existed before the advent of humans.” If
one accepts methodological naturalism as normative, however, scientists may
never consider this possibly true hypothesis. Such an exclusionary logic
diminishes the significance of any claim of theoretical superiority for any
remaining hypothesis and raises the possibility that the best “scientific”
explanation (according to methodological naturalism) may not be the best infact.
Scientific theory evaluation is an inherently comparative enterprise.
Theories that gain acceptance in artificially constrained competitions canclaim to be neither “best” nor “most probably true.” At most such theories canbe considered “the best, or most probably true, among an artificially limited
set of options.” Openness to the design hypothesis would seem necessary,
therefore, to any fully rational historical biology—that is, to one that seeks thetruth, “no holds barred.”46 A historical biology committed to following theevidence wherever it leads will not exclude hypotheses a priori because of
their possible metaphysical implications. Instead, it will employ only
metaphysically neutral criteria—such as causal adequacy—to evaluate
competing hypotheses. Yet this more open (and arguably rational) approach
would now seem to affirm the theory of intelligent design as the best, mostcausally adequate, scientific explanation for the origin of the informationnecessary to build the first living organism.

20
Why It Matters

At the height of the media frenzy surrounding the Dover trial in the fall of
2005, I was asked to appear on an MSNBC program called The Abrams
Report. As is customary on “talking heads” shows, the host, Dan Abrams,
played a short prerecorded “backgrounder” before the interview portion of the
program. The report about the trial had been filed by Robert Bazell, a sciencecorrespondent for NBC News. After playing the piece and before asking his
guests any questions, Abrams took the somewhat unusual step of offering his
own opinion about the theory of intelligent design.
What he had to say wasn’t too favorable. Abrams explained as how hethought that intelligent design was “dishonest.” In his opinion, it was a stealthform of creationism that refused to mention God in order to conceal areligious agenda. He also alleged that the theory wasn’t scientific. Not only
had advocates of the theory “provided no new evidence”; there were no “peerreviewed studies” in support of it, or so he claimed. After getting the otherguest on the program, my old nemesis Eugenie Scott (see Chapter 6), toconfirm this (falsely, as it happens), Abrams initiated a line of questioning toestablish that intelligent design was “religion.” To do this, he tried to get me
to say that I thought the designing intelligence responsible for life was God.
But Abrams was setting a trap, one that, by this time, I knew all too well.
If I answered truthfully (which I did) and told him that neither the evidence
from biology nor the theory of intelligent design could prove the identity ofthe designer, he would accuse me of dishonesty and “refusing to come clean”
about the religious nature of the theory (which he also did). If, on the other
hand, I told him—again truthfully—that I personally thought that God had
designed the universe and life, he would seize upon my words as proof thatthe theory of intelligent design was “religion,” thus establishing in his mind
that it must lack any scientific basis. “Just admit it, it’s religion,” he kept
demanding.
As a Christian, I’ve never made any secret about my belief in God or even
why I think theism makes more sense of the totality of human experience thanany other worldview. But I was on Mr. Abrams’s show to discuss the theoryof intelligent design, and the theory does not make claims about a deity, nor
can it. It makes a more modest claim based upon our uniform experienceabout the kind of cause—namely, an intelligent cause—that was responsible
for the origin of biological form and information.
Of course, that modest claim raises a separate question, indeed, an
important religious or philosophical question, namely, the very question about
identity of the designing intelligence that Abrams was pressing me to answer.
Clearly, his question was legitimate. But I wanted to answer it after I had
explained what the theory of intelligent design is and after I had establishedthat there is scientific evidence for it. Otherwise, I knew the minute I said that
I personally thought that God was the designer, he would dismiss the case forintelligent design as “religion” because he, and perhaps many of his viewers,
assume that if an idea is religious it has no basis in fact or evidence.
And so a little tug-of-war ensued. To get me to either “admit it” or lookevasive, Abrams asked two different questions in rapid succession: “What is
intelligent design?” and “Who is the intelligent designer?” As I tried toanswer his first question by defining intelligent design and describing some ofthe evidence that supports it, he kept demanding that I admit the designer isGod. He was playing the journalist on the scent of a scandal, and the scandal
he wanted to reveal was my belief in God. If I “admitted” that I thought God
had designed the universe, then that would invalidate my position by showing
intelligent design to be “religion.” And so he peppered me with a series ofquestions: “Is it religion or not?” “You just can’t…It’s religion.” “Is it religion
or not?” “Just admit it. It’s religion.”
Religion, Science, or What?
Perhaps more than any other objection, the accusation that intelligent design
is religion or “religion masquerading as science” has closed minds toconsidering the evidence for the design hypothesis. This has occurred partly
because the media have successfully portrayed those who advocate intelligent
design as having a hidden religious agenda. But there is another, more
fundamental reason that this criticism has had the effect of closing minds.
Many people assume that science and religion do not interact in any
significant way. They assume that scientific theories have nothing to say
about religious or philosophical questions and that if they do, then they must
not really be scientific.
Abrams was clearly making this assumption as he pursued his “either or”
line of questioning. Judge Jones’s ruling in the Dover case also betrayed thissame way of thinking. Either intelligent design is science or it is religion.
Since, as both men noted, “major scientific organizations” say it isn’t science,
it must be religion. Similarly, since some advocates of “intelligent design”
think that life was designed by God, intelligent design must be a religious

belief rather than an evidence-based scientific theory.
But does this follow? Is intelligent design religion? And, if so, does thatmean that the theory of intelligent design lacks a scientific basis? And what
about the beliefs and motives of advocates of intelligent design: Do they
invalidate the case for intelligent design, including the case I have developedin this book? There are several reasons to think not.
Not Religion
First, by any reasonable definition of the term, intelligent design is not“religion.” When most people think of religion, they think of an
institutionalized form of worship or meditation based upon a comprehensive
system of beliefs about ultimate reality. Religions also typically involve
various formal structures, practices, and ritualistic observances, including“formal services, ceremonial functions, the existence of clergy” and “theobservance of holidays.”1
Though intelligent design, like its materialistic evolutionary counterparts,
does address questions about the origin of living things and may, therefore,
have implications for metaphysical questions about ultimate reality (seeChapter 2), it does not proffer a comprehensive system of belief about thatreality. Intelligent design does not answer questions about the nature of Godor even make claims about God’s existence. The theory of intelligent design
does not promulgate a system of morality or affirm a body of doctrines about
the afterlife. It doesn’t require belief in divine revelation or tell adherents how
to achieve higher consciousness or how to get right with God. It simply
argues that an intelligent cause of some kind played a role in the origin of life.
It is a theory about the origin of biological information and other appearancesof design in living systems.
Moreover, the theory of intelligent design does not involve any of thepractices or have any of the institutional structures or features typicallyassociated with religions. It does not involve worship or meditation or
recommend a system of spiritual disciplines. It does not have sacred texts,
ordained ministers, rabbis, or priests; there are no intelligent-design liturgies,
prayer meetings, or intelligent-design holidays. Advocates of intelligent
design have formed organizations and research institutes,2 but these resemble
other scientific or professional associations rather than churches or religious
institutions.
Despite this, some critics, such as Robert Pennock and Gerald Skoog,
have gone so far as to characterize the theory of intelligent design as narrowly“sectarian.”3 Yet upon examination, this claim evaporates into nothing morethan the observation that the theory of intelligent design is popular with some

Christians and not others. In any case, the theory of intelligent design does notaffirm sectarian doctrines. It has nothing to say about, for example, the virgin
birth, the immaculate conception, predestination, infant baptism, the validityof Islamic law, salvation, original sin, or the reality of reincarnation.
Moreover, the belief that a designing intelligence played a role in the origin of
the living world is hardly unique to Christians or to religious persons ingeneral. Historically, advocates of design have included not only religious
theists, but nonreligious ones, pantheists, polytheistic Greeks, Roman Stoics,
and deistic Enlightenment philosophers and now include modern scientists
and philosophers who describe themselves as religiously agnostic.4
Theistic Implications
To deny that intelligent design is a religion is not to say, however, that theevidence for intelligent design in biology has no religious or metaphysicalimplications. Indeed, there is another option that Mr. Abrams and Judge Jones
did not consider in their attempts to classify the theory rather than assess its
merits. Theories, especially origins theories, needn’t be either scientific or
religious. They might be both. Or more precisely, some scientific theories—
although not themselves religions—might have philosophical or religious
implications.
There are good reasons to think that intelligent design is a scientifictheory of this kind. First, as I’ve already shown (see Chapters 18 and 19)
there are good reasons for thinking that intelligent design is a scientifictheory. Second, the theory of intelligent design addresses a major
philosophical question that most religious and metaphysical systems ofthought also address, namely, “What caused life and/or the universe to comeinto existence?” Thus, like its materialistic counterparts, the theory ofintelligent design inevitably raises questions about the ultimate or prime
reality, “the thing from which everything else comes” (see Chapter 2).5
Moreover, intelligent design, arguably, has specifically theistic
implications because intelligent design confirms a major tenet of a theistic
worldview, namely, that life was designed by a conscious and intelligent
being, a purposive agent with a mind. If intelligent design is true, it follows
that a designing intelligence with some of the attributes typically associatedwith God acted to bring the first living cells into existence. The evidence ofintelligent design in biology does not prove that God exists (or that a being
with all of the attributes of a transcendent God exists), since it is at least
logically possible that an immanent (within the universe) intelligence rather
than a transcendent intelligence might have designed life. Nevertheless,insofar as a transcendent God (as conceived by theists) does possessconscious awareness and intelligence, it possesses the causal powers
necessary to produce (and explain the origin of) specified biologicalinformation. Thus, the activity of a theistic God could provide an adequate
explanation of the evidence of intelligent design in biology, though otherentities could conceivably do so as well. Further, insofar as the evidence forintelligent design in biology increases the explanatory power of theism (as akind of metaphysical hypothesis), it makes theism more plausible or more
likely to be true than it would have been otherwise in the absence of such
evidence.
Those who believe in a transcendent God may, therefore, find support for
their belief from the biological evidence that supports the theory of intelligent
design. They may cite this and other evidence as a reason to identify thedesigning intelligence responsible for life’s origin with the God of theirreligious belief. Thus, it’s fair to say that intelligent design has theistic
implications, or implications that are friendly to theistic belief, even though
the theory is not itself a religion (or a proof of God’s existence).
Metaphysical or Religious Implications?
But if intelligent design makes belief in God more plausible or likely, doesn’tthat still mean intelligent design is essentially a religious, rather than ascientific, concept of biological origins? And shouldn’t that induce someskepticism about its scientific merit?
No. On the contrary, the religious implications of intelligent design are
not grounds for dismissing it. To say otherwise confuses the evidence for atheory and its possible implications. It also fails to recognize that intelligent
design is not the only theory that has metaphysical or religious implications.
Contrary to the popular “just the facts” stereotype of science, many scientifictheories have larger ideological, metaphysical, or religious implications.
Origins theories in particular have such implications since they make claims
about the causes that brought life or humankind or the universe into
existence.6
For example, many scientists believe that the big-bang theory, with its
affirmation that the universe had a temporal beginning,7 has affirmative
implications for a theistic worldview. In fact, many scientists withmaterialistic philosophical leanings initially rejected the big-bang theory,
because they thought it challenged the idea of an eternally self-existentuniverse and because they thought it pointed to the need for a transcendentcause of matter, space, and time.8 Nevertheless, scientists eventually accepted
the theory despite its (to some) unsavory philosophical implications. They did
so because they thought the evidence strongly supported it.

Scientific theories must be evaluated on the basis of the evidence, not on
the basis of philosophical preferences or concerns about implications. Antony
Flew, the longtime atheistic philosopher who has come to accept the case forintelligent design, insists correctly that we must “follow the evidence
wherever it leads,” regardless of its implications. Were that not the case, themetaphysical implications of other scientific theories would invalidate them—and yet they do not.
Or consider another example. Some scientists think that Darwinism andother materialistic origins theories have significant metaphysical and religious(or antireligious) implications. Because both classical Darwinism and modern
neo-Darwinism deny that the appearance of design in living organisms is real,
they affirm that the process that gave rise to that appearance is blind andundirected. Chemical evolutionary theorists likewise insist that the first life
arose, without direction, from brute chemistry.9 Richard Dawkins has dubbed
the idea that life arose as the result of an undirected process the “blindwatchmaker” thesis.10 He and other leading evolutionary theorists claim that
biological evidence overwhelmingly supports this purposeless and fullymaterialistic account of creation.11 As George Gaylord Simpson, the leading
neo-Darwinist a generation ago, stated: “Man is the result of a purposelessand materialistic process that did not have him in mind. He was notplanned.”12
In light of this, Simpson and a host of prominent Darwinian scientists—from Douglas Futuyma13 to William Provine14 to Stephen Jay Gould15 toRichard Dawkins16—have insisted that Darwinism (and the broader blindwatchmaker thesis) has made a materialistic worldview more plausible. They
also argue that materialistic evolutionary theories have made traditionalreligious beliefs about God either untenable or less plausible. As Dawkins
stated, “Darwin made it possible to become an intellectually fulfilled
atheist.”17 Or as the late Harvard paleontologist Stephen J. Gould argued,
Darwin formulated “an evolutionary theory based on chance variation andnatural selection…a rigidly materialistic (and basically atheistic) version ofevolution.” Or as Gould explained elsewhere, “Before Darwin, we thoughtthat a benevolent God had created us,” but after Darwin, “biology took away
our status as paragons created in the image of God.”18
Similarly, many major biology texts present evolution as a process inwhich a purposeful intelligence (such as God) plays no detectable role. As
Kenneth Miller and Joseph Levine explained in the fourth edition of their
popular textbook, Biology, the evolutionary process is “random andundirected” and occurs “without plan or purpose.”19 Or as W. H. Purvis, G. H.
Orians, and H. C. Heller tell students in Life: The Science of Biology, “Theliving world is constantly evolving without any goals. Evolutionary change isnot directed.”20 Other texts openly state that Darwin’s theory has profoundly
negative implications for theism. As Douglas Futuyma’s biology text puts it,“By coupling undirected, purposeless variation to the blind, uncaring processof natural selection, Darwin made theological or spiritual explanations of thelife processes superfluous.”21 For this reason, many people may find support
for materialistic metaphysical beliefs in Darwinian and chemical evolutionary
theory. Conversely, some scientists, such as Kenneth Miller, believe thatevolutionary theory reinforces their religious beliefs.22 Thus, if he is correct,
the study of evolutionary theory may lead a student to “find Darwin’s God.”
Either way, chemical evolutionary theory and neo-Darwinism raise
unavoidable metaphysical and religious questions. Arguably, these theories
also have incorrigibly metaphysical and religious (or antireligious)
implications. At the very least, many scientists think that evolutionary theory
has larger metaphysical, religious (or antireligious), or worldview
implications. Yet this fact has not prevented Darwinism from being regarded
as a scientific theory. Nor does anyone think that the possible implications of
the theory should determine its scientific merit or invalidate the evidence inits favor. Yet if the religious (or antireligious) implications of materialistic
evolutionary theories do not make these theories religion or invalidate theevidence in support of them, then neither should the religious implications of
the theory of intelligent design negate the evidence in its favor or make it a“religion”—with all that implies to the modern mind.
Instead, the content of a scientific theory, not its implications, should
determine its merit. Scientific theories must be evaluated by the quality of theevidence and the arguments marshaled in their favor. But if that principle
applies generally, and specifically, in the case of materialistic theories of
evolution, then it should apply to the assessment of intelligent design as well.
If it does, then the metaphysical or religious implications of intelligent design
do not invalidate the evidential case in its favor.
Religious Motivations?
Just as the implications of particular theories do not determine their merit ortruth, the motivations of the theorists who advance these theories do notinvalidate them either. Indeed, there is an obvious distinction between what
advocates of the theory of intelligent design think about the identity of thedesigning intelligence responsible for life and what the theory of intelligent
design itself affirms. Just because some advocates of intelligent design thinkthat God exists and acted as the designer does not mean that the theory ofintelligent design affirms that belief.
Notwithstanding, there is no question that many advocates of the theory

of intelligent design do have religious interests and beliefs and that some are
motivated by their beliefs. I personally think that the evidence of design inbiology, considered in the context of other evidence, strengthens the case fortheism and, thus, my personal belief in God. Subjectively, as a Christian
theist, I find this implication of intelligent design “intellectually satisfying.”
Does that negate the case for intelligent design that I have presented?
Some have argued as much. For example, in the Dover trial, Barbara Forrest
and Robert Pennock argued that the religious beliefs of advocates ofintelligent design delegitimized the theory. But this doesn’t follow.
First, it’s not what motivates a scientist’s theory that determines its merit,status, or standing; it’s the quality of the arguments and the relevance of theevidence marshaled in support of a theory. Even if all the scientists who haveadvocated the theory of intelligent design were motivated by religious belief(and they are not), motives don’t matter to science. Evidence does. To say
otherwise commits an elementary logical fallacy known as the genetic fallacy,
in which an alleged defect in the source or origin of a claim is taken to beevidence that discredits the claim.
Here’s an example. Suppose someone argues that because Richard wasraised by evil atheists, his arguments against the existence of God are wrong.
The reasoning is obviously fallacious. The facts of Richard’s upbringing areirrelevant to the soundness of the arguments he makes. The arguments must
be considered separately and on their own merits. Similarly, that many ID
advocates have religious beliefs that may increase their openness toconsidering intelligent design says nothing about the truth or falsity of thetheory. Instead, the theory must be assessed by its ability to explain theevidence.
In any case, scientists on both sides of the origins controversy haveideological or metaphysical or religious (or antireligious) motivations.
Barbara Forrest, a leading critic of intelligent design, is a board member of
the New Orleans Secular Humanist Association. Other prominent critics ofintelligent design such as Eugenie Scott and Michael Shermer have signed theAmerican Humanist Manifesto III. Richard Dawkins’s sympathies are wellknown.23 Aleksandr Oparin was a committed Marxist. Kenneth Miller takes adifferent, though no less disinterested tack. He claims that Darwinism
illuminates his religious beliefs as a Catholic.24
Do the religious or antireligious motives of leading advocates of
evolutionary theory disqualify Darwinian evolution or chemical evolutionary
theory from consideration as scientific theories or diminish the merit of thetheories? Obviously they do not. The motivations of the proponents of atheory don’t negate the scientific status, merit, or validity of that theory. But ifthat general principle applies to the evaluation of materialistic evolutionarytheories, then it should apply when considering the merits of intelligent
design. In short, the motives of the advocates of intelligent design do not
negate the claims of the theory.
It Gets Personal: Why it Matters
In public debates, I’ve often encountered critics of intelligent design who
quote design advocates acknowledging their religious beliefs as a way todiscredit the case for the design hypothesis. Though this happens frequently,
I’m always a bit surprised that scientists and especially professional
philosophers (who have presumably taught logic) would resort to suchfallacious motive-mongering. Nevertheless, I suppose it’s not surprising that
religious motives and worldview implications do surface in the heat ofdiscussion. The issue of biological origins raises deeply personal andphilosophical issues. As I have reflected on these issues, I’ve becomeconvinced that my former philosophy professor, Norman Krebbs, was right.
The scientific case for intelligent design is fraught with philosophicalsignificance and poses a serious challenge to the materialistic worldview thathas long dominated Western science and much of Western culture.
With the rise of materialistic evolutionary theories in the nineteenth andtwentieth centuries, science purported to explain the origin of everything fromthe solar system to the cell to the longings of the human soul, all by reference
to undirected physical processes. Collectively, Laplace, Darwin, Oparin, andothers portrayed the universe as an eternal, self-existent, self-creating system.
Skinner, Freud, and Marx applied this perspective to understanding humanbeings by asserting that the same impersonal forces that shaped the material
cosmos also determined human behavior, thought, and history. This view ofreality, derived as it was from the natural and social sciences, understandably
seemed to support the comprehensive philosophy or worldview of scientificmaterialism.
According to scientific materialism, reality is ultimately impersonal:
matter and energy determine all things and, in the end, only matter matters.
“In the beginning were the particles. And the particles became complex stuff.
And the complex stuff reacted with other stuff and became alive. As the living
stuff evolved, it eventually became conscious and self-aware…but only for atime.” According to the materialist credo, matter and energy are thefundamental realities from which all else comes, but also the entities into
which all that exists, including our minds and conscious awareness, ultimately
dissolves. Mind and person-hood are merely temporary “epiphenomena,” arestless foam effervescing for a time atop a deep ocean of impersonality.
Though this view of existence proved initially liberating in that it released

humans from any sense of obligation to an externally imposed system of
morality, it has also proven profoundly and literally dispiriting. If the
conscious realities that comprise our personhood have no lasting existence, iflife and mind are nothing more than unintended ephemera of the material
cosmos, then, as the existential philosophers have recognized, our lives canhave no lasting meaning or ultimate purpose. Without a purpose-driven
universe, there can be no “purpose-driven life.”
The British analytical philosopher Bertrand Russell understood theconnection between the denial of design (or what he called “prevision”) andhumankind’s existential predicament. As he explained in 1918:
That Man is the product of causes which had no prevision of the end
they were achieving; that his origin, his growth, his hopes and fears,
his loves and his beliefs, are but the outcome of accidental
collocations of atoms; that no fire, no heroism, no intensity of thought
and feeling, can preserve an individual life beyond the grave; that all
the labours of the ages, all the devotion, all the inspiration, all thenoonday brightness of human genius, are destined to extinction in thevast death of the solar system, and that the whole temple of Man’s
achievement must inevitably be buried beneath the debris of auniverse in ruins—all these things, if not quite beyond dispute, are yet
so nearly certain, that no philosophy which rejects them can hope tostand.25
As a teenager in the mid-1970s, I sensed this absence of meaning inmodern life. I’m not sure why. Perhaps it was that I had been acutely aware of
the distress of the generation coming of age just ahead of me. Perhaps it wasthat my family had left the church. Perhaps it was because the questions that I
kept asking did not seem to have any obvious answers. “What’s it going tomatter in a hundred years?” And by “it,” I meant anything. What heroism,
thought or feeling, labor, inspiration, genius, or achievement will last, if
impersonal particles are all that ultimately endure?
Though the theory of intelligent design does not identify the agent
responsible for the information—the signature—in the cell, it does affirm thatthe ultimate cause of life is personal. By personal I mean a self-conscious,
deliberative mind in possession of thoughts, will, and intentions. Only persons
have such minds and only minds of this kind can create complex specified
information. If we know anything we certainly know this. Thus, while thetheory of intelligent design does not prove the existence of God or answer allof our existential questions, it does reestablish the conditions of a meaningful“search for meaning.” The case for intelligent design challenges the premise

of the materialist credo and holds out the possibility of reversing thephilosophy of despair that flows from it. Life is the product of mind; it was
intended, purposed, “previsioned.” Hence, there may be a reality behindmatter that is worth investigating.
These implications of the theory are not, logically speaking, reasons toaffirm or reject it. But they are reasons—very personal and human reasons—for considering its claims carefully and for resisting attempts to define thepossibility of agency out of bounds. Is intelligent design science? Is it
religion? Perhaps these are not the right questions. How about, “Is there
evidence for intelligent design?” “Is the theory of intelligent design true?”And, if so, “What does it imply?”
Indeed, for me, far from wanting to avoid the philosophical or theologicalquestions that naturally arise from a consideration of the evidence forintelligent design, these questions have done much to sustain my long interestin the scientific controversy surrounding the origin of life. And why not? If
there is evidence of design or purpose behind life, then surely that does raise
deeper philosophical questions. Who is the designer, indeed? Can the mind
that evidently lies behind life’s digital code be known? Can we as persons
know something of the agent responsible for the intricacies of life? Is there ameaning to existence after all? I have asked these questions for many years.
What excites me about the theory of intelligent design and the compelling
evidence now on display in its favor is not that the theory answers thesequestions, but instead that it provides a reason for thinking that they are once
again worth asking.
Conclusion
For one hundred and fifty years many scientists have insisted that “chanceand necessity”—happenstance and law—jointly suffice to explain the originof life on earth. We now find, however, that orthodox evolutionary thinking—with its reliance upon these twin pillars of materialistic thought—has failed to
explain the origin of the central feature of living things: information.
Even so, many scientists insist that to consider another possibility would
constitute a departure from science, from reason itself. Yet ordinary reason
and much scientific reasoning that passes under the scrutiny of materialist
sanction not only recognize, but require us to recognize the causal activity ofintelligent agents. The sculptures of Michelangelo, the software of theMicrosoft Corporation, the inscribed steles of Assyrian kings—each bespeaks
prior mental activity rather than merely impersonal processes. Indeed,
everywhere in our high-tech environment we observe complex events,
artifacts, and systems that impel our minds to recognize the activity of otherminds: minds that communicate, plan, and design. But to detect the presenceof mind, to detect the activity of intelligence in the echo of its effects, requiresa mode of reasoning—indeed, a form of knowledge—that science, or at least
official biology, has long excluded. If living things—things that we manifestly
did not design ourselves—bear the hallmarks of design, if they exhibit asignature that would lead us to recognize intelligent activity in any otherrealm of experience, then perhaps it is time to rehabilitate this lost way ofknowing and to rekindle our wonder in the intelligibility and design of naturethat first inspired the scientific revolution.

Epilogue
A Living Science

John F. Kennedy was assassinated by a single gunman. The plays attributed toWilliam Shakespeare were written by William Shakespeare. Troy was an
actual city in the ancient world, not a mere legend. Imagine that a history
professor has just asserted these three claims to spark a class discussion about
how historians sift evidence and arrive at conclusions. But before thediscussion can take its expected course, an administrator steps through thedoor of the classroom and clears his throat. “The consensus among theuniversity’s leadership is that such claims should simply be dismissed,” hebegins. “Such claims lack utility. History, properly understood, develops
theories that allow the science of history to move forward. Unless these
historical claims can be shown to be useful for making new discoveries, you
as students should reject them. Thank you for your attention. Carry on.” Hetakes a seat.
The students respond in various ways—acquiescence, dumbfoundedsilence, a one-fingered salute hidden behind a textbook. The history professor,
after recovering from the unexpected intrusion (and remembering that she has
already secured tenure), seizes the teachable moment by immediately
responding. She begins by noting that one doesn’t assess the truth of a
proposition by determining if it’s useful for doing or discovering somethingelse. Neptune circles the sun whether or not we can make any further use of
the fact. What’s more, she continues, some historical conclusions that seem tolack practical utility can eventually lead to useful insights. The knowledge
that Shakespeare wrote Shakespeare tells us a great deal about what is andisn’t essential for fostering great literature, since, for instance, Shakespeare
was raised in an unimportant English village by parents of modest means andreceived only a grammar-school education. What appears useless to one may
prove quite useful to others. The students nod appreciatively.
The administrator is unfazed. “Unless historical claims demonstrate theirutility,” he repeats, “they should be rejected.”
This fictional scenario illustrates an important feature of the current
controversy over intelligent design. After making my case for intelligent
design as the best explanation for the origin of biological information, I often
encounter an objection similar to the one lodged by the intrusive administrator
in this imaginary history class. Scientists often respond to my argument notby proposing a better explanation for the origin of information, but by
insisting that intelligent design is irrelevant since it doesn’t lead anywhere.
“What can I do with intelligent design?” they ask. “What predictions does it
make?” “What research questions does it generate?” These scientists don’tnecessarily deny the truth of intelligent design. They reject it on the grounds
that it lacks utility. Yet a proposition may lack utility for generating
predictions and still be true.
Philip Kitcher articulates a more nuanced but related objection. Kitcher isa respected philosopher of science who holds a distinguished professorship in
philosophy at Columbia University. In 2008, he published Living with
Darwin, largely in response to the increasing interest in the theory ofintelligent design. In the book, Kitcher rejects intelligent design because hesees it as unable to explain putatively new DNA evidence derived fromstudies of the genome. Kitcher does not address the argument I make in thisbook—namely, that intelligent design best explains the origin of the specified
information necessary to produce the first living cell. Instead, he argues that
there are other features of the genome—in particular, the many supposed“junk” sequences in DNA—that intelligent design can’t explain. Because ofthis failure, he concludes that intelligent design is a “dead science.”
Kitcher is a sophisticated philosopher of science, and so he chose hiswords carefully. Kitcher acknowledges that attempts to use demarcationarguments to define intelligent design as inherently unscientific have failed.
To establish that intelligent design does not qualify as a scientific theory, “wemust,” he says, “explain which rule of proper science has been broken.” But,he observes, this “leads into thickets of philosophy from which no clear
resolution has yet emerged. For the past half-century, philosophers have triedand failed to produce a precise account of the distinction between science andpseudo-science. We cannot seem to articulate that essential demarcation.”
Besides this, Kitcher acknowledges that the design hypothesis played an
important role in the formation of many sciences and often guided fruitfulscientific investigations in centuries past. As he explains, “Intelligent design
has deep roots in the history of cosmology, and in the earth and life
sciences.”1
Nevertheless, Kitcher argues, the theory no longer plays that kind of role.
Though many earlier natural philosophers used the design hypothesis to guidetheir research, Kitcher says, intelligent design can no longer do so because it
fails to explain the facts of biology today—in particular, the pervasive
presence of “junk DNA” within the genome. Thus, Kitcher concludes that
intelligent design is a dead science—one that lacks utility as an interpretative
framework—because it cannot explain the current facts of molecular biology.
Kitcher presupposes, as do I, a connection between the explanatory powerof a theory and its probable truth. He does not insist that theories about thepast must guide discovery or predict the future. Nor does he say that ID isn’tscience because it doesn’t do these things. Instead, he claims that ID nolonger provides a useful interpretive framework because it lacks explanatory
power. This constitutes a significant objection to the case I have made.
Further, since I have based my case for intelligent design without reference torecent developments in the field of genomics, some might charge, echoing
Kitcher’s argument about “junk” DNA, that my argument for intelligent
design is obsolete.
Is Kitcher correct? Does intelligent design fail to explain the current DNA
evidence from genomic studies? Are others correct when they say intelligent
design no longer has utility for guiding research or making predictions? In
short, is intelligent design a dead science or an active, useful, living science?
If living, in what way? As an explanatory framework? A guide to discovery?
Or as a generator of predictions?
In this Epilogue, I argue that intelligent design is a “living science,” onethat can: (1) readily explain new facts, including some surprising new
discoveries in genomics, and (2) generate many fruitful research questions
and lines of inquiry. Indeed, I show that because ID explains the most current
data about the cell’s information storage and processing system (and,arguably, does so uniquely well), it naturally suggests many new researchquestions and lines of inquiry. Then, in Appendix A, I also show that, though
we do not generally require historical scientific theories to do so, (3)intelligent design does make a number of discriminating predictions about
what we ought to find in future investigations of living systems if intelligent
design actually played a role in the origin of these systems.
Before proceeding, a note of warning is probably appropriate. This
Epilogue is written primarily for scientists who wonder what they can do withintelligent design. This means that it’s a bit more technical than most of theprevious chapters. It is also longer, since it goes into some detail about how
intelligent design provides both an interpretive framework for a wider class offacts than I’ve previously considered (including recent discoveries ingenomics) and an investigative framework for ongoing research. Certainly
curious nonscientists will find in these pages much to fascinate them, but
perhaps also some parts to skim over in favor of summarizing material at the
beginnings and endings of the various sections.
The Ongoing Explanatory Power of Intelligent Design
A successful scientific research program provides, perhaps first and foremost,an interpretive framework for explaining new evidence, including evidence
that competing theories have difficulty explaining. Throughout the history ofscience new theories or research programs have been established—as “living
sciences”—when they can explain anomalies and new facts that are
unexpected from the point of view of older established theories. For example,
Newton made the case for his theory of universal gravitation in part byshowing that it could readily explain many facts that the established vortex
theory could not. As he famously asserted at the beginning of the Principia
before reciting these anomalous facts, “The hypothesis of vortices is beset bymany difficulties.”2
As noted (see Chapters 7, 18, and 19), many theories in the history ofscience have been accepted mainly because of their ability to explainestablished facts better than their competitors, irrespective of whether theyalso successfully predict new facts—though clearly many successful theories
have done both. Explanatory power, especially with respect to a body ofanomalous data, often establishes a theory as an active scientific research
program. Certainly a theory typically also suggests further research questions
that scientists can pursue in the lab, in the field, or, increasingly, on thecomputer, but it typically gains its initial traction by providing a usefulframework for understanding and interpreting a broad range of evidence,
particularly evidence recently uncovered.
And so it is with the theory of intelligent design. Thus, before I examine
the research questions and predictions that the theory of intelligent design
generates, I want to look in some detail at the new evidence that it explains,
specifically the new evidence about the genome and the cell’s informationprocessing and storage system that has deepened the DNA enigma and provenincreasingly puzzling from the standpoint of conventional materialistic
theories of evolution.
Though intelligent design and the selection and mutation mechanism canboth explain some appearances of design, the two forms of causation work
differently. For this reason, there are some features of living systems that weshould expect only if mutation and selection had generated them, and otherfeatures or outcomes that we should expect if intelligent design had done so.
And there are some features of the living systems that we should expect tofind whether either cause was operative.
We know a lot from experience about how intelligent agents design
information-processing and storage systems. We know that intelligent agentsregularly use particular design strategies and patterns for such systems. They
organize information hierarchically so as to maximize storage density andfacilitate its efficient retrieval. We also know that they generally can conceiveof outcomes before they exist. Those outcomes are then actualized byarranging the many disparate parts of the system without the need to develop
and maintain functional “intermediate forms” or structures along the way tothe desired functional end point. We also know that designers often seek tooptimize one or more objectives, and where they seek to optimize several,they often do so by balancing competing objectives and by making judicious
compromises or “elegant” trade-offs between them. Thus, there are many
features of a designed system that we should expect to see if in fact the
information-processing and storage system in the cell was intelligently
designed. The main body of this book showed that two such features—functionally specified digital information and a functionally integrated systemfor processing that information—are clearly evident in the cell.
Conversely, we know a lot about natural selection and random mutationand other similarly naturalistic mechanisms. We know how such mechanisms
should work in theory and, therefore, what types of features in living systems
these mechanisms do, or do not, readily explain.
Evolutionary biologists commonly remind us that natural selection andmutation can produce the appearance of design. But many also acknowledge
that selection and mutations can explain some appearances of design and notothers. This mechanism explains appearances of design that are optimized ina way that is consistent with the results of a blind trial-and-error process. Yet
it wouldn’t explain optimized structures or designs that are inconsistent withthe outworking of such a process. Similarly, since natural selection is bydefinition mindless and undirected, it cannot explain structures or systems orinformational features (appearances of design) that require foresight toassemble, whereas actual intelligent design can explain such appearances.
Since natural selection “selects” or preserves functionally advantageous
mutations or variations, it can explain the origin of systems that could havearisen through a series of incremental steps, each of which maintains orconfers a functional advantage on a living organism. Nevertheless, by thissame logic, selection and mutation face difficulty in explaining structures orsystems that could not have been built through a close series of functional
intermediates. Moreover, since selection operates only on what mutation first
produces, mutation and selection do not readily explain appearances of designthat require discrete jumps of complexity that exceed the reach of chance; thatis to say, the available probabilistic resources.
Since these two different types of causes (mutation and selection, andintelligent design) generate at least some different types of effects, they also
generate different empirical expectations about what we should find in living
systems (or in the fossil record). Scientists can, therefore, use evidence fromliving systems to adjudicate the comparative explanatory power of thesedifferent modes of causation. (Since other materialistic mechanisms produce
certain types of features and effects and not others, their explanatory power
also can be assessed in a similar fashion.)
Since we would expect to find different patterns of evidence in living
systems if they had one type of cause rather than the other, the evidencepresent in living systems enables us to assess which of the two types of cause
provides a better explanation of the evidence in question, making theories
invoking these modes of causation eminently testable. Designing agentsproduce information-rich systems in one way. Mutation and selection arethought to have produced information in another. Therefore, the features of
the genome and its surrounding information-processing system provide a
critical test of the comparative explanatory power of these two competing
causal explanations.
So, given what we know about these prospective causes, what should we
expect to find in the genome and in the cell’s larger information-processing
system, and how does it compare with what we do see?
In fact, we find features that are just what we would expect if theinformation systems in the cell were the product of a designing intelligence as
opposed to an undirected process such as selection and mutation.
The New Genomics
Over the last fifteen years, genetic studies have revolutionized ourunderstanding of how the cell performs operations on genetic and other formsof biological information. Biologists still affirm that DNA contains specified
information, but they have discovered that the system for storing andprocessing this information is even more complex than I described in Chapter
5. Though the mechanisms of transcription and translation described in
Chapter 5 are still thought to play a central and essential role in the expressionof genetic information, leading researchers have discovered that the
information for building a given protein is not always (or even usually)
located in just one place along the DNA molecule. They have also discoveredthat one gene does not always code for just one protein, as George Beadle and
Edward Tatum first claimed in the early 1940s.
Instead, leading researchers now realize that, depending upon how the cellprocesses the information stored in DNA, a single gene may contribute to the
production of thousands of proteins and other gene products (such as
regulatory and structural RNA molecules).3 The cell also uses genetic
information to produce critical RNA molecules that do not undergotranslation, but instead direct the processing of other genetic information.4
Further, during the translation process, additional processes edit the chains ofamino acids produced before they fold into their final functional forms.5
Equally revolutionary is the discovery that biological information beyond (notresident in) DNA plays a critical role in the development of organisms. As
molecular biology and genomics have revealed new features of the cell’s
information storage and processing system, they have inspired a new

conception of the gene—one in which the gene is no longer understood as a
singular, linear, and localized entity on a DNA strand, but rather one in which
the gene is understood as a distributive set of data files available for retrieval
and context-dependent expression by a complex information-processing
system.6
For this reason, some might argue that the case for intelligent designpresented thus far is outdated. Nevertheless, far from making the case forintelligent design obsolete, these recent discoveries strengthen it. The cell’s
information-processing and storage systems possess many features that we
find only in intelligently engineered systems and they are, for this reason,
precisely what we should have expected to find if these systems had in fact
been designed. Based upon our experience of the cause-and-effect structure of
the world, we know of only one type of cause for the origin of these newly
discovered features—and that cause is, again, intelligence. That the theory ofintelligent design can provide a causally adequate explanation for otherwise
anomalous evidence and unexpected discoveries underscores its ability toprovide an interpretive framework for scientific research and illustrates a key
way that the theory functions as a dynamic scientific research program.
Consider three new discoveries about the cell’s informational system that
illustrate the ability of intelligent design to explain otherwise unexpecteddiscoveries, including many that the competing explanations do not. First,
functionally specified information is densely concentrated in DNA. Second,
the genome is hierarchically arranged to optimize access and retrieval of
information. And, third, the organism provides an informational context—
involving both genomic and extragenomic information—that determines theexpression of lower-level genetic modules.
1. The Concentration of Information in DNA
Over the last ten years, scientists have come to realize that genetic
information in DNA is organized to maximize the storage of information. Far
from containing a preponderance of “junk”—nonprotein-coding regions that
supposedly perform no function—the genome is dominated by sequences richin functional information. Indeed, even the nonprotein-coding regions of
DNA serve multiple functions. In Chapter 18, I listed some of the many
functions that the nonprotein-coding regions play. I also noted that—overall—these regions of the genome perform many of the same functions as an
operating system in a computer. For example, the noncoding regions of DNA(in concert with other sources of information) direct the expression of basesequences in the protein-coding regions in DNA. Thus, far from being
dispersed sparsely, haphazardly, and inefficiently within a sea ofnonfunctional sequences (ones that supposedly accumulated by mutation),functional genetic information is densely concentrated on the DNA molecule.
This is not to say there is no evidence of mutational accumulation ordegradation (of the functional genome) over time. The genome does display
evidence of past viral insertions, deletions, transpositions, and the like, muchas digital software copied again and again accumulates errors. Nevertheless,the vast majority of base sequences on the genome, and even the many
sequences that do not code for proteins, serve essential biological functions.
Genetic signal dwarfs noise, just as design advocates would expect and just as
they predicted in the early 1990s. (See Chapter 18, backnotes 25 and 39).
Another feature of the cell’s information-storage system is reshaping ourunderstanding of the gene itself. Genomic studies reveal that the cell accesses“distributed genetic data sets” and then combines these modules of specified
information to direct the production of various proteins during translation—much as a computer operating system retrieves and accesses modular data sets
stored in various places on a hard drive and then reassembles them into asingle data file. As a result of these recent discoveries, biologists have cometo understand the gene less as a string of nucleotide bases stored in just one
place along the DNA molecule and more as a distributed data set of specified
base sequences stored in various places along the DNA helix (and sometimes
across various chromosomes).7 These distributed information-rich sequences
are assembled by the organism’s information-processing system using—inaddition to the genetic code—other higher-level codes that determine howvarious modules of genetic information should be retrieved and concatenatedbefore translation occurs.
There is another more striking—even eerie—way that the genome
maximizes information storage. In the traditional picture of the gene, asequence of bases for building a given protein begins at a start codon (a tripletof bases designating the beginning of relevant coding instructions) and ends at
a stop codon. The sequence in between constitutes the genetic assembly
instructions for building that particular protein. Many proteins are built in thisfashion. But recently molecular biologists have discovered that multiple
messages (sets of assembly instructions) can be stored in the same sequenceof bases or region of the genome.
Sometimes these multiple messages overlap along the genome. In suchcases, the RNA polymerase complex accesses and transcribes different
genetic messages by starting and stopping at different places in the same geneor genome region.8 Additionally, two large ribonucleoprotein complexes
called “spliceosomes” and “editosomes” splice and edit the RNA transcripts
to produce many other genetic messages before their translation at theribosome.9 (RNA transcripts from the same genome region and even RNA

transcripts from different chromosomes can be spliced together to form many
new sets of assembly instructions.)10 At the ribosome, the translation
machinery—in concert with various other protein and RNA factors—then
determines how these messages will be read.
The splicing, editing, and reading process can produce more than one
protein from the same RNA message. Indeed, one gene or region of the
genome, in concert with extragenomic codes and machinery, can produce
many thousands of different RNA messages and proteins. This polypotency
results from a highly efficient system of information storage involving boththe DNA molecule and a larger information-processing system composed ofnumerous specific RNAs and protein factors. Indeed, as a result of theoverlapping genetic messages and different modes of information processing,
the specified information stored in DNA is now recognized to be orders ofmagnitude greater than was initially thought in the immediate wake of themolecular biological revolution.
Genetic messages are also often imbedded within other messages inanother way. Genes are culled from regions of DNA that contain both intronsand exons. Exons contain coding instructions for specific proteins (andsometimes RNAs). Introns are sections of DNA interspersed between theexons—sections that do not contain coding information for the proteins or
RNAs that exons specify. To produce a transcript for translation, theinformation-processing machinery of the cell must cut out introns from an
RNA transcript and splice together individual exons (i.e., those previously
separated by introns) to form a single seamless message. At first glance it
looks as if the introns are just getting in the way. Nevertheless, scientists
recently have discovered that, though introns do not have information thatcodes for the proteins that exons specify, they often have imbedded withinthem other genes for building other proteins.11 Additionally, individual exons
and introns have imbedded within them separate messages or coding regions
for specifying structural and regulatory RNAs.12 Thus, like Russian dolls
stored within Russian dolls, exons and introns encode multiple genetic
messages within themselves and are themselves part of a larger genetic
message.
To get a picture of how this works, imagine a coded message in a letter
sent back across enemy lines during the Revolutionary War. Read in thestandard way, the letter describes problems on the farm, the weather,household challenges, and progress the children are making in their studies,
all of it clear and accurate information. Yet the letter also manages to encode
another message about enemy troop strength and movements, supplies of
ammunition, and an impending enemy attack. The solider receiving themessage can read it because he possesses a key—a cipher—that enables him

to identify the location of the second message imbedded in the first and thento translate it.
In the same way, the cell has protein machinery and RNA codes thatjointly function as a cipher enabling it to access and read the secondary
imbedded messages within the primary message of the genome. Within thecell, higher-level RNA codes, protein factors, and cutting-and-splicing
enzymes work together to enable the cell to identify, access, and transcribe
these genetic messages within messages—messages that are transcribed into
RNA and read at the ribosome during translation. The presence of these genes
imbedded within genes (messages within messages) further enhances theinformation-storage density of the genome and underscores how the genome
is organized to enhance its capacity to store information.
Signs of Design?
This extraordinarily dense concentration of functional information, and thestorage system that makes it possible, suggest design for several reasons.
First, the amount of information present in even the simplest prokaryotic
genome is orders of magnitude greater than previously assumed. Since eventhe amounts previously known vastly exceeded the probabilistic resources of
the universe, the origin of the volume of information now known to be stored
in the genome is even more unlikely to have arisen by chance alone. As W.-Y.
Chung, a bioinformatician at the Center for Comparative Genomics andBioinformatics at Penn State University, has noted, the existence of “dualcoding” and overlapping protein-coding reading frames, just one of manycellular innovations for concentrating genomic information, is “virtually
impossible by chance.”13
Nor does natural selection acting on random mutations help explain theefficient information-storage density of the genome. Quite the reverse.
Natural selection and random mutation is essentially a trial-and-error process.
It would, therefore, necessarily generate many mutational errors in theprocess of producing any functionally specified sequences. It should produce
a genome in which genetic noise rivals or dwarfs genetic signal. This is why
the discovery of supposed junk-containing regions of the genome was seen as
confirming that selection and mutation had shaped the genome. Indeed, thescientists who first reflected upon the discovery of the supposed junk DNA—
Susumu Ohno, Richard Dawkins, Francis Crick, Leslie Orgel, W. Ford
Doolittle, Carmen Sapienza—all assumed that junk DNA was an expected byproduct of mutational processes and that the presence of junk in the genome
confirmed that such processes had played a central role in the origin ofgenetic information.14 Yet, as noted in Chapter 18, advocates of intelligent

design expected early on that the nonprotein-coding regions of the genome
should play a functional role in the cell, because the design logic of an
information-processing system precludes carrying a preponderance of uselesscode, especially in biological settings where such excess would impose aburdensome energy cost on the cell.
There are other positive reasons to suspect that intelligent design played arole in the origin of the information-storage system of the genome. Basedupon our experience of how intelligent agents design information-processing
systems and encrypt coded messages, the features of the genome responsiblefor its storage density are not at all unexpected. We have experience of
information-processing systems that rely on one section of encoded
information to regulate and direct the use of another. And we know ofsystems that direct the retrieval of dispersed data modules in different places
and then reassemble them as complete data files. Computer operating
systems, undisputed products of intelligent design, perform both thesefunctions.
Operating systems use digitally encoded information stored in one part ofthe computer hard drive to direct the use of other digitally coded information,in particular, the application programs stored in another part of the hard drive.
In the cell, nonprotein-coding regions of the genome provide formatting,bracketing, and indexing codes that enable the cell to locate and express
specific modules of stored genetic information, the expression of which may
be needed to respond to specific environmental stresses or changing
developmental conditions.15 Operating systems also direct the retrieval ofdispersed data modules that are stored on the hard drive in different places
and then reassemble them as complete data files, just as the cell’s
information-processing system directs the retrieval of genetic data modules
for expression, assembly, and later concatenation (of gene products).
Operating systems also store code to perform functions (“services”) thatmany application programs need, allowing specific application programs to
be more streamlined and store less information than they otherwise would
have to do. Similarly, nonprotein-coding DNA provides services and needed
functions to the protein-coding DNA during gene expression. For example,
nonprotein-coding sections of DNA produce small microRNAs crucial fortranslational regulation whenever protein-coding regions of the gene are being
accessed and expressed. Every protein-coding region also needs promoter
sequences and a host of other codes (including some stored in nonproteincoding regions as far as a million bases upstream from the coding region of
the gene).16 These promoters are necessary to orient the cell’s transcriptional
machinery correctly.
Though vastly more complex, the cell’s information-processing andstorage system performs many of the same functions, and does so using
functional logic and design patterns that again are reminiscent of those in theoperating systems of modern digital computers. Clearly, biological andcomputer systems use different material media to store and process
information. Yet the logic embodied in the two systems exhibits many
uncanny similarities and functional mappings.
The discovery of genetic messages encoded within genetic messages also
calls to mind systems that have their origin in intelligence. With cipher codes,
human agents conceal one message in another. A bit earlier, I offered thefictional scenario of a letter sent to a Revolutionary War soldier containing
messages at two levels. The scenario, as it turns out, is only fictional up to apoint. During the Revolutionary War, George Washington’s men did in fact
use simple ciphers in which each English letter in the primary text was to beread as a different English letter in a hidden secondary text. Thus, if therecipient of a letter bearing the primary message knew the code—the set ofcorrespondences—he could translate the primary message and reveal theencrypted message. Clearly, encrypting a message within a message is moredifficult than just writing a single message, because the writer must consider
two sets of functional constraints. For each letter selected, the writer has toconsider how two sequences are affected simultaneously—whether they areboth meaningful and whether the second encrypted sequence expresses themeaning intended. The decoded meaning of the secondary sequence ofcharacters has to conform to the conventions of English communication toconvey its message, and the surface message also must express some meaning
as well—at least, if it is to conceal the presence of the encrypted message.
Clearly, making two sequences that satisfy two sets of functional
constraints simultaneously is more difficult than constructing a single
sequence that must satisfy only one set of such constraints. Thus, theprobability of generating such a meaningful message within anothermeaningful message is vastly smaller than the odds of getting a single
message to arise by chance on its own. For this reason, the discovery of dual
and overlapping messages in genetic texts—messages essential to function—
only complicates the information problem for scenarios that rely on chance
and/or natural selection. Indeed, a trial-and-error process seems unlikely to
produce nested coding of information, since the probability of error increases
with each trial when two or more sets of functional constraints have to besatisfied. And many functional outcomes in the cell depend upon satisfying
multiple sets of constraints.
Further, since self-organizational affinities fail to explain the sequential
arrangements of DNA base sequences generally, they do nothing to accountfor even more sophisticated forms of sequencing (i.e., those involving dual
messaging) in the genome. Instead, this form of encryption seems to point

decisively to design, because the use of such encryption techniques are, based
upon our experience, the sole province of intelligent agents. We know of noother such cause of this effect. The evidence of sophisticated encryption
techniques within the genome thus constitutes another distinctive diagnostic—or signature—of intelligence in the cell.
2. Hierarchical Arrangement, Optimized for Access and Retrieval
Another feature of the genome evokes comparisons to information-processing
and storage systems that are known to have been intelligently designed. Like
the files-within-folders system used to organize data files in a personal
computer, the genome is hierarchically organized to make retrieving,
manipulating, and expressing information-rich data sets more efficient.
The genome manifests this hierarchical organization in several ways.
First, different types of genes and various genomic regions occur innonrandom groupings along a DNA string.17 It was long thought that thearrangement of one sequence relative to another is a chance affair, with geneclustering arising only occasionally from duplications or some other type ofmutation. But studies of genome sequences in disparate organisms haverevealed that genes themselves are ordered into longer functional complexes,
just as the bases in various coding sections of a gene are specificallysequenced to enable the production (and efficient regulation) of distinct RNAs
and proteins. In the same way that words are ordered into sentences andsentences into paragraphs, nucleotide bases are ordered into genes and genes
are ordered into specifically arranged gene clusters. Or think of these
individual genes as computer data files and groupings of genes as folders
containing several files.
The groupings of DNA “files” that we observe serve several roles. These
groupings allow the cell to make longer transcripts that are combinations ofdifferent gene messages. In other words, the coding modules of the gene files
in a “folder” can be combined in numerous ways—and in both directions—togreatly increase the number of encoded transcripts and protein products fromthe same genomic region or resources. In addition, gene folders are structured
to permit the en masse retrieval of the DNA files needed to make these larger
combination transcripts, or they are structured to permit the selective
accessing of subfiles.18
Second, gene folders are themselves nonrandomly grouped along
chromosomes to form higher-order folders.19 “Housekeeping” gene folders
are clustered to form housekeeping gene “superfolders”; tissue-specific genefolders are clustered to form tissue-specific gene superfolders. Also, in the
genomes of mammals, specific types of functionally polyvalent DNA

elements preferentially associate with superfolders. SINEs (Short Interspersed
Nuclear Elements) are densest in housekeeping regions, for instance, where
they modulate a host of genomic activities. LINEs (Long Interspersed Nuclear
Elements) are by contrast a common feature of tissue-specific chromosome
domains, where they commonly act as chromosome “scaffold attachment
regions” that determine how strands of DNA will fold and unfold in the
nucleus. They also function as “molecular rheostats” that fine-tune gene
expression. Even the gene “deserts” that occur between superfolders—longsegments devoid of protein-coding genes once thought to be junk and often
still cited as evidence against intelligent design20—are now known to containa set of superfolders. Indeed, these gene deserts are extensively transcribedand code for regulatory RNAs.21 Overall, these clusters of gene clusters
perform many other functions that are only now being discerned.
Third, the combinatorial arrays of gene superfolders, including genedeserts, are in turn grouped into yet larger sets of sequences termed“isochores.” A way to think about isochores is as DNA megafolders.
Moreover, these isochores, millions of bases long, are arranged into triplets toform the band patterns, or “bar codes,” visible when scientists stain thechromosomes of mammals.22 Although the existence of isochore megafolders
has been known for well over two decades, only in the past few years havetheir many functions become clear. One of these functions is to regulate thethree-dimensional association of adjacent chromosomes in the nucleus. These
isochores also serve as a framework for the formation of nuclear organellelike compartments.23 And isochores and chromosome bands are likely only
the lower tiers of still higher codes that yet await discovery.
More Signs of Design?
All this is, again, just what we would expect from a design-theoretic
perspective based upon our experience of how intelligent agents design
information-processing systems. We have extensive experience of systems
that organize data sets as files within folders to facilitate access and retrievalof information—and we know what caused these systems to originate. The
computer operating system and word-processing program that I am using towrite this Epilogue will allow me to store this file within a folder of otherdrafts of this same chapter. I can store that folder within another folder that
contains the folders housing drafts of each of the other chapters of this book.
That folder can in turn be stored within another folder that stores folders
containing material for other books. Such hierarchical informational filingsystems are the undisputed products of intelligent design. Our knowledge ofthese computer filing systems induces a sense of déjà vu when we encounterthe hierarchical informational filing systems in biological organisms—as it
has for software engineers I know who have studied molecular biology. Thiseerie sense of déjà vu is nothing mystical, however. It derives from the quiterational human penchant for pre-theoretic uniformitarian reasoning,registering that we have experience of hierarchical systems for storing
information and that we are familiar with a type of cause that can produce
them: intelligent design.
On the other hand, the linear and hierarchical arrangement of the genome
is not what we would expect if the information in DNA and the chromosomes
had developed by undirected mutation and selection. Just as this mechanism
should have generated much more genetic noise than signal, because of its
reliance upon a random trial-and-error process, so too should we not expect
random mutation and selection to produce a nonrandom functionally specified
organization of genetic files along the DNA strand, or a similar arrangementof genetic files within folders, or folders within folders. Natural selection andrandom mutations (recombinations) might generate a few seemingly
nonrandom clusters of genes. But we would not expect this mechanism to
produce a system of genomic organization in which the location andarrangement of adjacent genes invariably assists, and indeed is essential to, afunctional outcome. Nor would we expect the arrangement of genes within acluster or the arrangement of clusters to always embody a clear andindispensable functional logic.
Mutation might occasionally produce a chance association of, forexample, a transcriptional regulatory element next to a protein-coding gene.
But we would not expect mutations to produce arrangements and associationsof genes such that cells would repeatedly deploy gene products to the righttissues at the right time and in the right amount. And yet the organization of
the genome allows the organism to express gene products with precisely suchtargeted and calibrated efficiency.
As University of Indiana evolutionary biologist Michael Lynch has argued
using standard population genetics, the size of breeding populations ofmulticellular organisms are simply not large enough to have afforded natural
selection sufficient opportunity to shape genomes into structures with the kindof hierarchically organized systems of information storage that they exhibit.
Lynch instead hopes that the structure of the genome can be explained by aneutralist theory of evolution based mainly on genetic drift.24 But by
removing natural selection as a potential shaping influence on the structure of
the genome, Lynch repairs to purely random mutations to explain theincredibly improbable arrangement and structure of the genome as a whole.
This scenario seems, if anything, less likely than one dependent on natural
selection.
In any case, the hierarchical organization and layering of many types ofinformation onto the same physical medium would seem to requireconsiderable forethought, precisely what natural selection by definition
cannot provide. And experience affirms that the hierarchical organization of
information evident in the genome constitutes another distinctive hallmark orsignature of intelligence, another feature of living systems for which
intelligent agency is the only known cause. Thus, intelligent design
constitutes the best, most causally adequate explanation for the origin of thisand other key features of the cell’s information-processing system,
underscoring the continuing and growing explanatory power of intelligent
design as a scientific theory of the origin of information.
3. Informational Context—Genomic and Extragenomic
In recent years, developmental and evolutionary biologists have discoveredthat coding sequences in the genome do not by themselves determine thefunction of gene products during embryological development. Instead,scientists have found that the larger informational context in which genes are
expressed often determines the specific function of the proteins they produce.
Biologists have shown this in part by taking a gene out of one kind oforganism and then expressing it in a radically different kind of organism.25
For example, the gene distal-less in Drosophila (an insect) and its homologue
in vertebrates, Dlx, regulate the development of very different appendages indifferent organisms. In sea urchins the gene regulates the development ofspines. In fruit flies, it regulates the development of compound limbs withexoskeletons and multiple joints. In vertebrates, by contrast, it regulates thedevelopment of another type of limb, one with multiple joints but an internalbony skeleton.26 Except insofar as these structures all exemplify a broad
general class, namely, appendages, they have little in common with eachother. As Stuart Newman, professor of cell biology at New York Medical
College, notes, “Insect and vertebrate appendages have little in common
anatomically other than being produced, in part, by outgrowth of the bodysurface.”27 Yet in each case the same gene plays a critical role in the
production of these different anatomical structures. The gene distal-less (orDlx) functions as a switch, but in each case a switch that regulates manydifferent downstream genes, leading to different anatomical features,
depending upon the larger informational context in which the gene finds
itself.
This was surprising to many evolutionary biologists because they had
long assumed on the basis of orthodox evolutionary theory that (a) genes
control the development of organisms and anatomical structures and that (b)homologous genes should, therefore, produce homologous organisms andstructures.28 Yet the anatomical structures produced by distal-less and Pax-6
are so different and they are found in such distantly related organisms, thatthey are unlikely to have evolved from a single common ancestor possessing
a precursor structure to each.29 There is no conceivable organism whose
appendages were ancestral precursors to echinoderm spines, arthropod
compound limbs, and vertebrate bony limbs. As biologists Douglas Erwin and
Eric Davidson note in reflecting on this general puzzle, “Although the heads,
hearts, eyes, etc., of insects, vertebrates and other creatures carry out
analogous functions, neither their developmental morphogenesis nor
functional anatomies are actually very similar if considered in any detail.”30
On the other hand, the way genes perform different functions based upon
the larger informational context in which they find themselves is not at all
unexpected from a design-theoretic point of view. We have extensive
experience of informational systems, designed by intelligent agents, thatexhibit exactly this feature of context-dependent modularity.
Consider any body of meaningful English text made of paragraphs,
sentences, and words. In such a text, the meanings of the lower-level modules—the words—are determined by the context in which the words occur.
English words have a range of associated meanings. This range is not
infinitely large: words cannot mean anything at all. Nor is it usuallycompletely discrete. Words rarely have only a single, univocal meaning.
Instead, the particular meaning a word assumes, among a set of allowable
meanings, is determined by the larger context in which the word occurs.
Consider two largely identical sequences of words that have completely
different meanings. First, “‘My, this cake is delicious,’ he said
enthusiastically.” Second, “‘My, this cake is delicious,’ he said sarcastically.”
The first seven words in each eight-word sentence are identical. Yet these twogroups of words have completely different meanings. Why? The final word ineach case provides important contextual information that determines themeaning of the other modular elements—the first seven words—in thesentence. Notice too that in the larger context of this paragraph, bothsentences have another function as well—not to express either myappreciation or disdain for a piece of cake, but to illustrate how context
determines the meaning of modular elements in intelligently arranged
communications.
A colleague of mine, Paul Nelson, illustrates how the context of a whole
informational system can determine the meaning or function of individual
modular elements in another way. He shows that the same forty-three words
used to write the conclusion of the Gettysburg Address can be rearranged towrite “An Anarchist Manifesto,” with a meaning diametrically opposite thatof Abraham Lincoln’s.31 What’s different in the two cases? Not the lower-level modules (i.e., the words). Instead, the difference is the overallarrangement of the words and the context it provides for interpreting themeaning of the individual words, the lower-level modular elements. Same
modular elements, completely different meanings.
Increasingly, it appears that modular genomic elements within biologicalsystems manifest this same context dependence. Yet according to standard
evolutionary models for the origin of biological form, this should not be. In
both chemical and biological evolutionary theory, novel organismal form
arises from the “bottom up” first as chemical building blocks and then new
genes and proteins determine higher-level biological form and function. Yet
the context dependence of these lower-level modular elements (genes andproteins) shows that their functions are determined by a larger informational
system and, presumably, only once this system is in place. Indeed, thefunction of many genes and proteins is determined “top-down” by the larger
system-wide informational and organismal context—by the needs of theorganism as a whole.
The question is inescapable: How could genes and proteins have survived,much less reproduced with variation, before there existed the extraordinarily
complex organismal context in which they alone appear to function? Though
this feature of genes seems puzzling given standard materialistic andreductionistic theories of evolution, it is entirely expected from a designtheoretic perspective. Why? Simply this: the function of modular elements in
intelligently constructed blocks of text (or software) routinely exhibits suchcontext dependence. In other words, we know of a cause of this feature of
information-rich systems and that cause is, again, intelligent design. Thus,
design readily explains—provides a cause known to produce—thisunexpected feature of life’s informational structure.
Extragenomic or Ontogenetic Information
Design can explain another surprising feature of life’s informational structureand hierarchy. Much of the information stored in the organism that determines
the context and function of genes is stored on other genes. But developmental
biologists in particular have learned that the cell stores other kinds ofinformation, so-called extragenomic (not stored in DNA), or ontogenetic,
information that plays a critical role in the development of the body plans oforganisms.
There is now a wealth of embryological evidence showing that DNA does
not wholly determine morphological form in organisms.32 DNA directs thesynthesis of proteins and RNAs. It also helps to regulate the timing andexpression of the synthesis of various proteins within cells. Yet DNA alonedoes not determine how individual proteins assemble themselves into larger
systems of proteins; still less does it, by itself, determine how cell types,
tissue types, and organs arrange themselves into body plans.33 Instead, otherfactors—such as the three-dimensional structure and organization of the cell
membrane and cytoskeleton, and the spatial architecture of the fertilized egg—play important roles in determining body-plan formation during
embryogenesis.
Two analogies may help clarify what is going on in organisms. At abuilding site, construction workers make use of many materials: lumber, wire,
nails, drywall, piping, and windows. Yet building materials do not determine
the floor plan of the house or the arrangement of houses in a neighborhood.
Similarly, electronic circuits are composed of many components, such as
resistors, capacitors, and transistors. But such lower-level components do not
determine their own arrangement in an integrated circuit. Instead, builders
have a blueprint or floor plan and electrical engineers have a wiring diagram,each of which determines the arrangement of lower-level parts.
Biological organisms also depend on higher-level information and ahierarchical arrangement of parts. Genes and proteins are made from simple
building blocks—nucleotide bases and amino acids—arranged in specificways. Cell types are made of, among other things, systems of specialized
proteins. Organs are made of specialized arrangements of cell types andtissues. And body plans comprise specific arrangements of specialized organs
and tissues. Yet the properties of individual proteins (or, indeed, the lowerlevel parts in the hierarchy generally) do not fully determine the organizationof the higher-level structures and organizational patterns.34 Nor does thegenetic information that codes for proteins determine these higher-levelstructures.
Instead, higher-level structural information appears to play a critical rolein the development of organisms. Developmental biologists do not know
where all this extragenomic information in the cell resides, but they havelocated some of it. For example, they know that the structure and location of
the cytoskeleton influence the patterning of embryos. Arrays of microtubules
help to distribute the essential proteins used during development to theircorrect locations in the cell. These microtubules themselves are made of many
protein subunits. Nevertheless, like bricks that can be used to assemble manydifferent structures, the tubulin subunits in the cell’s microtubules are
identical to one another. For this reason, it is not possible to predict thestructure of the cytoskeleton of the cell from the characteristics of the protein
constituents that form that structure.35 Neither the tubulin subunits nor thegenes that produce them account for the different shape of microtubule arraysthat distinguish different kinds of embryos and developmental pathways.

Instead, the structure of the microtubule array itself is determined by thelocation and arrangement of its subunits, not the properties of the subunits
themselves. And the location of specified target sites on the interior of the cell
membrane helps to determine the shape of the cytoskeleton and also
influences the development of organismal form. So does the position of thecentrosome, which “nucleates” or spurs the assembly of the microtubules thatform the cytoskeleton. Although both the membrane targets and thecentrosomes are made of proteins, the location and form of these structures is
not wholly determined by the proteins that form them. Instead, centrosome
structure and membrane patterns as a whole convey three-dimensional
structural information that helps determine the structure of the cytoskeleton
and the location of its subunits.36 Moreover, the centrioles that compose thecentrosomes replicate independently of DNA replication.37 The daughter
centriole receives its form from the overall structure of the mother centriole,
not from the individual gene products that constitute it.38 In ciliates,
microsurgery on cell membranes can produce heritable changes in membrane
patterns, even though the DNA of the ciliates has not been altered.39 Thissuggests that membrane patterns (as opposed to membrane constituents) are
impressed directly on daughter cells. In both cases, form is transmitted fromparent three-dimensional structures to daughter three-dimensional structures
directly and is not wholly contained in constituent proteins or genetic
information.40
Thus, in each new generation, the form and structure of the cell arise as
the result of both gene products and preexisting three-dimensional structure
and organization. Cellular structures are built from proteins, but proteins findtheir way to correct locations in part because of preexisting three-dimensional
patterns and organization inherent in cellular structures. Preexisting threedimensional form present in the preceding generation (whether inherent in thecell membrane, the centrosomes, the cytoskeleton, or other features of thefertilized egg) contributes to the production of form in the next generation.
Neither structural proteins alone nor the genes that code for them are
sufficient to determine the three-dimensional shape and structure of theentities they form. Gene products provide necessary but not sufficient
information for the development of three-dimensional structure within cells,organs, and body plans.41
This is not expected in standard views of evolutionary theory. NeoDarwinism has long sought to explain the origin of new information, form,and structure as a result of selection acting on randomly arising variation at alow level within the biological hierarchy, namely, within the genetic text. Yet
major morphological innovations depend on a specificity of arrangement at amuch higher level of the organizational hierarchy, a level that DNA alonedoes not determine. Yet if DNA is not wholly responsible for body-planmorphogenesis, then DNA sequences can mutate indefinitely, without regard
to realistic probabilistic limits, and still not produce a new body plan,suggesting the possibility of something else at work in the origin of major
morphological innovations.
Might intelligent design have played a role? There are reasons to consider
this possibility. Organisms contain both information-rich components (such asproteins and genes) and information-rich arrangements of those components
forming a rich multilayered informational hierarchy. We know that design
engineers can also produce hierarchical systems in which both individual
modules and the arrangements of those modules exhibit complexity andspecificity—information so defined. Individual transistors, resistors, andcapacitors exhibit considerable complexity and specificity of design; at ahigher level of organization, their specific arrangement within an integrated
circuit represents additional information and reflects further design.
Conscious and rational agents have, as part of their powers of purposive
intelligence, the capacity to design information-rich parts and to organize
those parts into functional information-rich systems and hierarchies. A richmultilayered informational hierarchy in life is, therefore, just what we might
have expected from an ID perspective. That biological organisms exhibit such
hierarchy further underscores the relevance of intelligent design as an
explanatory and interpretive framework for understanding biological systems.
New Research Questions
Clearly, the genome and the cell’s information-processing and storage systemmanifest many features—hierarchical filing, nested coding of information,
context dependence of lower-level informational modules, sophisticatedstrategies for increasing storage density—that we would expect to find if they
had been intelligently designed. Conversely, many of these newly discoveredfeatures are not readily explained by standard materialistic evolutionary
mechanisms.
Moreover, these informational features are found in higher-level
multicellular organisms as well as in single-celled prokaryotes. This suggests
an intriguing, if radical, possibility. It suggests that intelligent design may
have played a role in the origin of complex multicellular organisms, and that
mutation and selection, along with other undirected mechanisms ofevolutionary change, do not fully account for the origin of these higher formsof life. Might intelligent design have played a role in biological evolution—that is, in the origin or historical development of new living forms from
simpler preexisting forms? Given the centrality of information to livingsystems, and given that all forms of life, including complex multicellularorganisms, display distinctive hallmarks of intelligent design in theirinformational systems, there would now seem to be an increasing reason toconsider this possibility.
This possibility in turn suggests a number of research questions that are
not being addressed because of the limitations of the neo-Darwinian
perspective. We now know that organisms contain information of different
types at every organizational level, including ontogenetic or structural
information not encoded in DNA. Yet, according to neo-Darwinism, newform and structure arise as the result of information-generating mutations inDNA. In its population-genetics models of evolutionary change, neoDarwinism has long assumed a number of things about genes that we also
now know to be incorrect. For example, these models assume that genetic
information is context-independent, that genes independently associate, andthat genes can mutate indefinitely with little regard to extragenomic and otherfunctional constraints. In short, neo-Darwinism gives primacy to the gene as
the locus of biological change and innovation. In so doing, however, it
assumes a one-dimensional conception of biological information.
Consequently, neo-Darwinism provides little reason to consider orinvestigate (and every reason to ignore) the additional tiers of information andcodes that reside beyond the gene. On the other hand, advocates of intelligent
design not only acknowledge, but expect to find, sophisticated modes of
information storage and processing in the cell. Since the theory of intelligent
design treats the hierarchical organization of information as theoreticallysignificant, advocates of the theory have naturally shown intense interest in
the cell’s informational hierarchies and intricate modes of coding. Therefore,a design-theoretic perspective tends to encourage questions about thehierarchies of information in life that neo-Darwinists tend to ignore.
Such questions include: Where exactly does this ontogenetic information
reside? How does it affect the function of lower-level genetic modules? How
many types of information are present in the cell? How much ontogenetic
information is present in the cell? How do we measure this information, giventhat it is often structural and dynamic rather than digital and static? And how
mutable are various forms of non-DNA-based information—if at all?
We know that animal body plans are static over long periods of time. Is
this morphological stasis the result of constraints imposed upon mutability by
the interdependence of informational hierarchies? Are there other constraints—even probabilistic constraints operating at the level of the individual geneand proteins—that limit the transformative power of the selection andmutation mechanism? Given the phenomenon of “phenotypic plasticity”(individuals in a population with the same genotype that have different
phenotypes) and the recurrence of similar variations in the same species, howmuch variability in organisms is actually the result of preprogramming as
opposed to random mutations? If recurring variations result frompreprogramming, where does the requisite information for these programs
reside and how is it expressed? How many phenomena currently regarded as
examples of so-called neo-Lamarckian processes can be properly attributed topreprogrammed, intelligently designed, adaptive capacity?
All these questions arise naturally from a design-theoretic perspective andhave little place in a neo-Darwinian framework. Many are questions about thestructure, function, and composition of living systems themselves. Some arequestions about the efficacy of various evolutionary mechanisms—questions
about whether these mechanisms can explain various appearances of design
as well as an actual designing intelligence. What can mutation and selection
produce, and what can they not produce? Can selection and mutation produce
novel genes and proteins? New anatomical structures? Novel body plans? If
not, are there mechanisms or features of life that impose limits on biologicalchange? Or are there perhaps other materialistic mechanisms that have thecausal powers to produce novel forms of life? If not, and if the pervasive
hallmarks of intelligent design in complex organisms indicate actual design,
what does that imply about what else we should find in living systems or thefossil record? Are there perhaps other design patterns known from software
design or mechanical engineering that await description or discovery in living
systems or molecular machines? Are there patterns in the fossil record
indicative of intelligent activity?
If so, do they suggest that intelligent design played a role in the
production of novel body plans or smaller innovations in form? Is intelligent
design needed to build new forms of life that exemplify higher taxonomic
categories—such as orders, classes, and phyla? Is it also needed to build the
new forms encompassed by lower taxonomic categories such as genera andspecies? How much morphological change can undirected mechanisms suchas selection and mutation produce, and at what point, if any, would intelligent
design need to play a role? Such questions move from whether intelligenceplayed a role in the history of life to how, when, and how often intelligence
acted.
If a designing intelligence generates new form by infusing new
information into the biosphere, do we see evidence of that anywhere in thegeological time scale? If so, where, and how can we detect it? If intelligent
design played a role in the history of life after life’s initial origin, did thatdesigning agency act gradually or discretely? Did it affect a gradualtransformation of form from simple to more complex organisms? Or did that
intelligence affect more sudden transformations or innovations in biologicalform, thus initiating new and separate lines of genealogical descent? In otherwords, is the history of life monophyletic or polyphyletic? If the latter, howmany separate lines of descent or trees of life have existed during life’s
history, and what should the fossil record, embryological development,
comparative anatomy, and phylogenetic studies show in that case? If life’s
history is polyphyletic, how wide are the envelopes of variability in theseparate trees of life?
Conversely, if undirected evolutionary mechanisms are sufficient toaccount for the origin of all new forms of life, is it possible that the pervasive
signs of design in higher forms of life were preprogrammed to unfold fromthe origin of life itself? If design was thus “front-loaded” in the first simple
cell, what does that imply about the capacity of cells to store information for
future adaptations? And what should the structure and organization of theprokaryotic genome look like in that case?
Many of the preceding questions follow from considering theinformational signature of intelligence in life. But design arguments, such as
Michael Behe’s argument from irreducible complexity, suggest other kinds ofresearch questions. Are specific molecular machines irreducibly complex, as
Behe argues? If so, is intelligent design the only cause known to produce thisfeature of systems? Can mechanisms of “co-option” or “exaptation” explain
the origin of the flagellar motor and other molecular machines, as his critics
argue in recent scientific publications?42
Indeed, the controversy over Behe’s argument from irreducible
complexity has already motivated specific new lines of empirical inquiry andgenerated a number of new research questions. The argument of this book
based on the informational features of life raises many others. Many of theselines of inquiry are admittedly radical from an orthodox neo-Darwinian pointof view, and some scientists may not want to consider them. Nevertheless,they cannot argue that the scientists who do will have nothing to do. Nor canthey argue that the new genomics has weakened the case for intelligent designpresented in the main body of this book.

Appendix A
Some Predictions of Intelligent Design

Critics of intelligent design often argue that the theory cannot be tested,because it makes no predictions. The charge turns on a fundamental
misunderstanding of how historical scientific theories are tested. Primarily,such testing is accomplished by comparing the explanatory power ofcompeting hypotheses against already known facts. The theory of intelligent
design, like other theories about the causes of past events, is testable, and has
been tested, in just this way. That said, the theory of intelligent design also
has predictive consequences. Since the design hypothesis makes claims about
what caused life to arise, it has implications for what life should look like.
Moreover, the explanatory framework that intelligent design provides leads to
new research questions, some of which suggest specific predictions that are
testable against observations or by laboratory experiments.
Some of these predictions can help adjudicate proposals that invoke either
intelligent causes or materialistic mechanisms as explanations for various
features of life or events in life’s history. Other predictions can help
discriminate between competing ideas of how a designing intelligenceinfluenced the history of life—for instance, between design hypotheses thataffirm universal common ancestry and those that envision more discrete or
discontinuous intelligent activity in the history of life. Indeed, depending
upon how scientists envision intelligent design playing a role in the history oflife, they may formulate different kinds of design hypotheses, each entailing
different though testable predictions.
Some predictions (those that discriminate the explanatory power ofintelligent causes and materialistic mechanisms) will necessarily function as
tests of the causal efficacy of mechanisms of evolutionary change. Since
design hypotheses are often formulated as strong claims about intelligence as
the best causal explanation of some particular phenomenon, these hypotheses
entail counterclaims about the insufficiency of competing materialistic
mechanisms. But such claims also entail predictions. The claim that
intelligent design constitutes the best explanation of particular informationalfeatures of organisms leads inevitably to the claim that other competing
causal hypotheses will not demonstrate the power to produce these effects—
just as they may not have done so to this point. Similarly, the claim that
intelligent design constitutes the best explanation of the integrated or

“irreducible complexity” of molecular machines entails a prediction about the
insufficiency of competing materialistic causes for these systems.
In addition to predictions about what future evidence will show about thecausal powers of various processes, intelligent design also generates
predictions about what we are likely to find in living systems as we
investigate them. We have extensive experience-based knowledge of the kindsof strategies and systems that designing minds devise to solve various kindsof functional problems. We also know a lot about the kinds of phenomena thatvarious natural causes produce. For this reason, the theory of intelligent
design makes predictions about the kinds of features we are likely to find inliving systems if they were in fact intelligently designed.
Other types of predictions flow from considering the possibility that
intelligence influenced, directed, or guided the history of life, either graduallyor discretely. Various ID hypotheses generate different predictions about
what, for example, the fossil record or phylogenetic studies should show.
Depending upon how ID theories conceive of the designing intelligenceaffecting the history of life over time and what other characteristics they
attribute to this intelligence (such as benevolence, for example), design
hypotheses may make specific claims about the causes of so-called
dysteleology, or bad design. These claims may entail specific empiricalpredictions as well.
Thus, intelligent-design hypotheses may generate several distinct types ofpredictions: predictions about causal powers, or lack thereof, of various
mechanisms; predictions about the structure, organization, and functional
logic of living systems; predictions about what evidence will show about thehistory of life; and predictions about the causes of putatively bad design.
Consider a dozen or so ID-based predictions, each of which exemplifies one
or more of these types. I start with two predictions directly relevant to testing
the main arguments made in this book.
The Causal Powers of Materialistic Mechanisms
The theory of intelligent design makes predictions about what the evidence
will show about the causal powers of various material mechanisms.
According to the hypothesis developed in this book, intelligent design is thebest explanation of the origin of the information necessary to produce the firstlife. To make this case, I argued that no purely physical or chemical entity orprocess had demonstrated the causal powers to produce complex specified
information—where “complex” refers to a specific amount of information(roughly 500 bits or more) and the inverse of a probability measure(Dembski’s universal probability bound). An obvious prediction follows from

this claim—in particular, that large amounts of new functionally specified
information (over 500 bits) will not accumulate as a result of random orundirected natural processes and that no such process will be discovered thatcan produce over 500 bits of new specified information starting from purely
physical and chemical antecedents. My theory acknowledges that small
amounts of specified information can occasionally arise by random processes,
but that the amount of information that can be generated is limited by theprobabilistic resources of the universe. This prediction can be clearly falsified
by the discovery of an undirected physical or chemical process that cangenerate over 500 bits of functionally specified information.
This general prediction entails other more specific ones. For example,
based upon the considerations just discussed, the theory of intelligent design
developed here predicts that genetic algorithms simulating the power of
undirected evolutionary processes will not produce more than 500 bits of newcomplex specified information (information not supplied by the programmer
in the algorithm)—unless, that is, an intelligent programmer provides
necessary “active information.” It further predicts that a close examination ofprograms that appear to produce novel complex specified information will
reveal either (a) inputs from programmers that account for the putative
creation of new complex specified information beyond what could have been
expected given the available probabilistic resources, or (b) a lack of biologicalrealism in the computer simulation, or (c) both. Recently, William Dembski
and Robert Marks have produced peer-reviewed papers performing
informational accounting on genetic algorithms that confirm these predictionsin specific case studies.1
Typically, genetic algorithms may lack realism (1) by providing the
program with a target sequence, (2) by programming the computer to selectfor proximity to future function rather than actual function, or (3) by selecting
for changes that fail to model biologically realistic increments of functionalchange, increments that reflect the extreme rarity of functional sequences ofnucleotide bases or amino acids in the relevant sequence space (see Chapter
13).
The theory of intelligent design presented in this book entails specificpredictions about RNA-world research as well. For example, it predicts thatinvestigations of the properties of ribozymes (RNA catalysts) will reveal an
insufficient number of enzymatic functions to sustain a primitive cell or evenan alternative RNA-based system of protein synthesis, demonstrating thatRNA could not have performed both the necessary enzymatic functions ofmodern proteins and the information-storage functions of nucleic acids that
the RNA-world scenario envisions. Further, the theory of intelligent design
developed in this book also predicts that putatively “successful” ribozyme
engineering experiments—in particular, experiments that appear to enhancethe replicase activity of ribozymes—will require significant sequencespecificity in the RNA bases in any functional ribozyme and that, upon
examination, active information provided by ribozyme engineers will accountfor much of this sequence specificity (in particular, the amount beyond what
could have been expected to arise spontaneously given available probabilistic
resources).
The Structure, Organization, and Functional Logic of Living Systems
ID also makes predictions about the structure, organization, and functional
logic of living systems. In 2005, University of Chicago bacterial geneticist
James Shapiro (not an advocate of intelligent design) published a paper
describing a regulatory system in the cell called the lac operon system.2 Heshowed that the system functions in accord with a clear functional logic thatcan be readily and accurately represented as an algorithm involving a series of
if/then commands. Since algorithms and algorithmic logic are, in our
experience, the products of intelligent agency, the theory of intelligent design
might expect to find such logic evident in the operation of cellular regulatory
and control systems. It also, therefore, expects that as other regulatory andcontrol systems are discovered and elucidated in the cell, many of these also
will manifest a logic that can be expressed in algorithmic form.
Molecular biologist Jonathan Wells has also used design reasoning toinfer the possible existence of a previously undiscovered molecular machine
within the cell. While researching the causes of cancer, Wells became
intrigued with the possibility that an organelle in the cell called thecentrosome may play a significant role in this disease.
In animal cells, the centrosome sits alongside the nucleus. It serves as thefocus of the microtubules that give the cell its shape and provides tracks along
which proteins from the nucleus are moved to their proper locations
elsewhere in the cell. Centrosomes play a role in the process of cell division,and some cancer researchers—noting that cancer cells usually containdamaged, abnormal, or extra chromosomes—have suggested that centrosomal
defects may be the first stage in cancer.
Many cancer researchers disagree. Influenced by neo-Darwinism, they
believe that cancer is caused by DNA mutations. As a skeptic of neoDarwinism, Wells was inclined to doubt this and to consider the centrosomal
theory as a possibility. As he studied the literature on cancer, he surmised that
there was no consistent pattern of mutations in cancer. As he studied
centrosomes, he became even more intrigued with the possibility that they
may play a significant role in cancer.
When animal cells divide, they rely on an intracellular apparatus calledthe “spindle.” Looking somewhat like the barrel-shaped pattern formed byiron filings around the two poles of a magnet, the spindle has a centrosome ateach pole. The already duplicated chromosomes are contained within it, andbefore the cell divides, a “polar ejection force” helps to move thechromosomes to the midpoint of the spindle. After the chromosomes areproperly aligned at the midpoint, the calcium level inside the cell rises
abruptly. Then the chromosomes split into two equal sets and move to thespindle poles—the centrosomes.
Suspecting that centrosomes produce the polar ejection force, Wells
turned his attention to them. Each animal-cell centrosome contains two“centrioles,” cylindrical structures only half a millionth of a meter long. Eachcentriole looks like a tiny turbine with nine tilted blades. As an advocate ofintelligent design and a critic of undirected evolution, Wells assumed
provisionally that these structures actually were designed to be turbines. From
there, Wells used reverse engineering to predict other features of centrioles
and their action in both normal and cancerous cells.
First, hints from other centriole studies—together with engineering
considerations—led Wells to suggest that each centriole contains an
Archimedes screw, a helical pump that draws fluid into one end of the turbine
and pushes it out through the blades. Second, Wells postulated that dyneinmotor molecules inside the centriole would provide the force to turn thehelical pump. Third, Wells concluded from engineering considerations thatsuch an arrangement would operate like a laboratory vortexer, a common
device that produces a wobble to swirl the contents of a test tube.
Doing the math, Wells deduced that centrioles could rotate tens of
thousands of times a second. A pair of centrioles at both ends of the spindle
could produce the polar ejection force that moves chromosomes to the
midpoint before cell division. The rise in intracellular calcium that
accompanies chromosome separation would shut off the dynein motors,
thereby turning off the polar ejection force. This would permit thechromosomes to move poleward without being pushed away at the same time.
But if the centriole turbines fail to turn off, the continuing polar ejection force
would subject the chromosomes to unusual stress and could cause the damage
now thought by some researchers to be the first step in cancer. The fact that
there is a correlation between calcium deficiency and cancer is consistent withthis hypothesis.
Wells is currently testing his hypothesis experimentally. If corroborated,the hypothesis could aid in the prevention and early diagnosis of cancer.
Wells’s work also shows how an intelligent-design perspective can lead tonew hypotheses, testable predictions, and new lines of research. The outcomeof his work won’t directly confirm or disconfirm intelligent design, or neoDarwinism for that matter, since the truth of neither theory depends upon

whether any specific structure is or is not a turbine. But it illustrates how an
ID perspective can prove fruitful for generating new testable hypotheses andpredictions about the structure and function of the cell (as well as the causesof cellular malfunctions when they occur).
Wells himself has noted that scientists operating out of a Darwinian
framework could have formulated a similar hypothesis. Nevertheless, he also
notes that the underlying assumptions of Darwinians (about the role ofmutations in DNA) disinclined them to do so. Conversely, Wells’s convictions
about intelligent design inclined him to suspect that appearances of design
might in fact be evidence of real design, which led him to suspect thepresence of a molecular machine in the centriole. It also led him to use
reverse engineering to develop a testable hypothesis about its structure andfunction. As he explained in an abstract to a scientific article about his
hypothesis, “Instead of viewing centrioles through the spectacles of molecularreductionism and neo-Darwinism, this hypothesis assumes that they areholistically designed to be turbines…. What if centrioles really are tiny
turbines? This is much easier to conceive if we adopt a holistic rather than
reductionistic approach, and if we regard centrioles as designed structures
rather than accidental by-products of neo-Darwinian evolution.”3
The History of Life
Various ID hypotheses also generate predictions about the history of life.
Advocates of design who think that the information necessary to produce newforms of life was front-loaded in the first cell might predict that prokaryotic
cells would demonstrate the capacity to carry amounts of genetic information(information in excess of the needs of those cells) or that such cells would
retain vestiges of having done so. Similarly, advocates of design who thinkthat the information necessary to produce new forms of life was front-loadedin the initial conditions of the universe and the fine-tuning of the laws ofphysics might predict that physical and chemical laws would demonstratebiologically relevant self-organizing tendencies. Since both these design
hypotheses favor a monophyletic view of the history of life, they would
predict, along with neo-Darwinism, that the traditional evidences for universal
common descent (from biogeography, paleontology, embryology, comparative
anatomy, and genomics) would withstand challenge. Indeed, such theories
generate a host of specific predictions about what the evidence in each ofthese subdisciplines of biology should show. These are not, of course,
predictions that provided reasons for preferring a front-loaded design
hypothesis to a materialistic explanation for the origin of the first life. The
evidence and predictions considered already do that. Instead, they arepredictions that could help decide the merits of hypotheses of front-loaded
design versus those of discrete design.
Conversely, advocates of design who envision a designing intelligenceacting discretely at intervals across the geological time scale tend to favor a
polyphyletic rather than a monophyletic view of the history of life. Thus, they
predict emerging patterns of evidence from these same subdisciplines that
contradict a traditional monophyletic view and support a polyphyleticinterpretation of the history of life.
Design theorists who expect a polyphyletic rather than a monophyleticgeometry for the history of life often do so because of the functional
integration and interdependence of parts exhibited in living systems andbecause of their understanding of how such functionally integrated systems
constrain changes in form. The constraints principle of engineering assertsthat the more functionally integrated a system is, the more difficult it is toperturb any part of the system without destroying the whole. Because many
ID theorists think organisms were designed as functionally integrated systems
comprising many parts and subsystems, they think it is difficult to alter thesesystems significantly without destroying them, particularly when suchalterations consist of a series of blind mutations “in search of” increased
functionality. Thus, these ID theorists predict that there should be significant
and discoverable limits to the amount of change that various organisms canendure and that major body plans should exhibit significant stasis over time in
the fossil record.
Design hypotheses envisioning discrete intelligent action also predict apattern of fossil evidence showing large discontinuous or “quantum”
increases in biological form and information at intervals in the history of life.
Advocates of this kind of design hypothesis would expect to see a pattern ofsudden appearance of major forms of life as well as morphological stasis.4
Since designing agents are not constrained to produce technologicalinnovations in structure from simpler precursors or to maintain the function ofthese simpler precursors through a series of intermediate steps, they would
also predict a “top-down” pattern of appearance in which large-scaledifferences in form (“disparity” between many separate body plans) emerge
suddenly and prior to the occurrence of lower-level (i.e., species and genus)
differences in form. Neo-Darwinism and front-loaded design hypotheses
expect the opposite pattern, a “bottom-up” pattern in which small differencesin form accumulate first (differentiating species and genera from each other)and then only much later building to the large-scale differences in form thatdifferentiate higher taxonomic categories such as phyla and classes.
Both types of design hypotheses also make different predictions about
what phylogenetic analyses should show. Those design advocates who (likeneo-Darwinists) accept common descent predict that phylogenetic analysesperformed on different molecules and structures in the two species should
yield harmonious trees of life—trees that indicate similar degrees ofdifference, relatedness, and divergence from a common ancestor regardless ofwhich molecules or anatomical structures are compared. According to thetheory of common descent, since all the molecules and subsystems of anorganism evolved from the same common ancestor, the phylogenetic trees
generated from different molecules and structures in the same two species
should agree regardless of which molecules or subsystems are compared.
For this same reason, design advocates who affirm polyphyly (and with it
more discrete modes or infusions of design) predict that phylogenetic
analyses would often yield conflicting trees of life—that is, dissimilar
measures of difference, relatedness, and divergence depending upon whichmolecules or anatomical structures are compared in the same two species.
They predict this for another reason. Many design advocates see similarities
in functional biomacromolecules and anatomical structures as resulting fromfunctional or engineering considerations rather than common ancestry. Since
intelligent agents have the freedom to combine modular elements andsubsystems in unique ways from a variety of information sources, we should
expect phylogenetic analyses of diverse systems and molecules to generatesome conflicting trees.
Clearly, there are several possible hypotheses about how design played arole in the history of life. Since each of these hypotheses has different
empirical consequences, design hypotheses can generate different andcompeting predictions about what different classes of evidence should show.
There is nothing unusual about this, however. Philosophers of science havelong recognized that hypotheses generate predictions when they are conjoinedwith so-called auxiliary hypotheses, that is, other claims or suppositions about
the world. In this, design advocates are no different from advocates of otherscientific theories. Some may conjoin the hypothesis of design with amonophyletic hypothesis about the history of life; some may conjoin it with apolyphyletic view or with other claims about the world or life (such as theconstraints principle of engineering) in order to generate different though still
specific and testable predictions. These differing predictions do notdemonstrate that intelligent design is incoherent. They merely make testing
and assessing the relative merits of competing design hypotheses possible.
Dysteleology, or Bad Design
The theory of intelligent design generally affirms that complex biologicalstructures were designed for functional reasons. Thus, it predicts that the
study of supposedly “dysteleological” or “poorly designed” structures will

reveal either (a) functional reasons for their design features or (b) evidence ofdegenerative evolution—that is, evidence of decay of an otherwise rationaland beneficial original design. Neo-Darwinists have argued, for example, thatthe backward wiring of the vertebrate retina exhibits a suboptimal or baddesign, one ill-befitting an intelligent designer. Design theorists havechallenged this and predict that further study of the anatomy of the vertebrate
retina will reveal functional reasons for its nonintuitive design parameters.
Biologists George Ayoub and Michael Denton have identified a number offunctional reasons for the design of the vertebrate retina confirming thisprediction.5 Ayoub has shown, for example, that the vertebrate retina provides
an excellent example of what engineers call constrained optimization, in
which several competing design objectives are elegantly balanced to achieve
an optimal overall design.
The theory of intelligent design also predicts that instances of “bad”
design in nature may turn out to be degenerate forms of originally elegant or
beneficial designs. Critics of design have pointed to the existence oforganisms such as virulent (disease-producing) bacteria to refute the ID
hypothesis, arguing that an intelligent and beneficent designer would not havemade such organisms. Some design theorists (in this case, those who hold thatthe designer is both intelligent and benevolent) predict that genetic studies
will reveal that virulent bacterial systems are degenerative systems that haveresulted from a loss of aboriginal genetic information.6 University of Idaho
microbiologist Scott Minnich, an ID advocate, has specifically predicted thatthe virulence capacity in Yersinia pestis, the bacterium that caused the black
plague in medieval Europe, resulted from genetic mutations that stopped itfrom manufacturing molecules and structures recognized by the humanimmune system. He is currently conducting experimental tests of thishypothesis. He and his team already have shown that the more limited
virulence capacity of Yersinia pseudotuberculosis (a bacterium that causes
gastroenteritis) resulted from the mutational degradation of genes that
produce flagellin, a protein that the human immune system recognizes inflagellar motors of bacteria. Minnich and his team have found that virulencein Yersinia pseudotuberculosis can be reduced by restoring its gene for
producing flagellin.7 This plus a growing body of data showing that virulencecapacity in bacteria generally results from a loss of genetic information haveprovided a significant initial confirmation of the ID-generated prediction
about the cause of bacterial virulence.8
The Efficacy of Causal Mechanisms and the Features of Living Systems
Theories of intelligent design also sometimes link considerations of causal

adequacy to predictions about what we should find in the cell or the genome.
These predictions test whether intelligent causes or competing materialistic
mechanisms are more likely to have caused the origin of some system by
explicating how the two modes of causation are different and then showing
how each would produce different features in some part of a living system.
Our previous discussions about nonprotein-coding DNA illustrate this.
Since neo-Darwinism affirms that biological information arises as the resultof an undirected trial-and-error process, it predicts the existence of vast
regions of nonfunctional DNA in the genome. By contrast, advocates ofintelligent design think that DNA sequences arose mainly as the result of
purposeful intelligence. They predict, therefore, that nonprotein-coding DNA
should perform important biological functions. Thus, ID and neo-Darwinism
affirm competing causes of the origin of biological information, both ofwhich have different implications for what scientists should find as the
genome.
The debate about the origin of irreducibly complex molecular machinesprovides another example of this kind of prediction. In his book Darwin’s
Black Box, biochemist Michael Behe argues that the many miniaturemachines and circuits that have been discovered in cells provide strong
evidence for intelligent design. A crucial part of Behe’s argument for
intelligent design involves his attempt to show that miniature machines, suchas the now infamous bacterial flagellar motor, could not have developed from
simpler precursors in a gradual step-by-step fashion. In Behe’s view, thecoordinated interaction of the many parts of the flagellar motor resulted
ultimately from an idea in the mind of a designing intelligence, not from aprocess of gradual step-by-step evolution from a series of simpler material
precursors.
Behe’s critics, however, such as biologist Kenneth Miller of Brown
University, have suggested that the flagellar motor might have arisen via a
different causal pathway. They suggest that the flagellar motor might havearisen from the functional parts of other simpler systems or from simpler
subsystems of the motor. They have pointed to a tiny molecular syringe calleda type-3 secretory system (or T3SS). They note that the T3SS usuallyfunctions as part of the flagellar motor, but it is sometimes found in bacteriathat do not have the other parts of the flagellar motor. Since the type-3
secretory system is made of approximately ten proteins that have close
homologues in the thirty-protein flagellar motor, and since this tiny pumpdoes perform a function, Miller intimates that the flagellar motor might havearisen from this smaller pump.9 Behe, however, remains convinced that theflagellar motor is the aboriginal system. To defend Behe, other ID theorists
have suggested that when the type-3 secretory system appears in isolationfrom the other parts of the flagellar motor, it does so as the result of

degenerative evolution—that is, as the result of a loss of the genetic
information necessary to produce the other parts of the motor.
Thus, these two different views of the T3SS imply something different
about the relative age of the genes that produce the flagellar motor and theT3SS, respectively. The co-option theory predicts that the genes that produce
the T3SS syringe should be older than the genes that produce the flagellar
motor, since the syringe in this view is a precursor system. The hypothesis
from design and degenerative evolution predicts the opposite—that the genesof the flagellar motor should be older than those of the T3SS. Thus, the twotheories posit causal histories for these molecular machines that contradict
each other, and both make testable predictions about features of the systems(the age of the genes) as a result.
As it happens, phylogenetic analyses of the distribution of flagellar
systems in bacteria now make it possible to assess the relative age of two
suites of genes. These phylogenetic studies suggest that the flagellar motor
genes are older than the T3SS genes, thus providing an initial confirmation of
the design-theoretic hypothesis about the origin of the flagellar motor.10
There is an interesting twist to this story, however—one that underscores
how intelligent design leads to many new and unresolved research questions.
There is another design hypothesis about the flagellar motor and the T3SS. It
envisions both systems as the products of independent instances of design,
despite the similarity of the T3SS to parts of the flagellar motor. It predicts
that if the T3SS were designed independently of the flagellar motor, then weought to find many unique (nonhomologous) genes in the T3SS—genes thatexhibit little similarity to those found in the flagellar system. It is now known
that the T3SS does have several such nonhomologous genes, confirming thisprediction of a separate ID hypothesis. So did the T3SS devolve from theflagellar motor, or did it arise independently by a separate act of design? Thisis another research question generated by the theory of intelligent design.
Clearly, further experimental tests are needed to discriminate between thesetwo competing design hypotheses.
Here’s another prediction of this type that has also generated aninteresting program of experimental research. If intelligence played a role in
the origin of new biological forms after the first life, it likely would have done
so by generating the biological information necessary to produce these forms.
If so, intelligent design would have played a role in either the origin of new
genes and proteins or the origin of extragenomic information, or both. Eitherway, this design hypothesis implies that the undirected mechanism of random
mutation and selection is not sufficient to produce the information necessary
for such biological innovation. Thus, this particular design hypothesis would
predict that mutation and selection lack the capacity to produce fundamentally
new genes and proteins. (Again, those who object that this is a merelynegative claim against neo-Darwinism rather than a positive prediction of
intelligent design misunderstand the fundamentally comparative nature ofhistorical scientific theories, which take the form of arguments to the best
explanation.)
Douglas Axe, whom I discussed in Chapter 9, has been intrigued withintelligent design since the early 1990s. He devised a way to test this IDinspired prediction with a program of rigorous experimental work—work that
he first performed at Cambridge University and continues to perform at
Biologic Institute in Redmond, Washington.
In developing this test, Axe reasoned as follows. According to neoDarwinism new biological information arises as natural selection acts on
functionally advantageous mutations in genes. To produce any fundamentallynew biological forms these mutations would—at the very least—have to
produce a number of new proteins. But natural selection can act only on what
mutations first generate. Thus, for mutation and selection to produce newfunctional proteins or protein folds—the smallest unit of selectable function—
new proteins or protein folds must first arise by chance—that is, by random
mutation. If the probability of this were extremely low—beyond the reach ofavailable probabilistic resources—then this would undermine the plausibilityof the neo-Darwinian mechanism and confirm Axe’s own ID-based
expectation of its inadequacy.
Thus, as a specific test of the efficacy of the neo-Darwinian mechanism(as well as the chance origin of information in a prebiotic setting), Axe posed
the question: How rare or common are functional protein folds within theircorresponding amino acid–sequence space? He realized that if functionalsequences were common enough for mutations to stumble upon them
relatively easily (within the time required for relevant evolutionarytransitions), mutation and selection might be able to build otherwise
extremely improbable structures in small incremental steps. On the other
hand, if functional proteins are extremely rare within sequence space, suchthat mutations will not have a realistic chance of finding them in the available
time, selection will have little or nothing to work on, undermining its ability
to produce biological information.
It’s important to emphasize that Axe’s prediction follows from thepremise that intelligent design played a role in the origin of new genes andproteins during biological (or chemical) evolution. Since the case forintelligent design as the best explanation for the origin of biologicalinformation necessary to build novel forms of life depends, in part, upon theclaim that functional (information-rich) genes and proteins cannot be
explained by random mutation and selection, this design hypothesis implies
that selection and mutation will not suffice to produce genetic informationand that, consequently, functional sequences of amino acids within protein-sequence space will be extremely rare rather than common. Axe’s
mutagenesis experiments have tested, and continue to test, this prediction of
ID theory. As noted in Chapter 9, Axe has shown that the ratio of functionalamino-acid sequences to nonfunctional amino-acid sequences is extremely
small, 1 in 1074 for a protein fold 150 amino acids in length. Since most new
proteins exceed 150 amino acids in length, and since fundamentally new lifeforms require many new proteins, this experimental result, published in the
Journal of Molecular Biology, provides an initial confirmation of Axe’s IDinspired prediction.11
Of course, predictions about the insufficiency of materialistic mechanisms
do not form the whole basis of the case for intelligent design—whether as atheory of chemical or biological evolution. To establish a design hypothesis as
a best explanation requires more than a confirmed prediction about theinadequacy of a competing causal explanation. Instead, it requires positive
evidence for the efficacy of an intelligent cause and refutations of otherrelevant causal hypotheses using either predictive methods of testing,evaluations of explanatory power, or both. For this same reason, a single
failed prediction rarely falsifies a theory. How well a theory explains (orpredicts) the preponderance of relevant data is usually a better gauge of its
merit. In Chapter 15 and the Epilogue, I showed that intelligent design
exhibits broad explanatory power, at least as a hypothesis about the origin of
the first life. Even so, any claim that intelligent design best explains someparticular feature of life also entails a prediction about some relevant body ofevidence showing the inability of competing hypotheses to explain that samefeature. For this reason, and others, design hypotheses do make predictions.
Lest there be any doubt about this, I summarize a number of the key
predictions of the preceding discussion in the following list:
A Dozen ID-Inspired Predictions• No undirected process will demonstrate the capacity to generate 500
bits of new information starting from a nonbiological source.
• Informational accounting will reveal that sources of active
information are responsible for putatively successful computer-based
evolutionary simulations.
• Future experiments will continue to show that RNA catalysts lackthe capacities necessary to render the RNA-world scenario plausible.
• Informational accounting will reveal that any improvements inreplicase function in ribozymes are the result of active information

supplied by ribozyme engineers.
• Investigation of the logic of regulatory and information-processing
systems in cells will reveal the use of design strategies and logic thatmirrors (though possibly exceeds in complexity) those used insystems designed by engineers. Cell biologists will find regulatory
systems that function in accord with a logic that can be expressed as
an algorithm.
• Sophisticated imaging techniques will reveal nanomachines(turbines) in centrioles that play a role in cell division. Other evidence
will show that malfunctions in the regulation of these machines are
responsible for chromosomal damage.
• If intelligent design played a role in the origin of life, but not
subsequently, prokaryotic cells should carry amounts of genetic
information that exceed their own needs or retain vestiges of having
done so, and molecular biology should provide evidence of
information-rich structures that exceed the causal powers of chance,
necessity, or the combination of the two.
• If a designing intelligence acted discretely in the history of life, thevarious subdisciplines of biology should show evidence of polyphyly.
• The fossil record, in particular, should show evidence of discrete
infusions of information into the biosphere at episodic intervals aswell as a top-down, rather than bottom-up, pattern of appearance of
new fossil forms.
• If an intelligent (and benevolent) agent designed life, then studies ofputatively bad designs in life—such as the vertebrate retina andvirulent bacteria—should reveal either (a) reasons for the designs thatshow a hidden functional logic or (b) evidence of decay of originally
good designs.
• If the flagellar motor was intelligently designed and the type-3
secretory system devolved from it, the genes that code for thebacterial flagellar motor should be older than those that code for theproteins in the T3SS, and not the reverse. Alternatively, if the T3SS
and the flagellar motor arose by design independently, T3SS shouldhave unique (nonhomologous) genes that are not present in the
genome for the flagellar motor.
• The functional sequences of amino acids within amino acid–sequence space should be extremely rare rather than common.

Appendix B
Multiverse Cosmology and the Origin of Life

Scientists have increasingly recognized that the probabilistic resources of theobservable universe are insufficient to explain—by chance alone—the originof a minimally complex cell or even a self-replicating system of RNA
molecules (or even, for that matter, a single protein of modest length). In
response, some scientists have sought to explain the origin of life by invoking
other materialistic mechanisms or self-organizational processes. But as noted
in Chapters 11–14, theories of this kind have also fallen on hard times. As aresult, a few scientists have looked beyond our universe for additional
probabilistic resources by which to render a chance explanation for the originof life more plausible.
In May 2007, Eugene Koonin, of the National Center for Biotechnology
Information at the National Institutes of Health, published an article in
Biology Direct entitled “The Cosmological Model of Eternal Inflation and theTransition from Chance to Biological Evolution in the History of Life.” In it,
Koonin acknowledges that neither the RNA world nor any other materialistic
chemical evolutionary hypothesis can account for the origin of life, given theprobabilistic resources of the entire universe. As he explains: “Despite
considerable experimental and theoretical effort, no compelling scenarios
currently exist for the origin of replication and translation, the key processes
that together comprise the core of biological systems and the apparent
prerequisite of biological evolution. The RNA World concept might offer thebest chance for the resolution of this conundrum, but so far cannot account for
the emergence of an efficient RNA replicase or the translation system.”1
To address this problem, Koonin proposes an explanation for the origin oflife based purely on chance. His particular chance explanation, however, does
not refer to any process taking place on earth or even within the observablecosmos. Instead, he posits the existence of an infinite number of other lifecompatible universes, since, he argues, the existence of such universes would
render even fantastically improbable events (such as the origin of life)
probable or even inevitable.
To justify his postulation of other universes, he invokes a model ofcosmological origins based on inflationary cosmology dubbed the “manyworlds in one” hypothesis by cosmologist Alexander Vilenkin. According to
inflationary cosmology, in the first fraction of a second after the big bang ouruniverse experienced an exponentially rapid rate of expansion, after which its
expansion settled down to a more sedate pace. Inflationary cosmology was
originally proposed in order to explain two features of the universe that werepuzzling from the perspective of standard big-bang cosmology—its
uniformity (homogeneity) and its flatness.
By homogeneity, cosmologists mean that the universe looks the same toall observers, no matter where they are located. One aspect of thishomogeneity is the uniformity of cosmic background radiation, which has thesame temperature throughout the observable cosmos. This is a problem instandard big-bang cosmology. According to the big-bang theory, up until
about 300,000 years after the beginning of the universe, the photons in the
background radiation would have been bouncing off the electrons in the hot
plasma that filled the entire universe. At that point the universe would havecooled enough for electrically neutral atoms to form, releasing thebackground radiation. This radiation eventually reached us, giving us apicture of the universe at around 300,000 years of age.
The puzzling thing about this radiation is that it has the same temperaturein every direction to about one part in a hundred thousand. This implies thatthe universe at 300,000 years old was incredibly uniform in temperature,
which in turn would have required very precise initial conditions. It follows
that the observed uniformity of the background radiation can be explained in
the ordinary big-bang scenario only by postulating that the initial state of theuniverse was one of almost perfect uniformity in the temperature anddistribution of the plasma, which in turn requires a very precisely fine-tuned
initial explosion.2
A homogeneous universe is called “flat” if it is balanced between eventual
gravitational collapse and eternal expansion; in such a case its geometry
would be precisely Euclidean and space would not be curved. The universe
achieves such flatness when the actual mass density in the universe is very
close to the critical mass density (the density required to halt the expansion of
the universe)—that is, if the ratio between the actual and critical massdensities is close to one. In our universe, the ratio of these two quantities isever so slightly less than one. As a result, our universe will keep on
expanding without a gravitational recollapse, and space has hardly any overall
curvature. That these values were so precisely balanced is surprising from thestandpoint of standard big-bang theory, because, again, for this balance toarise, the universe would have needed to have very finely tuned initialconditions.
Inflationary cosmology attempts to explain the horizon problem(homogeneity) not as the result of these finely tuned initial conditions (though
it does invoke special conditions of its own; see below), but instead as aconsequence of an early, exponentially rapid rate of cosmic expansion.

According to the inflationary model, during the first fractions of a second
after the big bang the temperature of the universe had a chance tohomogenize. Then the rapid expansion of the universe distributed thishomogeneous radiation throughout the observable universe. It also pushed
any remaining inhomogeneity beyond the edge of the observable universe.3
In current models, inflation begins at around 10–37 seconds after the bigbang and lasts until 10–35 seconds, during which space itself expands by afactor of 1060 or so. At the beginning of the inflationary epoch the observable
universe was, say, about 10–60 meters in size and at the end of it about a meter
across. At the start of inflation, however, the horizon size (the distance lighttraveled since the big bang) was 10–37 light-seconds, which is far larger than
the tiny patch that was destined to grow into our observable universe. Thus,
the inflationary process not only distributed the homogeneous backgroundradiation throughout the observable universe, it also distributed any remaining
inhomogeneity beyond the edge of the observable universe.
Inflation explains the near flatness of the universe as a consequence of thehyper-expansion as well. During the inflationary epoch, all the distances in
the universe increased by a measure of 1060, which means the radius of theobservable universe increased by this factor as well. Suppose the fourdimensional space-time of the universe prior to inflation had positive
curvature, like the surface of a balloon does in three dimensions, and that its
radius was a billionth of a meter (a nanometer). After inflation, its radiuswould be 1051 meters, or about 10 billion trillion trillion light-years. Just likeinflating a balloon to larger and larger sizes makes a small patch of it lookflatter, so inflating the whole universe makes the observable patch of spacetime look flatter and flatter.
Inflationary cosmology is relevant to discussions of the origin of life,
because some cosmologists think that it provides a mechanism for generating
many universes other than our own, and also because one prominent
molecular biologist has recently invoked those other universes in an attempt
to explain the origin of life. According to the currently dominant “chaotic
eternal inflationary model,” the rapid expansion of the universe was driven by
an “inflaton field”—a repulsive gravitational field. After an initial phase of
expansion, the inflaton field decayed locally to produce our universe.
However, it also continued to operate at full strength outside the local area toproduce a wider expansion of space into which other universes were birthed
as the inflaton field decayed at other locations. Thus, inflationary
cosmologists postulate the decay of the inflaton field as a mechanism bywhich other “bubble universes” can be created. They also postulate that
inflation can continue indefinitely into the future and, therefore, that the wider
inflaton field will spawn an endless number of other universes as it decays inlocal pockets of the larger and larger expansions of space. Since the inflaton
field continues to expand at a rate vastly greater than the bubble universes
expanding within it, none of these bubble universes will ever interfere witheach other. The one inflaton field therefore gives birth to endless bubble
universes—“many worlds in one,” as Vilenkin colorfully describes it.4
Koonin has appropriated this cosmology in order to explain the origin oflife by chance. Following Vilenkin, he argues that since the inflaton field can
produce an infinite number of other universes, every event that has occurredin our universe was bound to occur somewhere endlessly many times. Thus,
events that appear to be extremely improbable when considering theprobabilistic resources of our universe are actually highly probable—indeed,
inevitable—given the plethora of other universes that do—and will—exist. As
Koonin himself explains: “In an infinite multiverse with a finite number ofdistinct macroscopic histories (each repeated an infinite number of times),
emergence of even highly complex systems by chance is not just possible but
inevitable…. it becomes conceivable that the minimal requirement (thebreakthrough stage) for the onset of biological evolution is a primitive
coupled replication-translation system that emerged by chance. That thisextremely rare event occurred on earth and gave rise to life as we know it isexplained by anthropic selection alone.”5 By “anthropic selection,” Koonin
simply means that our perception that life is incredibly improbable is just an
artifact of our particular vantage point. Since we observe only one bubble
universe, we do not realize that the existence of other universes and themechanism that produced them makes life in a universe such as our own
inevitable.
So has Koonin’s use of inflationary cosmology solved the problem of theorigin of life and the origin of the biological information necessary to it? Has
he proposed a better explanation for the origin of biological information thanintelligent design? There are several reasons to think not.
Do Inflaton Fields Exist?
First, there are good reasons to doubt that inflaton fields even exist. Inflaton
fields were postulated mainly to explain the homogeneity and flatness
problems, but they may not explain these features of the universe well at all,as several prominent physicists have long pointed out. In order to explain thehomogeneity of the universe using inflaton fields, physicists have to makegratuitous assumptions about the singularity from which everything came. As
Oxford physicist Roger Penrose points out, if the singularity were perfectly
generic, expansion from it could yield many different kinds of irregular(inhomogeneous) universes, even if inflation had occurred.6 Thus, inflation

alone, without additional assumptions, does not solve the homogeneity
problem. Getting workable results requires imposing the right metric(distance measure) on space-time.
Furthermore, as physicists Stephen Hawking and Don Page note, it has
proven difficult to explain why inflaton fields and gravitational fields (asdescribed by general relativity, which we have strong reasons to accept)
should work together to produce the homogeneity of the background radiationand the flatness of space-time in our observable universe. Indeed, when thefields are linked, there is no guarantee that inflation will even take place.7
Moreover, these inflaton fields, with their uncanny ability to decay at just theright time (between 10–37 to 10–35 seconds after the big bang) and in just theright measure, have properties associated with no other physical fields.
(Conversely, they have properties that were invented solely for the purpose ofsolving the horizon and flatness problems, which they can’t solve without
additional arbitrary assumptions and specifications of initial conditions.)
Causal Adequacy Considerations
There is another reason that inflationary cosmology does not provide asatisfying explanation or a better explanation than intelligent design for theorigin of biological information. Inflationary cosmology relies for its
explanatory power on the presumed causal powers of an entirely unknown
entity—one posited solely to explain a mysterious class of effects—and one
whose causal powers have not been demonstrated or observed. We do notknow if inflaton fields exist. And we do not know, if they exist, what exactly
they can actually do. Nevertheless, we do know (from direct first-personawareness, if nothing else) that conscious intelligent minds exist and what
they can do.
Further, as philosopher of physics Robin Collins argues, all things being
equal, we should prefer hypotheses “that are natural extrapolations from what
we already know” about the causal powers of various kinds of entities.8 In aslightly different context he argues that multiple-worlds hypotheses fail tomeet this test in their explanations of the anthropic fine-tuning of theuniverse, whereas design hypotheses do not. To illustrate, Collins asks hisreaders to imagine a paleontologist who posits the existence of anelectromagnetic “dinosaur bone–producing field,” as opposed to actual
dinosaurs, as the explanation for the origin of large fossilized bones. Althoughcertainly such a field qualifies as a possible explanation for the origin of thefossil bones, we have no experience of such fields or of their producing
fossilized bones. Yet we have observed animal remains in various phases of
decay and preservation in sediments and sedimentary rock. Thus, mostscientists rightly prefer the actual dinosaur hypothesis over the apparent
dinosaur hypothesis (i.e., the “dinosaur bone–producing field” hypothesis) as
an explanation for the origin of fossils.
In the same way, we have no experience of anything like an inflaton field
generating infinitely many universes (or, for that matter, any experience ofany machine or mechanism capable of producing something as finely tuned as
our universe that is not itself designed). Yet we do have extensive experienceof intelligent agents producing finely tuned machines or information-richsystems of alphabetic or digital code. Thus, Collins concludes, the postulationof mind to explain the fine-tuning of the universe constitutes a natural
extrapolation from our experience-based knowledge of the causal powers ofintelligent agency, whereas postulation of multiple universes (including those
produced by inflaton fields) lacks a similar basis. It follows a fortiori that thedesign hypothesis is a better explanation than inflaton-field decay for theorigin of the information necessary to produce the first life, because it
depends upon the known causal powers of an entity familiar from repeated
and direct experience. Inflationary cosmology depends upon an abstract entity
whose causal powers have not been observed or demonstrated.
Return of the Displacement Problem
There is an additional problem with using inflaton fields to explain the originof the information necessary to produce the first life. In order to explain theorigin of certain features of our observable universe, and (as an unintended
bonus) the origin of presumably innumerable life-friendly universes such as
our own, inflationary cosmology must invoke a number of unexplained
sources or infusions of information. For example, both inflaton fields, and thefields to which they are coupled, have to be finely tuned in order to produce
new bubble universes of the right sort. The “shutoff” of the energy of theinflaton field (which occurs during its decay) alone has to be finely tuned to
between one part in 1053 and one part in 10123 (depending on the model ofinflation invoked) to produce a bubble universe compatible with life.
Additionally, inflationary cosmology makes the already acute fine-tuning
problem associated with the initial low-entropy state of our universe
exponentially worse. According to calculations by Roger Penrose (whoregards inflationary cosmology as a very dubious enterprise), the initial
entropy of our universe is already finely tuned to an accuracy of one part in
10exp (10exp(123)).9 Inflation not only does nothing to explain this finetuning; it actually exacerbates it.
Some cosmologists argue, of course, that these improbabilities can beovercome by the number of bubble universes that the original inflaton fieldproduces. But aside from the inelegance and lack of parsimony of this
explanatory strategy, generating a larger inflaton field that produces the right
results (i.e., a universe with the properties of our observable universe) itself
depends on a number of gerrymandered assumptions and finely tuned initialconditions. As noted above, physicists make a number of gratuitousassumptions about the initial singularity in order to get the inflaton field tomesh with the theory of general relativity. For example, to get inflationary
cosmology to harmonize with general relativity, cosmologists have to assume
a specific way of measuring distance in space-time (a so-called metric) andreject others. In addition, there are a number of possible inflationary
cosmological models, only some of which (when conjoined with generalrelativity) would actually cause universes to inflate. In order to ensure that
inflaton fields will create bubble universes, physicists have to select someinflationary models and exclude others in their theoretical postulations. Eachof these choices constitutes an informative intervention on the part of themodeler—one that reflects unexplained information that would have had tohave been present in the initial conditions associated with the universal
singularity.
Indeed, the need to make such assumptions and restrict theoreticalpostulations implies that the initial singularity itself would have had to havebeen finely tuned in order for any inflaton field to be capable of producing auniverse such as our own. Yet we know that our universe exists. We also havegood reasons for thinking that general relativity is true. Thus, if an inflaton
field exists, it could operate the way that inflationary cosmologists envision
only if the singularity from which the inflaton field emerged was itself finely
tuned (and information-rich).
Thus, by relying on inflationary cosmology to explain the informationnecessary to produce the first life, Koonin has once again created an
information problem in the act of purportedly solving one (see Chapter 13).
Even assuming that inflaton fields exist and that they can create an infinite
number of universes (by no means a safe bet), Koonin solves the problem of
the origin of biological information by creating a new problem ofcosmological information—information that, in his model, is neverthelessentirely necessary to explain the origin of life. Additionally, all inflationarymodels assume that the inflaton field operates within and creates new
universes with the same basic laws and constants of physics that exist within
our universe. Yet the laws and constants of our universe are themselves
extremely fine-tuned to allow for the possibility of life. This fine tuning
represents another source of information that has to be accounted for in order
to explain the origin of life in our cosmos. Yet inflationary theory
presupposes, rather than explains, the existence of this fine tuning.

An Epistemological Cost
Inflationary cosmology has yet another liability: once permitted as a possible
explanation for anything, it destroys practical and scientific reasoning about
everything. Inflationary cosmology can explain the origin of all events, nomatter how improbable, by reference to chance because of the infinite
probabilistic resources it purports to generate. It follows that events we
explain by reference to known causes based upon ordinary experience are justas readily explained in inflationary cosmology as chance occurrences without
any causal antecedent. According to inflationary cosmology, all events
consistent with our laws of nature will eventually arise as the result of random
fluctuations in the quantum vacuum constituted by the inflaton field. Thismeans that an exquisitely designed machine or an intricately crafted piece of
poetry is just as likely to have been produced by chance fluctuations in thequantum vacuum as by a human being. It also means that events such as
earthquakes or regular phenomena such as condensation are just as likely tohave been the result of chance fluctuations in the quantum vacuum as they are
to have been the result of an orderly progression of discernible materialcauses. In short, if inflationary cosmology is true, anything can happen for noreason at all, beyond the supposed random fluctuations in the quantum
vacuum of the inflaton field.
To make matters worse, inflationary cosmology actually implies thatcertain explanations that we regard as extremely improbable are actually more
likely to be true than explanations we ordinarily accept. Consider the“Boltzmann brain” phenomenon, for example, over which quantum
cosmologists have been greatly exercised. Within inflationary cosmology, it istheoretically possible for a fully functioning human brain to pop
spontaneously into existence, due to thermal fluctuations in the quantum
vacuum, and then disappear again. Such an entity has been called a“Boltzmann brain.” Under standard conditions for bubble-universe generation
in inflationary cosmology, Boltzmann brains would be expected to arise asoften, or more often, than normal occurrences in our universe. Indeed,
calculations based upon some inflationary cosmological models lead to asituation in which these free-floating Boltzmann brains infinitely outnumber
normal brains in people like us.10
The epistemological implications of this possibility have raised issues that
cosmologists cannot ignore. If these inflationary cosmological models areaccurate, it becomes infinitely more probable that we ourselves are freefloating Boltzmann brains than real persons with a history living in a universe
13.7 billion years old. In some models, it’s even more probable that a whole
universe like ours could have spontaneously fluctuated into existence than itis that our universe with its extraordinarily improbable initial conditions

would have evolved in an orderly and lawlike way over billions of years. Thismeans that the many-worlds-in-one hypothesis generates an absurdity. It
implies that we are probably not the people we take ourselves to be and that
our memories and perceptions are not reliable, but quite possibly chancefabrications of quantum fields. Neither is our universe itself what it appears tobe according to the hypothesis of eternal inflation. In short, the proposal
Koonin has adopted to solve the origin-of-life problem renders all scientificreasoning and explanation unreliable, thus undermining any basis for his own
explanation of how life came to be. It would be hard to invent a more selfrefuting hypothesis than that!

Notes

Prologue
1. Wilgoren, “Seattle Think Tank Behind ‘Intelligent Design’ Push.”
2. Wilgoren, “Politicized Scholars Put Evolution on the Defensive.”
3. Holden, “Random Samples,” 1709; Giles, “Peer-Reviewed Paper Defends
Theory of Intelligent Design”; Stokes, “Intelligent Design Study Appears”;
Monastersky, “Society Disowns Paper Attacking Darwinism.”
4. Klinghoffer, “The Branding of a Heretic”; Price, “Researcher Claims Bias
by Smithsonian”; “Unintelligent Design Hostility.”
5. “Intelligent Design and the Smithsonian,” New York Times, August 20,
2005, Editorial/Letters, national edition; Powell, “Darwinian Debate
Evolving.”
6. Ostling, “Lifelong Atheist Changes Mind.”
7. Powell, “Editor Explains Reason for ‘Intelligent Design’ Article”;“Intelligent Design and the Smithsonian,” New York Times, August 20,
2005, Editorial/Letters, national edition; Wilgoren, “Politicized Scholars
Put Evolution on the Defensive.”
8. The O’Reilly Factor (Fox News), “Brutally Criticized,” August 25, 2005.
9. Thaxton, Bradley, and Olsen, The Mystery of Life’s Origin.
10. Meyer, “The Origin of Biological Information and the Higher Taxonomic
Categories.” See also Meyer, “The Cambrian Information Explosion”;“DNA and the Origin of Life”; “Evidence of Design in Physics andBiology,” 53–111; “The Scientific Status of Intelligent Design”;“Teleological Evolution”; “DNA by Design”; “The Explanatory Power of
Design”; Meyer, Nelson, and Chien, “The Cambrian Explosion.”
11. Meyer, “The Origin of Biological Information and the Higher Taxonomic
Categories.”
12. Pera, The Discourses of Science.13. Darwin, On the Origin of Species,
481–82.14. Ayala, “Darwin’s Greatest Discovery.”
Chapter 1: DNA, Darwin, and the Appearance of Design
1. Newton, Opticks, 369–70.
2. Crick, What Mad Pursuit, 138.
3. Dawkins, River out of Eden, 17.
4. Gates, The Road Ahead, 188.
5. Küppers, Information and the Origin of Life, 170–72.
6. The Harvard Origins of Life Initiative, http://origins.harvard.edu.7.

Elizabeth Pennisi, “Finally, the Book of Life.”
8. Interview with Williams, in Brockman, ed., The Third Culture, 42–43.
9. Interview with Williams, in Brockman, ed., The Third Culture, 42–43.
10. Klir and Wierman, Uncertainty-Based Information.
11. Gilder, Telecosm.
12. Watson and Crick, “A Structure for Deoxyribose Nucleic Acid.”
13. Williams, Natural Selection, 11.
14. Yockey, “Origin of Life on Earth,” 105.
15. Lewontin, “Adaptation.”
16. Dawkins, The Blind Watchmaker, 1.
17. Mayr, “Darwin: Intellectual Revolutionary.” The effort to explain
biological organisms naturalistically was reinforced by a trend in science toprovide fully naturalistic accounts for other phenomena such as the precise
configuration of the planets in the solar system (Pierre Laplace) and theorigin of geological features (Charles Lyell and James Hutton). It was also
reinforced (and in large part made possible) by an emerging positivistic
tradition in science that increasingly sought to exclude appeals tosupernatural or intelligent causes from science by definition (see Gillespie,“Natural History, Natural Theology, and Social Order”). See also Darwin,
On the Origin of Species, 481–82.
18. Darwin, On the Origin of Species, 188.
19. Darwin, Life and Letters, 1:278–79.
20. Ayala, “Darwin’s Greatest Discovery,” in Ruse and Dembski, eds.,
Debating Design, 58. As the late Harvard evolutionary biologist Ernst
Mayr explained, “The real core of Darwinism…is the theory of natural
selection. This theory is so important for the Darwinian because it permits
the explanation of adaptation, the ‘design’ of the natural theologian, by
natural means, instead of by divine intervention” (Foreword, in Ruse, ed.,Darwinism Defended).
21. Ayala, “Darwin’s Greatest Discovery,” Proceedings of the National
Academy of Sciences, 8573.
22. Bishop, “The Religious Worldview.”
23. Crick, What Mad Pursuit, 138. Emphasis added.
24. Ruse, “Teleology in Biology.”
25. Dawkins, River Out of Eden, 17.
26. Shapiro, review of Darwin’s Black Box, by Michael Behe.
27. Lenoir, The Strategy of Life, ix.
28. Watson, et al., Molecular Biology of the Gene, 1:704.
29. I used the character converter at http://www.csgnetwork.com/asciiset.html
for the binary conversion of the first few words of the Declaration of
independence in ASCII code.

Chapter 2: The Evolution of a Mystery and Why It Matters
1. For a scholarly account of this discovery, along with a more detailed
account of the series of discoveries that caused vitalism to fall from favor,
see Aaron J. Ijde, The Development of Modern Chemistry, 164–65.
2. As quoted in Kauffman and Chooljian, “Friedrich Wöhler (1800–1882), on
the Bicentennial of His Birth,” 126.
3. Partington, A History of Chemistry.
4. Florkin, A History of Biochemistry, 251–52.
5. Hopkins, “BAAS Presidential Address,” 382.
6. Darwin, The Life and Letters, 1:18.
7. For a detailed treatment of this perspective in ancient philosophy, seeSedley, Creationism and Its Critics in Antiquity.
8. Newton, General Scholium, Mathematical Principles of Natural
Philosophy.
9. Kirk and Raven, The Presocratic Philosophers.
10. Hobbes, Leviathan; Hume, Dialogues Concerning Natural Religion.
11. Laplace, (Vietnamese)Exposition du système du monde.
12. Lyell, Principles of Geology.
13. Ernst Haeckel, for instance, in The History of Creation, stated: “We can,therefore, from these general outlines of the inorganic history of the earth’s
crust, deduce the important fact that at a certain definite time life had its
beginning on earth, and that terrestrial organisms did not exist frometernity” (401).
14. Darwin, More Letters of Charles Darwin, 273.
15. Farley, The Spontaneous Generation Controversy, 103ff.; Lechevalier andSolotorovsky, Three Centuries of Microbiology, 35–37.
16. Farley, Spontaneous Generation Controversy, 103–7, 114, 172; Lanham,
Origins of Modern Biology, 268.
17. Nevertheless, the doctrine of spontaneous generation did not die easily.
Even after Pasteur’s work, Henry Bastian continued to find microbialorganisms in various substances that had been sealed and “sterilized” at 100
degrees C or higher. Not until the 1870s, when microbiologists like Cohn,
Koch, and Tyndall perfected methods of killing heat-resistant spores, wereBastian’s observations discredited. Despite an increasingly criticalscientific response to his experimental methods and conclusions, Bastiancontinued to offer observational evidence for spontaneous generation frominorganic matter for another thirty years. Nevertheless, these and otherattempts yielded to a familiar pattern of refutation. Experiments supposedly
establishing the spontaneous occurrence of microorganisms remained
tenable only as long as sterilization methods were inadequate to kill
existing microorganisms or prevent bacterial contamination of experimental

vessels from the surrounding environment. When sources of
microorganisms were identified and methods of destroying them perfected,
observational evidence for spontaneous generation was withdrawn ordiscredited. In the minds of some scientists, especially after the turn of the
century, development seemed to confirm that living matter is too complex
to organize itself spontaneously, whether beginning from organic or
inorganic precursors. Although Huxley and Haeckel accepted Pasteur’s
results, both insisted that his work was not relevant to abiogenesis (lifearising from nonliving matter), as his experiments discredited only theoriesof what Haeckel called “plasmogeny” or what Huxley called“heterogenesis,” i.e., spontaneous generation from once living matter(Haeckel, The Wonders of Life, 115; Kamminga, “Studies in the History of
Ideas,” 55, 60).
18. Glas, Chemistry and Physiology, 118.
19. Coleman, Biology in the Nineteenth Century, 129.
20. Steffens, James Prescott Joule and the Concept of Energy, 139.
21. Steffens, James Prescott Joule and the Concept of Energy, 129–30; Glas,
Chemistry and Physiology, 86.
22. Haeckel, The Wonders of Life, 27.
23. Haeckel, The History of Creation, 332.
24. Virchow, “On the Mechanistic Interpretation of Life,” 114.
25. Virchow, “On the Mechanistic Interpretation of Life,” 115.
26. “Haeckel’s attitude, and that of other contemporary Darwinians, to thequestion of the origin of life was first and foremost an expression of theirworldview. Abiogenesis was a necessary logical postulate within aconsistent evolutionary conception that regarded matter and life as stages ofa single historical continuum” (Fry, The Emergence of Life on Earth, 58).
27. Hull, “Darwin and the Nature of Science,” 63–80. As Hull, a philosopherof biology, explains, Darwin posited “that species are not eternal but
temporary, not immutable but quite changeable, not discrete but graduating
imperceptibly through time one into another.” As Darwin himself said in
On the Origin of Species, “I was much struck how entirely vague andarbitrary is the distinction between species and varieties…. Certainly noclear line of demarcation has yet been drawn between species andsubspecies…or, again, between subspecies and well-marked varieties”(104, 107).
28. For instance, John Tyndall argued, “There does not exist a barrier
possessing the strength of a cobweb to oppose the hypothesis whichascribes the appearance of life to that ‘potency of matter’ which finds
expression in natural evolution” (Fragments of Science, 434). As
Kamminga explains, Tyndall held that “it was inconsistent to believe in
evolution and at the same time reject abiogenesis” (“Studies in the History

of Ideas,” 57).
29. Darwin’s work amplified a theme that evolutionists such as Lamarck andMatthew had articulated in various ways since early in the nineteenth
century. The publication of Origin in 1859 reaffirmed the importance ofenvironmental influences on the development of species. See Mckinney,ed., Lamarck to Darwin, 7, 9, 29, 41.
30. Darwin, “Letter to Hooker”; see also Darwin, Life and Letters, 18.
31. Oparin, Genesis and Evolutionary Development of Life, 7.
32. Haeckel, The Wonders of Life, 135.
33. This strengthened the conviction among many scientists that vital function
was ultimately reducible to a “physical basis,” as Thomas H. Huxleyphrased it in 1868 (“On the Physical Basis of Life”). See also Geison, “TheProtoplasmic Theory of Life”; and Hughes, A History of Cytology, 50.
34. Geison, “The Protoplasmic Theory of Life.”
35. Geison, “The Protoplasmic Theory of Life,” 274.
36. As cited in Geison, “The Protoplasmic Theory of Life,” 274.
37. The descriptions matched those by Felix Dujardin in 1835 and Gabriel
Gustav Valentin in 1836. See Geison, “The Protoplasmic Theory of Life”;
Hughes, A History of Cytology, 40, 112–13.
38. Geison, “The Protoplasmic Theory of Life,” 276. Shultze, in particular,
emphasized the importance of protoplasm based on his realization thatlower marine animals sometimes exist in a “primitive membranelesscondition” and on his identification of protoplasm as the source of vitalcharacteristics like contractility and irritability. See Suñer, Classics ofBiology, 19–20. This conclusion was widely endorsed and amplified
throughout the 1860s by influential thinkers like Ernst Wilhelm vonBrucke, Wilhelm Kühne, Ernst Haeckel, T. H. Huxley, and others.
39. Hughes A History of Cytology, 50; see also Huxley, “On the PhysicalBasis of Life,” 129; Wilson, The Physical Basis of Life, 1–2; Geison, “TheProtoplasmic Theory of Life,” 273, 276–91. Geison comments that during
this period, “The conviction grew that the basic unit of life was essentiallya protoplasmic unit” (278). It was during this period that the term
“protoplasm” gained wide currency.
40. Huxley, “On the Physical Basis of Life,” 138–39.
41. Haeckel, Generelle Morphologie der Organismen, 179–80; The History ofCreation, 411–13; Kamminga, “Studies in the History of Ideas,” 60–61.
42. Haeckel, The History of Creation, 411–13; Kamminga, “Studies in the
History of Ideas,” 62.
43. Haeckel, Generelle Morphologie der Organismen, 183; Kamminga,“Studies in the History of Ideas,” 62.
44. Haeckel, The History of Creation, 421–22; Kamminga, “Studies in the
History of Ideas,” 60–62.

45. Haeckel, The Wonders of Life, 111. Even amid such bold claims, Haeckeland Huxley acknowledged that certain special but unspecified pastconditions must have played an important role in the origin of protoplasm,
since its spontaneous generation from nonliving matter had never been
observed. However, neither Huxley nor Haeckel could demonstrate that the
specific conditions enumerated in their prebiotic scenarios could be inferredfrom classes of evidence other than the existence of life itself. This
deficiency ensured that, as Haeckel himself put it, “Any account of the
primary generation of [life] must be considered premature” (as quoted in
Oparin, The Origin of Life [1938], 105). Neither Huxley nor Haeckel
attempted to identify the relevant past conditions. Haeckel acknowledged,“We have no satisfactory conception of the extremely peculiar state of ourearth’s surface at the time of the first appearance of organisms.” Instead,both men tried to turn this absence of information to their advantage by
intimating that the conditions specified in their scenarios were at least
possible. They also relied on materialistic conceptions of life’s nature tosuggest that life’s origin could be explained by reference to materialistic
causes.
46. Kamminga, “Studies in the History of Ideas,” 69–70, 73–74; Haeckel, The
Wonders of Life, 111–14.
47. Kamminga, “Studies in the History of Ideas,” 70–71; Haeckel, The
Wonders of Life, 112.
48. Kamminga, “Studies in the History of Ideas,” 70–71.
49. Kamminga, “Studies in the History of Ideas,” 185, 188–92. As Robert E.
Kohler argues, the rise of the new field of biochemistry after 1900 marked
the emergence of a “common outlook on the physico-chemical nature oflife” in which it was agreed “that enzymes were the key agents in life
processes” (“The Enzyme Theory and the Origin of Biochemistry,” 194).
50. Oparin, The Origin of Life (1924); Meyer, “Of Clues and Causes.”
51. Kamminga, “Studies in the History of Ideas,” 222.
52. Graham continues: “The Russian biologist with the securest claim tobeing an orthodox Darwinist was K. A. Timiriazev. If any Russian deserved
the title of ‘Darwin’s Bulldog,’ to match that of Huxley in England, it was
Timiriazev. A plant physiologist, Timiriazev combined scientificknowledge with radical politics. Throughout his career he was engaged in arunning battle with established authorities, even though, as a teacher in St.
Petersburg University, and later, a professor at Moscow University, hebecame something of an authority himself, particularly among radical
intellectuals. His political scraps included expulsion as a student from St.
Petersburg University and, many years later, dismissal from the faculty of
Moscow University as a result of his continuing radical sympathies.
Timiriazev was the most popular defender of Darwinism in all of Russia.

His books, A Short Sketch of the Theory of Darwin and Charles Darwin
and His Theory, were published in fifteen editions between 1883 and 1941.
His influence was so great that it could still be felt well into the middle ofthe twentieth century. In an interview in Moscow in 1970 academician A. I.
Oparin, a well-known authority on origin of life, described the lectures on
Darwinism Timiriazev gave at the Polytechnical Museum in Moscow when
Oparin was a boy as the most important influence on his professional
development” (Science in Russia and the Soviet Union, 67).
53. Graham, Science and Philosophy in the Soviet Union, 262–63.
54. In a letter to Ferdinand Lassalle, Marx wrote: “Darwin’s work is mostimportant and suits my purpose in that it provides a basis in natural science
for the historical class struggle. One does, of course, have to put up with theclumsy English style of argument. Despite all shortcomings, it is here that,for the first time, ‘teleology’ in natural science is not only dealt a mortal
blow but its rational meaning is empirically explained”(http://www.marxists.org/archive/marx/works/1861/letters/61_01_16.html).
55. Engels, The Dialectics of Nature.
56. Yockey, Information Theory, Evolution, and the Origin of Life, 151–52.
57. Graham, Science and Philosophy in the Soviet Union, 262–63. Oparin did
not himself participate in the revolution or join the Communist Party.
Moreover, in an interview near the end of his life, he stressed theimportance of Darwinism over Marxism on his initial theorizing. When
asked if he had been a Marxist in 1924, Oparin replied, “Politically, yes I
was. But at that moment what exercised the major influence on my ideas
about the origin of life was the postulates of Darwin’s theory of evolution.”
58. Although as Graham notes, “Oparin has not been one to attempt rigorous
definitions of ‘life.’” His theoretical work in 1924 did reflect a definite
conception of life, which he expressed “in metaphors or in terms of varying
combinations of characteristics necessary for life” (Science and Philosophy
in the Soviet Union, 268).
59. Graham, Science and Philosophy in the Soviet Union, 214, 208.
60. Oparin not only rejected “idealistic” approaches to the origin of life thatassumed an impassable divide between animate and inanimate. He also
rejected the “theory of panspermia” advocated by various nineteenthcentury scientists such as Helmholtz, Thomson, and Richter. Panspermia
specified that life on earth had originated from spores or germs transmitted
here from outer space. These germ species were then supposed either tohave originated elsewhere through some unspecified process or to haveexisted eternally. Many nineteenth-century advocates of the theory opted
for the latter of these two views on the grounds that the complicatedstructure of living cells, though only vaguely discernable with nineteenthcentury microscopes, suggested an “impassable abyss” between animate

and inanimate matter. In rejecting this assumption, Oparin regarded
recourse to panspermia as motivated by an incorrect premise and that it
was, as such, “philosophically useless.” Even if life on earth had issued
from outer space, he noted, the ultimate origin of life would remain
unsolved, especially if one assumed that life had existed eternally. (SeeGraham, Science and Philosophy in the Soviet Union, 203–4, 206, 211.)
61. Graham, Science and Philosophy in the Soviet Union, 203.
62. Graham, Science and Philosophy in the Soviet Union, 199–202.
63. Oparin’s hypothetical reconstruction actually began with an account of theearth’s formation. Oparin accepted a contemporary version of the nebular
hypothesis. He thought, therefore, that the planets formed from hot,incandescent clouds of gas. According to this view, as these nebular clouds
condensed under the mutual gravitational attractions of their gas molecules
they would have formed stars, with the vapors of heavier metals like iron,nickel, and cobalt settling to the center of the cloud. These vapors thencooled and concentrated in a liquid core of heavy metal. (See Graham,
Science and Philosophy in the Soviet Union, 217–18, 221.)
64. Oparin supported this suggestion with spectroscopic studies confirming
the presence of hydrogen in the sun’s atmosphere and studies showing thepresence of hydrocarbons in the atmospheres of dying red stars. He also
cited the work of the Russian chemist D. I. Mendeleev, who earlier had
proposed that methane and other naturally occurring hydrocarbons could
have had a nonbiological origin. Mendeleev believed hydrocarbon
production occurred naturally when metallic carbides combined withoxygen at high temperatures and pressures. Though Oparin did not accept
Mendeleev’s account of the origin of extant terrestrial hydrocarbons, he did
appropriate Mendeleev’s suggested reaction pathway to account for theorigin of high-energy biological precursors on the early earth. (See Oparin,
The Origin of Life [1938], 98, 107–8; Graham, Science and Philosophy in
the Soviet Union, 218–23.)
65. Oparin thought these hydrocarbon compounds would have included
alcohols, ketones, aldehydes, and organic acids (The Origin of Life [1938],
108).
66. Oparin thought these nitrogen-rich compounds would have included
amines, amides, and ammonium salts (The Origin of Life [1938], 108).
67. Oparin, The Origin of Life (1938), 229.
68. Oparin, The Origin of Life (1938), 229–31.
69. Miller, “A Production of Amino Acids.”
70. Shapiro, Origins, 98.
71. William Day, Genesis on Planet Earth, 5.
Chapter 3: The Double Helix

1. As Crick noted: “Only gradually did I realize that this lack of qualification
could be an advantage. By the time most scientists have reached age thirty
they are trapped in their own expertise. They have invested so much effort
in one particular field that it is often extremely difficult, at that time in theircareers, to make a radical change. I, on the other hand, knew nothing,
except for a basic training in somewhat old-fashioned physics andmathematics and an ability to turn my hand to new things” (What Mad
Pursuit, 16).
2. Chamberlain, “The Method of Multiple Working Hypotheses.”
3. Oparin, Genesis and Evolutionary Development of Life, 7.
4. Kamminga, “Protoplasm and the Gene,” 1.
5. Haeckel, The Wonders of Life, 111.
6. Mendel called the discrete inheritance of the two traits for one
characteristic the “law of segregation.” See Jenkins, Genetics, 13–15.
7. Jenkins, Genetics, 238–39.
8. Jenkins, Genetics, 238–39.
9. Morgan, The Physical Basis of Heredity.
10. Olby, The Path to the Double Helix, 73–96, including chap. 6, “Kossel,
Levene and the Tetranucleotide Hypothesis.”
11. Avery, MacCleod, and McCarty, “Induction of Transformation by a
Deoxyribonucleic Acid Fraction.”
12. Griffith, “The Significance of Pneumococcal Types”; Dawson, “TheInterconvertibility of ‘R’ and ‘S’ Forms of Pneumococcus.”
13. Avery, MacLeod, and McCarty, “Induction of Transformation by a
Deoxyribonucleic Acid Fraction”; for a detailed historical account of theirwork, see Judson, The Eighth Day of Creation, 13–23.
14. Chargaff, “Preface to a Grammar of Biology.”
15. Chargaff, Essays on Nucleic Acids, 1–24, “Chemical Specificity of
Nucleic Acids.”
16. According to Chargaff: “The deoxypentose nucleic acids extracted fromdifferent species thus appear to be different substances or mixtures ofclosely related substances of a composition constant for different organs of
the same species and characteristic of the species. The results serve todisprove the tetranucleotide hypothesis” (Essays on Nucleic Acids, 13).
17. Chargaff, Essays on Nucleic Acids, 21.
18. “The number of possible nucleic acids having the same analyticalcomposition is truly enormous. For example, the number of combinations
exhibiting the same molar proportions of individual purines andpyrimidines as the deoxyribonucleic acid of the ox is more than 1056, if thenucleic acid is assumed to consist of only 100 nucleotides; if it consists of
2,500 nucleotides, which probably is much nearer the truth, then thenumber of possible ‘isomers’ is not far from 101500” (Chargaff, Essays on
Nucleic Acids, 21).
19. McElheny, Watson and DNA, 5–29; Watson, The Double Helix, 17.
20. Watson, The Double Helix, 17, 27.
21. Watson, The Double Helix, 9–12, 31.
22. Olby, The Path to the Double Helix, 310.
23. “Politeness, Francis Crick said over the BBC at the time he got the Nobel
Prize, is the poison of all good collaboration in science. The soul ofcollaboration is candor, rudeness if need be. Its prerequisite, Crick said, isparity of standing in science, for if one figure is too much senior to theother, that’s when the serpent politeness creeps in. A good scientist values
criticism almost higher than friendship: no, in science criticism is the heightand measure of friendship. The collaborator points out the obvious, with
due impatience. He stops the nonsense, Crick said—speaking of James
Watson” (Judson, The Eighth Day of Creation, 125).
24. “When Crick and Watson began, they knew very little about DNA for
sure, and part of what they were most sure of was wrong. To consider DNA
as a physical object, they wanted diameters, lengths, linkages and rotations,screw pitch, density, water content, bonds, and bonds and again bonds. The
sport would be to see how little data they could make do with and still get it
right: the less scaffolding visible, the more elegant and astonishing thestructure…. ‘The point is [said Crick] that evidence can be unreliable, andtherefore you should use as little of it as you can…. There isn’t such a thing
as a hard fact when you’re trying to discover something. It’s only
afterwards that the facts become hard’” (Judson, The Eighth Day ofCreation, 92–93).
25. Watson, The Double Helix, 57–59.
26. Watson, The Double Helix, 57–59.
27. Watson, The Double Helix, 81.
28. According to Judson: “On Friday of that week, January 30 [1953], Watson
went to London to see Hayes. He took the copy of Pauling’s manuscriptwith him, and about teatime that afternoon stopped at King’s College” (The
Eighth Day of Creation [1996], 135).
29. Watson, The Double Helix, 96.
30. “Afterwards, in the cold, almost unheated train compartment, I sketchedon the blank edge of my newspaper what I remembered of the B pattern”(Watson, The Double Helix, 99).
31. Chargaff, Essays on Nucleic Acids, 1–24.
32. As Watson recalled in his memoirs, “The high point in Chargaff’s scorncame when he led Francis into admitting that he did not remember thechemical differences among the four bases…. Francis’s subsequent retortthat he could always look them up got nowhere in persuading Chargaff that

we knew where we going or how to get there” (Watson, The Double Helix,
78).
33. Chargaff, Heraclitean Fire, 101.
34. Judson, The Eighth Day of Creation, 120.
35. Judson, The Eighth Day of Creation, 135–38.
36. Judson, The Eighth Day of Creation, 141.
37. Olby, The Path to the Double Helix, 398.
38. Crick, quoted in Olby, The Path to the Double Helix, 400.
39. Olby, The Path to the Double Helix, 400.
40. “It must have been during the second week of February [1953] that Perutz
passed on to Crick his copy of the report which Sir John Randall had
circulated to all the members of the Biophysics Research Committee [of theMedical Research Council] who came to see round his Unit on December
15, 1952” (Olby, The Path to the Double Helix, 402). This report contained
a summary of Rosalind Franklin’s crystallographic findings about thestructure of DNA.
41. Watson, The Double Helix, 114.
42. Watson, The Double Helix, 110.
43. “I went back to my desk hoping that some gimmick might emerge tosalvage the like-with-like idea. But it was obvious that the new assignments
were its death blow. Shifting the hydrogen atoms to their keto locations
made the size differences between the purines and pyrimidines even more
important than would be the case if the enol forms existed. Only by themost special pleading could I imagine the polynucleotide backbone bending
enough to accommodate irregular base sequences” (Watson, The Double
Helix, 112).
44. Watson, The Double Helix, 114.
45. Watson, The Double Helix, 114.
46. “A few minutes later he [Crick] spotted the fact that two glycosidic bonds(joining base and sugar) of each base pair were systematically related by adiad axis perpendicular to the helical axis. Thus, both pairs could be flipflopped over and still have their glycosidic bonds facing in the same
direction. This had the important consequence that a given chain could
contain both purines and pyrimidines. At the same time, it strongly
suggested that the backbones of the two chains must run in opposite
directions” (Watson, The Double Helix, 115).
47. Crick, What Mad Pursuit, 74. This aesthetic criterion—the beauty and,hence, rightness of the double helix—rings through many accounts of theencounters with the molecule. Watson himself, at the end of an early 1950s
inebriated after-dinner lecture about DNA, was (in Crick’s judgment) “at aloss for words. He gazed at the model, slightly bleary-eyed. All he could
manage to say was ‘It’s so beautiful, you see, so beautiful!’ But then, ofcourse, it was” (Crick, What Mad Pursuit, 79). François Jacob, on firsthearing from Watson about the structure of DNA, reflected that “thisstructure was of such simplicity, such perfection, such harmony, such
beauty even, and biological advantages flowed from it with such rigor andclarity, that one could not believe it to be untrue” (The Statue Within, 270–
71).
48. Watson, The Double Helix, 115.
49. Watson and Crick, “Genetical Implications,” 965.
Chapter 4: Signature in the Cell
1. Watson and Crick, “A Structure for Deoxyribose Nucleic Acid”; “Genetical
Implications,” esp. 964; Schneider, “Information Content of Individual
Genetic Sequences”; Loewenstein, The Touchstone of Life. Hood andGalas, “The Digital Code of DNA.”
2. Lewbel, “A Personal Tribute to Claude Shannon.”
3. Gardner, The Mind’s New Science, 11.
4. Horgan, “Unicyclist, Juggler and Father of Information Theory.”
5. Shannon, “A Mathematical Theory of Communication.” Information
theorists found it convenient to measure information additively rather thanmultiplicatively. Thus, the common mathematical expression (I =–log2p)
for calculating information converts probability values into informational
measures through a negative logarithmic function, where the negative sign
expresses an inverse relationship between information and probability.
6. Shannon and Weaver, The Mathematical Theory of Communication, 8.
7. Astbury and Street, “X-Ray Studies of the Structure of Hair, Wool and
Related Fibres”; Judson, The Eighth Day of Creation, 61–62; Olby, The
Path to the Double Helix, 63.
8. Bergmann and Niemann, “Newer Aspects of Protein Chemistry.”
9. According to Judson: “The man who released the present-day
understanding of molecular specificity in living processes was Frederick
Sanger. His determination, beginning in the mid-forties, of the amino-acidsequences of bovine insulin proved that they have no general periodicities.
His methods and this surprising result had many consequences, of course:
the most general and profound was that proteins are entirely and uniquely
specified” (The Eighth Day of Creation, 88–89, 585). See Sanger andThompson, “The Amino Acid Sequence in the Glycyl Chain of Insulin.”
10. Judson, The Eighth Day of Creation, 581–85.
11. Olby, The Path to the Double Helix, 265.
12. Rose, “No Assembly Required,” 36, 28.
13. Judson, The Eighth Day of Creation, 538.
14. Judson, The Eighth Day of Creation, 539.

15. Kendrew, et al., “A Three-Dimensional Model of the Myoglobin
Molecule,” 662–66.
16. Lodish, et al., Molecular Cell Biology, 321–23.
17. Gitt, In the Beginning Was Information, 192–93.
18. Lodish, et al., Molecular Cell Biology, 322.
19. Alberts, et al., Molecular Biology of the Cell, 111–12, 127–31.
20. As Monod expressed the point in an interview with Judson: “The first
determination of the exact amino-acid sequence of a protein by Sanger”—
the sequencing of bovine insulin between 1949 and 1955—“was absolutely
essential. One could not even have begun to think seriously about thegenetic code until it had been revealed, to begin with, that a protein isbeyond the shadow of doubt a polypeptide in which the amino acid residues
really are arranged in a definite, constant, genetically determined sequence—and yet a sequence with no rule by which it determined itself. Therefore
it had to have a code—that is, complete instructions expressed in somemanner—to tell it how to exist, you see” (Judson, The Eighth Day ofCreation, 188).
21. Watson and Crick, “Genetical Implications.”
22. Judson, The Eighth Day of Creation, 332–33.
23. Judson, The Eighth Day of Creation, 332–35.
24. Beadle and Tatum, “Genetic Control of Biochemical Reactions in
Neurospora.” Though Beadle and Tatum’s results helped to establish thatgenes encode proteins, we now know that in most cases one gene encodes
multiple RNA and protein products. See Gerstein, et al. “What Is a Gene,
Post-ENCODE?”; Carninci, Yasuda, and Hayashizaki, “Multifaceted
Mammalian Transcriptome”; Kapranov, Willingham, and Gingeras,“Genome-wide Transcription”; Pesole, “What Is a Gene?”
25. De Chadarevian, “Protein Sequencing and the Making of Molecular
Genetics.”
26. Judson, The Eighth Day of Creation, 453–70; Matthei and Nirenberg,“Characteristics and Stabilization of DNAase-Sensitive Protein Synthesis in
E. coli Extracts”; “The Dependence of Cell-Free Protein Synthesis”;
Zamecnik, “From Protein Synthesis to Genetic Insertion”; Brenner, Jacob,and Meselson, “An Unstable Intermediate”; Portugal and Cohen, A Century
of DNA, 272, 298–302; Hoagland, et al., “A Soluble Ribonucleic Acid
Intermediate in Protein Synthesis.”
27. Alberts, et al., Molecular Biology of the Cell, 106–8; Wolfe, Molecular
and Cellular Biology, 48.
28. Alberts, et al., Molecular Biology of the Cell, 108.
29. Yockey, Information Theory and Molecular Biology, 110.
30. Küppers, “On the Prior Probability of the Existence of Life,” 355–69.
31. See Schneider, “Information Content of Individual Genetic Sequences.”

See also Yockey, Information Theory and Molecular Biology, 246–58, forimportant refinements in the method of calculating the informationcarrying capacity of proteins and DNA.
32. Crick, “On Protein Synthesis”; Sarkar, “Decoding ‘Coding,’” esp. 857;
“Biological Information.”
33. Orgel, The Origins of Life, 189.
34. Dawkins, River Out of Eden, 17.
35. Gates, The Road Ahead, 188.
36. Yockey, “Origin of Life on Earth,” esp. 105.
Chapter 5: The Molecular Labyrinth
1. Judson, The Eighth Day of Creation, 264.
2. Gamow, “Possible Relation Between Deoxyribonucleic Acid and Protein
Structures”; “Possible Mathematical Relation Between Deoxyribonucleic
Acid and Proteins.”
3. Gamow, “Possible Relation Between Deoxyribonucleic Acid and Protein
Structures.” As Judson explains, Gamow thought that the “permutations of
the bases formed holes of different shapes…into which various amino acids
fit as specifically as keys into locks” (The Eighth Day of Creation, 256–57).
4. Crick, “On Degenerate Templates.”
5. Judson, The Eighth Day of Creation, 291.
6. Judson, The Eighth Day of Creation, 258.
7. Judson, The Eighth Day of Creation, 291.
8. De Chadarevian, “Protein Sequencing and the Making of Molecular
Genetics.”
9. Judson, The Eighth Day of Creation, 585.
10. http://ascii-table.com/(last accessed July 5, 2008).
11. Crick, “On Degenerate Templates,” 8–9.
12. Crick, “On Degenerate Templates,” 8–9.
13. Judson, The Eighth Day of Creation, 293.
14. Judson, The Eighth Day of Creation, 292.
15. Zamecnik, “From Protein Synthesis to Genetic Insertion.”
16. Brenner, Jacob, and Meselson, “An Unstable Intermediate.”
17. Lengyel, Speyer, and Ochoa, “Synthetic Polynucleotides and the Amino
Acid Code”; Nirenberg and Matthaei, “The Dependence of Cell-Free
Protein Synthesis.”
18. Portugal and Cohen, A Century of DNA, 272, 298–302.
19. Hoagland, et al., “A Soluble Ribonucleic Acid Intermediate in Protein
Synthesis.”
20. Jones, CAD/CAM.
21. Beeby, “The Future of Integrated CAD/CAM Systems.”
22. Watson, et al., Molecular Biology of the Gene, vol. 1:360–81.

23. Fraser, et al., “The Minimal Gene Complement.”
24. Watson, et al., Molecular Biology of the Gene, vol. 1:368–80.
25. Wolfe, Molecular and Cellular Biology, 580–81, 639–48.
26. Wolfe, Molecular and Cellular Biology, 639, 731.
27. Lodish, et al., Molecular Cell Biology, 342–45; Wolfe, Molecular andCellular Biology, 727–30.
28. Cramer, et al., “Structure of Eukaryotic RNA Polymerases.”
29. Wolfe, Molecular and Cellular Biology, 580–81.
30. Introns are now known to perform many functions in the cell, including(a) encoding transcriptional (and mutational) control elements, (b) encoding
nontranslated RNAs and their transcriptional regulatory sequences, and (c)encoding RNA splicing control elements. See Sowpati, et al., “An Intronic
DNA Sequence within the Mouse Neuronatin Gene Exhibits Biochemical
Characteristics of an ICR and Acts as a Transcriptional Activator Gene in
Drosophila,” 963–73; Ozsolak, et al. “Chromatin Structure Analyses
Identify miRNA Promoters,” 3172–83. Wang, et al. “Splicing Regulation:
From a Parts List of Regulatory Elements to an Integrated Splicing Code,”
802–13.
31. Lodish, et al., Molecular Cell Biology, 437–38.
32. Fraser, et al., “Minimal Gene Complement,” 399.
33. Watson, et al., Molecular Biology of the Gene, vol. 1:393–430.
34. Watson, et al., Molecular Biology of the Gene, vol. 1:443.
35. Lodish, et al., Molecular Cell Biology, 116–34.
36. Lodish, et al., Molecular Cell Biology, 120–25.
37. Lodish, et al., Molecular Cell Biology, 120–25.
38. Fraser, et al., “Minimal Gene Complement,” 399.
39. Fraser, et al., “Minimal Gene Complement,” 618–22.
40. Lewontin, “The Dream of the Human Genome.”
41. Goodsell, The Machinery of Life, 45.
42. Monod, Chance and Necessity, 143.
43. Bult, et al., “Complete Genome Sequence”; Glass, et al., “Essential Genesof a Minimal Bacterium.”
44. Lewontin, “The Dream of the Human Genome,” esp. 33.
45. Goodsell, The Machinery of Life, 45.
46. Popper, “Scientific Reduction.”
47. Monod, Chance and Necessity, 143.
Chapter 6: The Origin of Science and the Possibility of Design
1. Dyson, Origins of Life, 18.
2. Biever, “The God Lab.”
3. I’m not, of course, invoking the example of such genius to claim a similar
stature, but rather to highlight the legitimacy of a particular form of

scientific inquiry—one that I was inspired to pursue by my admiration forthese examples of boldness and willingness to think “outside the box” andperceive possibilities that had not been considered before, even by others
who had access to all the same facts.
4. Whitehead, Science and the Modern World, 2–4, 13.
5. Fuller, Science vs. Religion? 15. See also: Crombie, Augustine to Galileo;
Jaki, “Science: Western or What?”; Butterfield, The Origins of Modern
Science; Hooykaas, Religion and the Rise of Modern Science.
6. As cited in Holton, Thematic Origins of Scientific Thought, 86.
7. “God is a most free agent, and created the world not out of necessity but
voluntarily, having framed it as he pleased and thought fit at the beginningof things, when there was no substance but himself and consequently nocreature to which he could be obliged, or by which he could be limited”(Boyle, A Free Enquiry, 160).
8. Hodgson, “The Christian Origin of Science,” 145.
9. Hodgson, “The Christian Origin of Science,” 142.
10. Watson, The Double Helix, 133–34.
11. Agassiz, Essay on Classification; see also Rieppel, “Louis Agassiz (1807–1873) and the Reality of Natural Groups,” 34.
12. “Rational inquiry must inevitably, in Linné’s [Linnaeus] opinion, lead, notto skepticism or disbelief, but to the acknowledgment of and respect for anomniscient and omnipotent Creator” (Larson, Reason and Experience,151).
13. The full passage reads: “For the characters and impressions of wisdomthat are conspicuous in the curious fabric and orderly train of things canwith no probability be referred to blind chance, but must be ascribed to amost intelligent and designing agent” (Boyle, A Free Enquiry, 101). See
also Lennox, “Robert Boyle’s Defense of Teleological Inference.”
14. Newton, General Scholium, Mathematical Principles of Natural
Philosophy (1969).
15. Newton, General Scholium, Mathematical Principles of Natural
Philosophy (1969).
16. Scott, Testimony Before the U.S. Commission on Civil Rights.
17. Meyer, Testimony Before the U.S. Commission on Civil Rights.
18. I also remembered in my reading of the great chemist Robert Boyle thathe thought that scientists and philosophers ask different kinds of questions
about natural objects. Boyle noted that scientists usually ask whatsomething is made of and how it works or operates, whereas philosophersor theologians ask what the purpose of something is. But Boyle noted that
there was a third kind of question that scientists ask that stands at theboundary between science and philosophy. That question is, “How did thisoriginate?” or “Where did this come from?” So I thought maybe there is a

special class of scientific questions that required a special method ofinvestigation.
Chapter 7: Of Clues to Causes
1. Gould, “Evolution and the Triumph of Homology.” Following thenineteenth-century philosopher of science William Whewell, Gould
describes the process of testing in the historical sciences as seeking“consilience.” “Consilience” occurs when many facts can be explained wellby a single proposition or theory. Gould also notes that despite thedifferences between historical and experimental sciences, both share“nomothetic undertones,” meaning, in the case of the historical sciences,that they depend upon knowledge of the laws (Greek nomos) of nature to
make inferences about the past (see esp. 64–65). Cleland, “Methodologicaland Epistemic Differences”; “Historical Science, Experimental Science,and the Scientific Method.”
2. Meyer, “Of Clues and Causes.”
3. Morris, The Crucible of Creation, 63–115.
4. Whewell, The Philosophy of the Inductive Sciences; History of theInductive Sciences.
5. Whewell, The Philosophy of the Inductive Sciences, 1st ed., 95–123; 2nded., 637–65; Meyer, “Of Clues and Causes,” 12–23.
6. Whewell, The Philosophy of the Inductive Sciences, 1st ed., 95–96; 2nd ed.,637–38; History of the Inductive Sciences, 3:397; Meyer, “Of Clues andCauses,” 12–13.
7. Although most inductive sciences sought to establish general laws via
induction, palaetiology sought to use knowledge of such generalizations inorder to establish past causal conditions or events that could, in turn, be
used to explain present events or “manifest effects.” 8. Whewell, The
Philosophy of the Inductive Sciences, 1st ed., 112–13, 121–22; 2nd ed.,654–55, 663–64; Meyer, “Of Clues and Causes,” 18–22. For acontemporary study of how singular or past events can explain otherevents, see Alston, “The Place of the Explanation of Particular Facts inScience”; Martin, “Singular Causal Explanation”; Gallie, “Explanations in
History and the Genetic Sciences.”
9. Hodge, “The History of the Earth, Life, and Man,” esp. 262.
10. Whewell, The Philosophy of the Inductive Sciences, 1st ed., 95, 101–4,
121; 2nd ed., 637, 643–46, 663. See also Meyer, “Of Clues and Causes,”
14–18.
11. Whewell, The Philosophy of the Inductive Sciences, 1st ed., 113, 121; 2nded., 665, 663.
12. Gould, “Evolution and the Triumph of Homology,” 62.
13. Morris, The Crucible of Creation, 63–115; Gould, Wonderful Life.

14. Meyer, “Of Clues and Causes,” 36–76; Cleland, “Historical Science,
Experimental Science, and the Scientific Method”; Gould, “Evolution andthe Triumph of Homology.”
15. Peirce, Collected Papers, 2: 372–88. Later Peirce adapted his mode ofclassifying forms of reasoning so it would not contradict his earlier
classification scheme, but in so doing he made its implications for the
methodology of historical science less apparent. Only his earlyclassification scheme is appropriated in this discussion. See also Fann,
Peirce’s Theory of Abduction, 28–34.
16. Peirce, Collected Papers, 2:375; “Abduction and Induction.”
17. Peirce, Collected Papers, 2:375, emphasis added.
18. Chamberlain, “The Method of Multiple Working Hypotheses.”
Chamberlain, though a contemporary of Peirce, does not address the limits
of abductive reasoning by name. Nevertheless, he advances the method ofmultiple competing hypotheses to address the problem just the same. In his
seminal article, he recommends that geologists (and presumably otherhistorical scientists) use the method to protect against prematurely
establishing a merely possible explanation as a single “ruling theory.”
Moreover, his concern arises specifically in the context of geological andhistorical sciences in which many possible causes may conceivably explain
the origin of present features of the earth. Chamberlain uses as an example
a geologist who faces this problem as he tries to explain the origin of theGreat Lakes basin.
19. Lipton, Inference to the Best Explanation, 1.
20. Lipton, Inference to the Best Explanation, 1.
21. Lipton, Inference to the Best Explanation, 56–74.
22. Lipton, Inference to the Best Explanation, 133–57. A concrete example
supportive of Lipton’s claims can be found in a fascinating case study by
University of Maryland historian of science Stephen Brush, who showed
that, in the case of Einstein’s theory of general relativity, the ability to
explain many already known facts played a far greater role in generalrelativity’s acceptance than did the few successful predictions it made. See
Brush, “Prediction and Theory Evaluation.”
23. Prior to Lipton’s work, the most common conception of scientificreasoning had been advanced by Sir Karl Popper, a British-Hungarianphilosopher of science. Popper taught that for a theory to be scientific it
must be testable against the evidence. Furthermore, he insisted that to test ascientific theory, scientists must make predictions based upon the theory
and then observe whether the predictions came true. Typically, scientists do
this by establishing a set of highly controlled conditions in the laboratory
and then conducting an experiment to see if the outcome conforms to their
expectations based on the theory in question. If the predictions come true,the theory passes the experimental test and can be regarded as “confirmed,”
but not as “verified.” If the prediction fails to materialize, however, theexperiment is said to have falsified the theory—unless, of course, it can be
determined that some other factor, such as faulty experimental apparatus, is
responsible for the failed test. Another philosopher, Carl Hempel, had asimilar idea. He argued that to explain an event scientifically, scientists
need to be able to deduce an event from a set of initial conditions andscientific laws. Lipton’s work showed that Popper’s and Hempel’s
conceptions of scientific testing and explanation were far too limited(Inference to the Best Explanation, 29–31, 75–98). He showed that
explanatory power played a key role in testing in many branches of science.
I realized that Lipton’s work provided an excellent description of how
historical scientists formulate and test their theories—something that
Popper’s account of scientific testing could not do. Historical scientists
usually do not make predictions or deduce outcomes in advance. Instead,historical scientific theories typically explain events after the fact and canbe tested by comparing their explanatory power against that of theircompetitors.
24. Lipton summarized the problem this way: “One reason ‘Inference to theBest Explanation’ has been so little developed, in spite of its popularity, isclear. The model is an attempt to account for inference in terms of
explanation, but our understanding of explanation is so patchy that themodel seems to account for the obscure in terms of the equally obscure”(Inference to the Best Explanation, 2).
25. Lipton, Inference to the Best Explanation, 32–55, 75–98.
26. Lipton, Inference to the Best Explanation, 32–98. A particularly helpful
example can be found in Lipton’s extended discussion of Ignaz
Semmelweis’s well-known research on childbed fever, 75–98. See also
Scriven, “Causes, Connections and Conditions in History,” esp. 250.
27. For example, see Lyell’s discussions in Principles of Geology, 1:75–91;
3:1–7.
28. William Whewell, The Philosophy of the Inductive Sciences, 2: 101.
29. For a discussion of the similarity between Lyell’s principle ofuniformitarianism and the vera causa principle adopted by Darwin, see
Kavalovski, “The Vera Causa Principle,” 78–103.
30. Darwin, On the Origin of Species, 80–130.
31. Darwin also argued that the process of descent with modification was avera causa of certain kinds of patterns found among living organisms. He
noted that diverse organisms share many common features. He called these“homologous” and noted that we know from experience that descendants,
although they differ from their ancestors, also resemble them in many
ways, usually more closely than others who are more distantly related. Sohe proposed descent with modification as a vera causa (a known cause) for
homologous structures. That is, he argued that our uniform experience
shows that the process of descent with modification from a common
ancestor is “causally adequate” or capable of producing homologous
features (On the Origin of Species, 131–70; esp. 159, where Darwin refers
to the “community of descent” as a “vera causa” of homologies among
plant species). See also Meyer, “Of Clues and Causes,” 112–36;
Kavalovski, “The Vera Causa Principle,” 104–29; Hodge, “Darwin’s
Theory and Darwin’s Argument”; Recker, “Causal Efficacy”; Kitcher,“Darwin’s Achievement”; Hull, The Philosophy of Biological Science, 50–
51.
32. Scriven, “Explanation and Prediction in Evolutionary Theory,” 480–81;“Causes, Connections and Conditions in History,” esp. 250.
33. Scriven, “Causes, Connections and Conditions in History,” 250;
“Explanation and Prediction in Evolutionary Theory,” 481. As Hodge
explains: “One should invoke only causes whose…competence to produce
such an effect can be known independently of their putative responsibility
for that phenomenon” (“The Structure and Strategy of Darwin’s ‘Long
Argument,’” 239).
34. Scriven, “Explanation and Prediction in Evolutionary Theory,” 481. See
also Hodge, “The Structure and Strategy of Darwin’s ‘Long Argument,’”
239; Meyer, “Of Clues and Causes,” 79–111; see esp. 108–10 for a
discussion comparing Scriven’s “retrospective causal analysis” and the vera
causa principle.
35. See the discussion of Sober below. See also Scriven, “Explanation andPrediction in Evolutionary Theory,” 480; “The Temporal Asymmetry of
Explanations and Predictions.”
36. Meyer, “Of Clues and Causes,” 96–108.
37. Sober, Reconstructing the Past, 1–5.
38. Sober, Reconstructing the Past, 3–4. I have modified Sober’s example inorder to make its point clearer.
39. See also: Michael Scriven, “Causes, Connections and Conditions in
History,” Philosophical Analysis and History, 250.
40. Scriven, “Explanation and Prediction in Evolutionary Theory,” 480;
Sober, Reconstructing the Past, 1–5; Alston, “The Place of the Explanationof Particular Facts in Science,” 23; Gallie, “Explanations in History and the
Genetic Sciences,” 392.
41. Gallie, “Explanations in History and the Genetic Sciences,” 392.
42. Meyer, “Of Clues and Causes,” 96–108. See also Whewell, The
Philosophy of the Inductive Sciences, 2:240; Thagard, “The Best
Explanation,” esp.
85. For background on Whewell’s ideas about consilience, see Butts, William

Whewell’s Theory of Scientific Method; Laudan, “William Whewell on theConsilience of Inductions.”
43. Meyer, et al., Explore Evolution, 4–5.
44. Scriven, “Causes, Connections and Conditions in History,” 250.
45. Scriven, “Explanation and Prediction in Evolutionary Theory,” 481.
46. Scriven, “Causes, Connections and Conditions in History,” 251.
47. Scriven, “Explanation and Prediction in Evolutionary Theory,” 481.
48. Meyer, “Of Clues and Causes,” 77–111.
49. Scriven, “Causes, Connections and Conditions in History,” 249–50.
50. Scriven, “Explanation and Prediction in Evolutionary Theory,” 481.
51. See the extended discussion of this claim in Meyer, “Of Clues andCauses,” 112–36. For other relevant studies, see Kavalovski, “The Vera
Causa Principle,” 104–29; Hodge, “Darwin’s Theory and Darwin’s
Argument,” 167–74; Recker, “Causal Efficacy,” 147–75; Kitcher,“Darwin’s Achievement,” 127–89; Ruse, The Darwinian Revolution, 160–
201; Hull, The Philosophy of Biological Science, 50–51.
52. Darwin, Life and Letters, 1:437, emphasis added.
53. Quastler, The Emergence of Biological Organization, 16.
54. Küppers, Information and the Origin of Life, 170–72.
Chapter 8: Chance Elimination and Pattern Recognition
1. Monod, Chance and Necessity, 97–98.
2. See Hogg, Craig, and McKean, Introduction to Mathematical Statistics,
esp. the sections on statistical testing.
3. For a quick synopsis of Fisher’s views on hypothesis testing, see Fisher,
The Design of Experiments, 13–17. For a fuller treatment, see Fisher,
Statistical Methods and Statistical Inference. For Neyman and Pearson’s
extension of Fisher’s ideas, which sets an explicit alternate hypothesis over
and against a null hypothesis, see Ian Hacking, The Logic of Statistical
Inference, 92–93. Hacking’s text also has an excellent historical discussionof rejection regions (“rejection classes”) going back to the eighteenthcentury work of John Arbuthnot and Nicholas Bernoulli and relating it to
the work of Fisher (78–83). For a Bayesian critique of Fisher’s ideas about
statistical significance and hypothesis testing, see Howson and Urbach,
Scientific Reasoning, chap.
8. For Dembski’s response to Bayesian criticisms of Fisher’s theory and
Dembski’s approach more generally, see Dembski, The Design Revolution,
chap. 33, “Design by Elimination Versus Design by Comparison.”
4. The waiting time is computed in terms of a Poisson distribution. See
Grimmett and Stirzaker, Probability and Random Processes, chap. 3.
5. Fisher, The Design of Experiments, 13–17.
6. Often scientists and statisticians have a wealth of data from their disciplines

about how frequently certain events occur, data that helps them define theserejection regions. Other times they may define them based on their analysisof the relevant outcome-producing process, such as a roulette wheel,radioactive decay, or genetic recombination or mutation. Fisher argued thatwhen events occur that fall within well-defined and prespecified rejection
regions, the chance hypothesis could safely be eliminated fromconsideration. Given the chance hypothesis, it would be more likely than
not that events would fall outside such a rejection region. In such cases,
Fisher therefore concluded that something other than chance was operating—such as, in our previous illustration, Slick cheating at roulette or somephysical defect in the roulette wheel (along with an apparently dim-witted
croupier).
7. In this case, a gaming commissioner will be on the lookout for an
improbable ratio of victories for the casino, such as a roulette wheel thattypically has bets on about half its numbers, but consistently turns up
winners only one-fourth of the time.
8. William A. Dembski, “The Logical Underpinnings of Intelligent Design,”
in William A. Dembski and Michael Ruse, eds., Debating Design: From
Darwin to DNA, 314.
9. De Duve, Singularities, 233.
10. For the underlying mathematics, see Dembski, The Design Inference,
175–77. Dembski recycles this example for a more popular audience in his
book No Free Lunch, 19.
11. Dembski puts the criminal’s situation in perspective: “The probability ofgetting 100 heads in a row on a given trial is so small that the prisoner has
no practical hope of getting out of prison, even if his life expectancy andcoin-tossing ability were dramatically increased. If he could, for instance,
make 10 billion attempts each year to obtain 100 heads in a row (this iscoin-flipping at a rate of over 500 coin-flips per second every hour of every
day for a full year), then he stands an even chance of getting out of prisonin 1020 years (i.e., a hundred billion billion years). His probabilistic
resources are so inadequate for obtaining the desired 100 heads that it ispointless for him to entertain hopes of freedom” (No Free Lunch, 19).
Chapter 9: Ends and Odds
1. Lehninger, Biochemistry, 782.
2. Wald, “The Origin of Life,” 48; Shapiro, Origins, 121.
3. Crick, “The Origin of the Genetic Code,” 369–70; Kamminga, “Studies inthe History of Ideas,” 303–4.
4. Cairns-Smith, The Life Puzzle, 95.
5. De Duve, “The Beginnings of Life on Earth,” 437.

6. According to the advocates of chance, we are now observing in the DNA
molecule or in proteins the outcome of a fortuitous sequence of random
events. So, according to this hypothesis, if an observer had been on theearly earth to watch this process, he or she would have witnessed an event
very similar to a gambler turning up just the right card time after time after
time. An astute observer would have witnessed a pattern, a pattern of
events that produced a functionally significant outcome. De Duve’s point isthat just as we should be suspicious of chance in the case of the uncannily
successful gambler, so should we be suspicious of chance in the game oflife—and so should we be suspicious of chance as the explanation for theorigin of life and the information it requires.
7. De Duve, “The Beginnings of Life on Earth,” 437; Quastler, The
Emergence of Biological Organization, 7; Morowitz, Energy Flow in
Biology, 5–12; Hoyle and Wickramasinghe, Evolution from Space, 24–27;
G. Cairns-Smith, The Life Puzzle, 91–96; Prigogine, Nicolis, andBabloyantz, “Thermodynamics of Evolution,” 23–31; Yockey, Information
Theory and Molecular Biology, 246–58; “Self-Organization Origin of Life
Scenarios and Information Theory”; Shapiro, Origins, 117–31; Behe,“Experimental Support for Regarding Functional Classes of Proteins”;
Crick, Life Itself, 88.
8. Bult, et al., “Complete Genome Sequence.”
9. In prokaryotic cells, the information content of the genes and their protein
products are nearly equivalent. In eukaryotic cells, posttranscriptional
processing of the amino-acid chain requires information beyond theinformation that encodes amino-acid sequences. Nevertheless, sinceprokaryotes arose on the earth first and have a simpler structure, they arethought to more closely resemble the first living cells. Thus, those
calculating the probability of the first minimally complex cell arising by
chance correctly assume that the genes and the proteins they encode had
closely comparable information content—just as they do in modern
prokaryotes.
10. The redundancy in the genetic code makes it easier as a practical matter tocalculate these odds using proteins.
11. Moorhead and Kaplan, Mathematical Challenges to the Neo-DarwinianInterpretation of Evolution.
12. Reidhaar-Olson and Sauer, “Functionally Acceptable Substitutions”;
Bowie and Sauer, “Identifying the Determinants of Folding and Activity.”
13. Oddly, Sauer’s descriptions of his team’s results often downplay the rarityof functional sequences within sequence space. Instead, he oftenemphasizes the tolerance for different amino acids that is allowable at eachsite. For example, the abstract of the paper reporting the figure of 1 in 1063
makes no mention of that figure or its potential significance, stating insteadthat their results “reveal the high level of degeneracy in the information thatspecifies a particular protein fold” (Reidhaar-Olson and Sauer,“Functionally Acceptable Substitutions”).
14. Bowie and Sauer, “Identifying the Determinants of Folding and Activity”;
Reidhaar-Olson and Sauer, “Functionally Acceptable Substitutions”;
Chothia, Gelfand, and Kister, “Structural Determinants in the Sequences of
Immunoglobulin Variable Domain”; Axe, “Extreme Functional
Sensitivity”; Taylor, et al., “Searching Sequence Space for Protein
Catalysts.”
15. See, e.g., Perutz and Lehmann, “Molecular Pathology of Human
Hemoglobin.”
16. Axe, “Estimating the Prevalence of Protein Sequences.”
17. Axe, “Estimating the Prevalence of Protein Sequences.”
18. Cramer, Bushnell, and Kornberg, “Structural Basis of Transcription.”
19. Hoyle and Wickramasinghe, Evolution from Space, 24–27.
20. Morowitz, Energy Flow in Biology, 5–12; Hoyle and Wickramasinghe,
Evolution from Space, 24–27; Cairns-Smith, The Life Puzzle, 91–96;
Prigogine, Nicolis, and Babloyantz, “Thermodynamics of Evolution,” 23–
31; Yockey, Information Theory and Molecular Biology, 246–58; “SelfOrganization Origin of Life Scenarios and Information Theory”; De Duve,“The Beginnings of Life on Earth,” 437; Shapiro, Origins, 117–31; Crick,Life Itself, 88.
Chapter 10: Beyond the Reach of Chance
1. The number of possible ways to combine elementary particles (and thus thenumber of combinatorial possible events) is actually much greater than thenumber of different events that could have taken place in the history of theuniverse. Why? Because the occurrence of each individual event precludes
the occurrence of many other possible events within the larger
combinatorial space. The number of combinatorial possible events
represents the number of different events that might have occurred before
the universe actually unfolded in the way that it did. Dembski correctly
identifies the maximum number of events that could actually occur in any
given history of the universe as the number that determines the probabilistic
resources of the universe. This smaller number determines how many
opportunities the universe has to produce a particular outcome by chance.
As Dembski explains, it is not the total number of combinatorial possible
events (or elementary particles) in the universe that determines theavailable probabilistic resources, but how many opportunities there are to“individuate” actual events. See Dembski, The Design Inference, chap. 6;
see also 209, n.15.
2. For Dembski’s treatment of probabilistic resources at the scale of theknown universe, see Dembski, The Design Inference, chap. 6.
3. The elementary particles enumerated in this calculation include only
protons, neutrons, and electrons (fermions), because only these particles
have what physicists call “half-integral spin,” which allows them to formmaterial structures. This calculation does not count bosons, which cannot
form material structures, but instead only transmit energy. Nor does thiscalculation count the quarks out of which protons and neutrons are made,because quarks are necessarily bound together within these particles. Even
if quarks were counted, however, the total number of elementary particles
would change by less than one order of magnitude because there are only
three quarks per proton or neutron.
4. To be safe, Dembski rounded the number he had calculated up a few ordersof magnitude to 10150, though without any physical or mathematicaljustification. Since he didn’t need to do this, I decided to use his moreaccurate, if less round, number as the actual measure of the probabilistic
resources of the universe in my evaluations of the chance hypothesis.
5. Cryptographers, for instance, have established 1 chance in 1094 as auniversal limit. They interpret that improbability to mean that if it requires
more than 1094 computational steps to decrypt a cryptosystem, then it ismore likely than not that the system won’t be cracked because of thelimited probabilistic resources of the universe itself.
6. Borel, Probabilities and Life, 28–30.
7. Van de Sande, “Measuring Complexity in Dynamical Systems.”
8. Dam and Lin, eds., Cryptography’s Role in Securing the Information
Society, 380, n. 17; Lloyd, “Computational Capacity of the Universe”; seealso Kauffman, Investigations, 144.
9. Sometimes after I explain the odds against producing a protein of evenmodest length by chance, someone will ask why a shorter functional
protein couldn’t have arisen by chance and then gradually evolved into alarger one. Sometimes critics point out that some functional amino acidchains, such as peptide hormones, have fewer than 150 amino acids. Some
peptide hormones, for example, are just a few tens of amino acids long,though some are much longer. And there are also some proteins that areshorter than 150 amino acids. Critics ask me, “Couldn’t such molecules
have arisen by chance and then evolved into longer functional molecules?”There are a number of problems with this scenario.
First, functional proteins (including all enzymes) depend upon complex
folds, or “tertiary structures.” Attaining tertiary structure in proteinsrequires about 50 properly sequenced amino acids for the simplest
structures and many more (typically hundreds) for more typical structures.
Moreover, these thresholds of minimal function vary from protein toprotein. Just because one protein fold or tertiary structure may need “only”
50 specifically sequenced amino acids does not mean that another can form
with that few. Most can’t. The protein equivalent of a ruler may form with
only 50 amino acids, but the hammer and saw may need 150, the wrench
200, and the drill 300. Many of the functions that a minimally complex cellrequires depend upon these longer proteins. Thus, the presence of someshorter proteins or peptide hormones in living systems does nothing toobviate the need for many larger proteins in the origin of life.
Moreover, as protein chemist Doug Axe explains in more detail in aforthcoming article (“The Nature of Protein Folds: Quantifying theDifficulty of an Unguided Search Through Protein Sequence Space”) there
are physical reasons that short proteins with small tertiary structures can’t
be gradually transformed into larger tertiary structures. Short proteinstypically exhibit a hydrophilic exterior. To build a larger structure aroundthem, at least some of this hydrophilic exterior would have to become
interior to the larger structure. But this requires, among other things, that a
region of hydrophilic surface become hydrophobic, which in turn requires
many simultaneous amino acid changes. Having a short protein to start with
contributes little or nothing toward building a larger one. The sameprobabilistic hurdles have to be overcome in sequencing.
In any case, it is important to distinguish between peptides that function
without a folded structure at all and proteins that function only with afolded structure. The former (which includes the shorter peptide hormones)
are functional only by virtue of binding to larger folded protein structures.
But this implies that these shorter molecules have no function—and noselective advantage—apart from the prior existence of much larger protein
molecules. Thus, citing functional peptide hormones as a starting point inevolution begs the question as to the origin of the larger protein moleculesthat give them functional significance. It only pushes the problem back towhere it started—to the problem of explaining the origin of large
functionally specified proteins by chance. Indeed, absent long functional
protein molecules—and, realistically, a minimally complex selfreproducing cell—there would be no context to confer functionalsignificance or advantage on either unfolded peptide hormones or shorter
proteins (for that matter).
10. If Slick had replaced each marble before he searched again, the number ofmarbles he would have needed to sample to have a 50 percent chance offinding the red one would have been significantly more than 5,000. The
probability of selecting a red marble in n random draws with replacementfrom the sack is given by the equation 1–q^n, where q equals the proportionof blue marbles. The variable q is given by the equation q = 1–p where p is
the proportion of red marbles in the gunnysack. If p = .0001, then q =1–.0001, or .9999. It follows that Slick would have a 50 percent chance offinding a red marble only after he had sampled 6,931 marbles. Indeed, theprobability of finding the red marble reaches 1/2 only if n = log (.5)/log(.9999) = 6,931. For the relevant math see Feller, An Introduction toProbability Theory, 49.
11. For the record, most of the skeptics about the chance hypothesis to whom
I refer here (Christian de Duve, Alexander G. Cairns-Smith, P. T. Mora,
Hubert Yockey, Ilya Prigogine, Robert Shapiro, the later Francis Crick ca.
1980) were not advocates of intelligent design.
12. Mora, “Urge and Molecular Biology,” 215.
13. Miller, “A Production of Amino Acids.”
14. Abelson, “Chemical Events on the Primitive Earth”; Florkin, “Ideas andExperiments in the Field of Prebiological Chemical Evolution”; Fox andDose, Molecular Evolution and the Origin of Life, 43, 74–76; Cohen,“Novel Center Seeks to Add Spark to Origins of Life”; Walker, Evolutionof the Atmosphere, 210, 246; Kerr, “Origin of Life”; Thaxton, Bradley, andOlsen, The Mystery of Life’s Origin, 73–94; Horgan, “In the Beginning”;
Kasting, “Earth’s Early Atmosphere.”
15. Towe, “Environmental Oxygen Conditions”; Berkner and Marshall, “On
the Origin and Rise of Oxygen Concentration,” 225; Kasting, “Earth’s
Early Atmosphere”; Brinkman, “Dissociation of Water Vapor and
Evolution of Oxygen,” 5355; Dimroth and Kimberly, “Pre-CambrianAtmospheric Oxygen,” 1161; Carver, “Prebiotic Atmospheric Oxygen
Levels,” 136; Holland, Lazar, and McCaffrey, “Evolution of the
Atmosphere and Oceans”; Kasting, Liu, and Donahue, “Oxygen Levels in
the Prebiological Atmosphere”; Kerr, “Origin of Life”; Thaxton, Bradley,
and Olsen, The Mystery of Life’s Origin, 73–94.
16. Holland, The Chemical Evolution of the Atmosphere and Oceans, 99–100;
Schlesinger and Miller, “Prebiotic Synthesis in Atmospheres Containing
CH4, CO, and CO2,” 376; Horgan, “In the Beginning.”
17. Thaxton, Bradley, and Olsen, The Mystery of Life’s Origin, 69–98;
Dembski and Wells, The Design of Life, 222–24.
18. Thaxton and Bradley have shown that polymerizing amino acids under
reducing conditions releases 200 kcal. of energy per mole, whereas
polmerizing amino acids in neutral conditions requires an input of 50 kcal.
of energy per mole (“Information and the Origin of Life,” 184).
19. Brooks, The Origins of Life, 118.
20. Schwartz, “Intractable Mixtures and the Origin of Life,” 656.
21. Dean Kenyon, in Thaxton, Bradley, and Olsen, The Mystery of Life’s
Origin, vi.
22. Thaxton, Bradley, and Olsen, The Mystery of Life’s Origin, 102.
23. Thaxton and Bradley, “Information and the Origin of Life,” 184; Thaxton,

Bradley, and Olsen, The Mystery of Life’s Origin, 100–101.
24. De Duve, “The Beginnings of Life on Earth,” 437; emphasis added.
25. Koonin, “The Cosmological Model of Eternal Inflation.”
Chapter 11: Self-Organization and Biochemical Predestination
1. Kenyon and Steinman, Biochemical Predestination, 31.
2. Kenyon and Steinman, Biochemical Predestination, 206.
3. Kenyon and Steinman, Biochemical Predestination, 207.
4. These differences in bonding affinity are due to steric (spatial) hindrances
between the side chains of the different amino acids. Longer side chainsblock each other and impede the formation of peptide bonds.
5. Kenyon and Steinman, Biochemical Predestination, 207.
6. The most prominent advocate of the protein-first origin-of-life model
during this time period was Sidney Fox. Fox not only thought that the firstfunctional macromolecules would have been proteins, but he thought thefirst protocells would have been enclosed with membranelike enclosures
made from polypeptides. He called these circular enclosures proteinoid
microspheres. See Fox, “Simulated Natural Experiments.”
7. Biochemical Predestination cemented Kenyon’s reputation as a world-classorigin-of-life researcher. For example, in 1974, Kenyon was invited to
publish an article in a prestigious Feschrift in honor of Aleksandr I. Oparin.
The volume included essays from most of the leading origin-of-liferesearchers at the time and included an introduction from Oparin himself.
See Kenyon, “Prefigured Ordering and Proto-Selection in the Origin ofLife,” in Dose, et al., The Origin of Life and the Evolutionary Biochemistry,
207–20; see also table of contents, v–vii.
8. Solomon Darwin, http://www.haas.berkeley.edu/faculty/darwin.html (lastaccessed September 9, 2008). Professor Kenyon conveyed the details of
this story to me in a personal interview.
9. Steinman and Cole, “Synthesis of Biologically Pertinent Peptides,” 745–41.
Though Kenyon and Steinman expected that the amino-acid sequences in
the proteins in extant organisms would strongly resemble the original
proteins from which they had evolved, they did not expect that they would
exhibit an exact sequence identity. Instead, as Kenyon told me, “we thoughtthat the primordial amino-acid sequences would have different mutation
histories within the subsequently evolving lineages of cells, but thought thatmuch of the primordial sequence specificity would still be intact today.”
10. Kok, Taylor, and Bradley, “A Statistical Examination of Self-Ordering.”
11. Kamminga, “Studies in the History of Ideas,” 301–4, 308–12.
12. Crick, Of Molecules and Men, 10.
13. Dawkins, River Out of Eden, 17.
14. Polanyi, “Life Transcending Physics and Chemistry,” 55.

15. Polanyi, “Life Transcending Physics and Chemistry,” 55; “Life’s
Irreducible Structure,” 1309.
16. Polanyi, “Life’s Irreducible Structure,” 1309.
17. Polanyi, “Life’s Irreducible Structure,” 1309.
18. Polanyi, “Life’s Irreducible Structure,” 1309.
19. The bases do participate in weak van der Waals and hydrophobic
interactions, but these chemical affinities are so slight and nonspecific as to
be incapable of determining the specific sequential arrangements of thebases in the DNA molecule.
20. Küppers, “On the Prior Probability of the Existence of Life,” 364.
21. De Duve, “The Beginnings of Life on Earth,” 428–37, esp. 437.
22. Note that the “RNA world” scenario was not devised to explain the originof the sequence specificity of biomacromolecules. Rather, it was proposed
as an explanation for the origin of the interdependence of nucleic acids andproteins in the cellular information-processing system. By proposing an
early earth environment in which RNA performed both the enzymaticfunctions of modern proteins and the information-storage function ofmodern DNA, “RNA first” advocates sought to formulate a scenario
making the functional interdependence of DNA and proteins unnecessary to
the first living cell. In so doing, they sought to make the origin of life amore tractable problem. Chapter 14 examines this hypothesis and the many
problems it has encountered.
23. An article heralding a breakthrough for “RNA world” scenarios makes
this clear. After telling how RNA researcher Jack Szostak had succeeded inengineering RNA molecules with a broader range of catalytic properties
than previously known, science writer John Horgan makes a candid
admission: “Szostak’s work leaves a major question unanswered: How did
RNA, self-catalyzing or not, arise in the first place?” (“The World
According to RNA”).
24. The National Center for Biotechnology Information (NCBI) maintains a
Web archive where the currently known variant codes, both nuclear andmitochondrial, are listed; see
http://www.ncbi.nlm.nih.gov/Taxonomy/Utils/wprintgc.cgi#SG4 (lastaccessed September 10, 2008).
25. Polanyi, “Life’s Irreducible Structure,” 1309.
26. This, in fact, happens where adenine and thymine interact chemically inthe complementary base pairing across the information-bearing axis of the
DNA molecule. Along the message-bearing axis, however, there are no
chemical bonds or differential bonding affinities that determine sequencing.
27. Stalnaker, Inquiry, 85.
28. Dretske, Knowledge and the Flow of Information (1981), 12.
29. As noted in Chapter 4, the information-carrying capacity of any symbol ina sequence is inversely related to the probability of its occurrence. The
informational capacity of a sequence as a whole is inversely proportional tothe product of the individual probabilities of each member in the sequence.
Since chemical affinities between constituents (“symbols”) increase theprobability of the occurrence of one, given another (i.e., necessity increases
probability), such affinities decrease the information-carrying capacity of asystem in proportion to the strength and relative frequency of such affinities
within the system.
30. Yockey, “Self-Organization Origin of Life Scenarios.”
31. Polanyi, “Life’s Irreducible Structure,” 1309.
Chapter 12: Thinking Outside the Bonds
1. Nicolis and Prigogine, Self-Organization in Nonequilibrium Systems, 339–
53, 429–47.
2. Prigogine, Nicolis, and Babloyantz, “Thermodynamics of Evolution,” 23–
31. Prigogine’s statement in full: “The probability that at ordinary
temperatures a macroscopic number of molecules are assembled to giverise to the highly ordered structures and to the coordinated functions
characterizing living organisms is vanishingly small. The idea of
spontaneous genesis of life in its present form is therefore highly
improbable, even on the scale of the billions of years during which
prebiotic evolution occurred.”
3. Yockey, “A Calculation of the Probability of Spontaneous Biogenesis,” esp.
380.
4. Eigen, Steps Toward Life, 12.
5. Kauffman, At Home in the Universe, 274.
6. Fry, The Emergence of Life on Earth, 158.
7. Kauffman, The Origins of Order, 285–341.
8. Kauffman, The Origins of Order, 299. He also cited proteins in the bloodclotting cascade that “cleave essentially single-target polypeptides” tosupport his claim that some low-specificity proteins can perform biological
functions.
9. Creighton, Proteins, 217–21.
10. See the entry for trypsin at http://www.expasy.org/cgi-bin/niceprot.pl?
P07477 (last accessed September 15, 2008).
11. Kauffman, The Origins of Order, 298.
12. Thaxton, Bradley, and Olsen, The Mystery of Life’s Origin, 127–43.
13. Shapiro, “A Simpler Origin of Life,” 47–52.
14. Shapiro, “A Simpler Origin of Life,” esp. 52.
15. Lifson, “On the Crucial Stages”; Joyce, “RNA Evolution and the Origin ofLife”; Dover, “On the Edge.”
16. Kauffman, At Home in the Universe, 47–92.

17. Kauffman, At Home in the Universe, 53, 89, 102.
18. Kauffman, At Home in the Universe, 86, 88.
19. De Duve, “The Beginnings of Life on Earth,” 437, emphasis added. De
Duve also notes: “The probability of any given card distribution is
infinitesimally small. Yet no bridge player has ever exclaimed at being
witness to a near-miracle. What would be a miracle, however, or, rather,
unmistakable evidence of trickery, is if the same distribution should be
dealt again, even once” (Singularities, 4).
20. De Duve, “The Beginnings of Life on Earth,” 437.
21. De Duve, Vital Dust, 9–10, emphasis added. In April 2000, I presented mycase for intelligent design in a talk titled “DNA by Design” in a session
with Christian de Duve at the “Nature of Nature Conference” at Baylor
University. In the discussion after my presentation, de Duve explained that
he had agreed with everything I had presented except “the last slide,” bywhich he meant my criticism of his statement on that slide. The slide towhich he referred contained the quote cited in the main body of the text towhich this note refers. De Duve’s response to my presentation revealed that
his differences with me were not scientific. Indeed, he has acknowledged
the same problems with the main naturalistic explanations for the origin of
biological information that I had described. Rather, our differences weremainly philosophical and methodological. Because of his understanding of
the definition of science, he was unwilling to consider the design
hypothesis. I was not.
Chapter 13: Chance and Necessity, or the Cat in the Hat Comes Back
1. Oparin, The Origin of Life (1938), 64–103, 107–8; Meyer, “Of Clues andCauses,” 174–79, 194–98, 211–12.
2. Oparin, The Origin of Life (1938), 133–35.
3. Oparin, The Origin of Life (1938), 148–59.
4. Oparin, The Origin of Life (1938), 195.
5. Oparin, Genesis and Evolutionary Development of Life, 146–47.
6. Kamminga, “Studies in the History of Ideas,” 326; Oparin, Genesis andEvolutionary Development of Life, 146–47.
7. Kamminga, “Studies in the History of Ideas,” 300–301, 308–28.
8. Kamminga, “Studies in the History of Ideas,” 301.
9. Kamminga, “Studies in the History of Ideas,” 323–28; Oparin, Origin ofLife on Earth (1957), 229–90; Genesis and Evolutionary Development ofLife, 127–51.
10. Versions of the gene theory, such as Richard Dawkins’s “selfish gene”
hypothesis (see Dawkins, The Selfish Gene) and the theory that life began
as self-replicating RNA, have persisted to the present day. The latter idea
has been the subject of extensive investigation during the mid-1980s.

11. Pattee, “The Problem of Biological Hierarchy,” esp. 123.
12. De Duve, Blueprint for a Cell, 187.
13. Dobzhansky, “Discussion of G. Schramm’s Paper,” 310.
14. Joyce and Orgel, “Prospects for Understanding,” esp. 8–13.
15. Mora, “The Folly of Probability,” 311–12; Bertalanffy, Robots, Men andMinds, 82.
16. Von Neumann, Theory of Self-Reproducing Automata.
17. Pennisi, “Seeking Life’s Bare (Genetic) Necessities,” 1098–99;
Mushegian and Koonin, “A minimal gene set,” 10268–73.
18. Wigner, “The Probability of the Existence of a Self-reproducing Unit,”
231–35; Morowitz, “The Minimum Size of the Cell”; Energy Flow in
Biology, 10–11.
19. Quastler, The Emergence of Biological Organization, ix.
20. Quastler, The Emergence of Biological Organization, 16.
21. Quastler, The Emergence of Biological Organization, 1.
22. Quastler, The Emergence of Biological Organization, 43.
23. Hubert Yockey, Information Theory and Molecular Biology, 247.
24. Though many regard hypercycles as a self-organizational model that
produces information purely by deterministic chemical reactions, I prefer toaddress them as a model that relies on both chance and necessity, because
the initial complement of information necessary to make them work (evenhypothetically) clearly does not come from any self-organization process
and thus presumably must come from chance.
25. Eigen and Schuster, “The Hypercycle.”
26. Smith, “Hypercycles and the Origin of Life”; Dyson, Origins of Life, 9–11, 35–39, 65–66, 78; Shapiro, Origins, 161.
27. See the critical discussion of Eigen’s hypercycle in Yockey, Information
Theory and Molecular Biology, 275–77. Yockey cites Eigen as
acknowledging that the “hypercyclic link would then become effective only
after concentrations have risen to sufficiently high level…. There is only
one solution to this problem: the hypercycle must have a precursor, present
in high natural abundance, from which it originates gradually by amechanism of mutation and selection” (275).
28. Dawkins, The Blind Watchmaker, 46–49; Küppers, “On the Prior
Probability of the Existence of Life.”
29. Küppers, “On the Prior Probability of the Existence of Life,” 366.
30. Dawkins reports the following as “10th generation” phrases, arising as
intermediate “progeny” of his Weasel program: MDLDMNLS ITJISWHRZREZMECS P and YYVMQKSPFTXWSHLIKEFV HQYSPY. This sequence contains a few
small words. A few small English words (for example, IT and ME) are
imbedded in these longer strings of gibberish. In the context of thesurrounding gibberish, however, these short strings perform nocommunication function and thus provide nothing “selectable” by the logic
of natural selection that Dawkins means to simulate. Even if they did, theseshort strings lack the complexity of genes and protein and thus do not
simulate the amount of complexity that would need to arise by chance inbiology before selection can play a role.
31. Berlinski, “On Assessing Genetic Algorithms.”
32. Schneider, “Evolution of Biological Information.”
33. See http://www-lmmb.ncifcrf.gov/~toms/paper/ev/(last accessed
September 17, 2008).
34. Schneider, “Evolution of Biological Information,” 2794, emphasis added.
35. Schneider, “Evolution of Biological Information,” 2796. In his simulation,
Schneider treats 131 specified digital characters as equivalent to a proteinbinding site on a gene. Schneider indicates that his computer organism has
265 digital characters, roughly 134 or so of which are dedicated tocomputing. That leaves 131 digital characters corresponding to the binding
site, the generation of which Ev attempts to simulate (see the caption to
Figure 1, p. 2796). A successful simulation would, therefore, involve
specifying 131 digital characters (“zeros” and “ones”), or 131 bits of
information.
36. Schneider, “Evolution of Biological Information,” 2795.
37. The amount of exogenous information is equal to the difference between
the amount of endogenous information and the amount of active
information. It represents the amount of information that a purely random
unguided search must generate after all active information has been used tosearch for a target.
38. Links to Robert Marks’s papers analyzing Ev and other evolutionary
algorithms are available on the interactive Web site supporting this book,www.signatureinthecell.com. See also Dembski and Marks, “The Search
for a Search.” Dembski and Marks, “The Conservation of Information.”
39. Adami and Brown, “Evolutionary Learning.”
40. Lenski, et al., “The Evolutionary Origin of Complex Features.”
41. Lenski, et al., “The Evolutionary Origin of Complex Features,” 139.
42. Pennock, “Does Design Require a Designer?” See also Pennock,“Learning Evolution.”
43. In these determinations, the character “1” typically represents true and thecharacter “0” typically represents false. After reading a pair of binary
digits, a computer applying an OR function will register a “1” if it is truethat one “or” the other of the digits represented true, that is, that one “or”
the other of the digits was a “1.” In the same way, if the computer applies
an AND to the pair of binary numbers it will register a “1” if bothcharacters—first and second—represent true—i.e., if they are “1’s.” It willregister a “0” otherwise.

44. In particular, the Avida organism generates an output indicating that thelogic function EQU has been performed on the input. Computer scientists
will recognize EQU as a compound logic function that is equivalent toAND, OR, and NOR being performed in sequence.
45. Lenski, et al., “The Evolutionary Origin of Complex Features,” 139.
46. Even as a demonstration of how biological evolution—mutation andnatural selection—might generate new information, Avida leaves much to
be desired. To understand why requires knowing a bit more about the way
Avida functions. After the evaluation algorithm has detected a logic
function, Avida will replicate the loop of instructions that generated thelogic function more frequently than it would have otherwise. Avida thenrandomly mutates the instructions in that loop before allowing them tooperate again on the set of input strings. As noted, each of these
instructions is, in fact, information-and content-rich. Yet Avida does not
mutate the content of these instructions themselves. Instead, it changes only
the instructions as discrete packets of specified information. Given theamount of information represented by each command, this presumably
simulates the equivalent of shuffling whole genes. Therefore, thisrearranging of instructions might simulate how a new structure andfunction made from several separate but preexisting gene products might
have arisen (via mechanisms such as inversion, recombination, or lateralgene transfer). Nevertheless, Avida does not simulate how new genes andproteins might have arisen, even from preexisting genes and self-replicatingorganisms. Yet clearly, theories of biological evolution must account for theorigin of such genetic information in order to account for the origin of neworganisms.
Avida rewards small incremental steps as functional improvements, but it
does so in a way that lacks biological realism. Although Avida rewards new
functions that are qualitatively similar to Darwinian evolution, it lacks
quantitative similarity to biology. Avida does not select for proximity tofuture function as Ev and earlier simulations did. Instead, it rewards
incremental additions of function—simple logic functions—along the way
to its desired end point, the compound logic function EQU. In Avida theseintermediate functional steps are relatively easy to generate by a random
search. In biology, however, no comparably modest changes produce newprotein structures. Yet such structures must arise before any truly novel
function can arise during biological evolution. And any complete theory of
biological evolution must explain the origin of such novel proteins and thegenetic information required to produce them.
The authors of Avida hoped that their simulation would produce acompound logic function, namely, EQU (EQU represents “equal”). EQU is
a compound logic function equivalent to a combination of other logic

functions operating on bit pairs. For this reason, the authors of the Avida
paper regard it as a reasonable representation of a functionally integrated or“irreducibly complex” biological system in their simulation. Nevertheless,the programmers of Avida knew that they could build EQU byconcatenating a series of less complex logic functions together. So they
designed their evaluation algorithm to reward each of these less complex
logic functions (NAND, AND, OR, and NOR) as well. This, by itself,
would not necessarily compromise the realism of the Avida simulation,
were it not for the size of the steps that the Avida simulation rewards. In
theory, at least, natural selection might select and preserve a series of small
incremental changes provided each of the smaller increments confers afunctional advantage along the way toward producing a more complex
structure. To produce new biological information, however, even startingfrom a self-replicating organism, requires producing new genes that findexpression as new proteins. Biological change is denominated in novel
proteins. In other words, proteins represent the smallest functionallysignificant (and selectable) step in evolution. And building new proteinsrequires new genetic information.
Since natural selection acts upon or preserves new functional proteins
only after the fact of a successful search, random mutations must do thework of generating these new genes and proteins. Yet finding new proteins(and the genetic information necessary to produce them) by a random
search is a vastly more difficult proposition than Avida simulates. Instead,the logic functions that Avida rewards can be produced easily given theprobabilistic resources available to it. Yet the same thing cannot be said ofnovel protein structures.
Bob Marks has written a similar simulation program to replicate thefunction of Avida for purposes of evaluation. He used loops with a fixed
length of 50 without the instructions needed for organism duplication. He
found the NAND logic function, out of which EQU can be made, aroseroughly once every 2,297 times. The other logic functions out of which
EQU can be built—AND, OR, and NOR—arose roughly once every
892,193; 3,605,769; and 157,894,737 (1.57 × 109 times), respectively.
Though these frequencies are clearly small, Avida had more than enoughtrials (probabilistic resources) available to it to ensure that these functions
would arise. Avida also had more than enough trials to ensure that these
simple functions would combine in enough different ways to ensure that thecompound EQU function would eventually arise from one of the
combinations. (In Professor Marks’s simulation of Avida, EQU arose 9times in 6 billion queries.)
Yet, as noted in Chapter 9, site-directed mutagenesis experiments haveshown that the odds of generating a functionally sequenced protein of a

modest length (150 residues) via a random search stand at about 1 chancein 1074. In other words, the ratio of functional proteins of modest length tononfunctional amino-acid sequences of equivalent length is about 1 in 1074.
To sample a combinatorial space this size by a series of undirected trials
would require probabilistic resources far in excess of those available to theentire history of the earth. (Recall that the probabilistic resources
corresponding to the 4.6 billion year history of the earth stand at about 1041
possible events.) Yet many crucial evolutionary transitions, including theCambrian explosion in which many novel body plans as well as proteinsfirst arose, took place within 5 to 10 ten million years, in less than .2
percent of the history of the earth. I have argued elsewhere that these facts
cast doubt on the mechanism of mutation and selection as an adequate
explanation of the origin of new genes and proteins. They certainly alsosuggest that Avida, whatever its other merit, does not provide a realistic
simulation of the origin of new genetic information in a biological context.
That is, Avida does not realistically simulate the generation of new
functional genetic information even in a context in which the informationnecessary to a self-replicating organism is already present. See Meyer, “TheOrigin of Biological Information.”
47. Pennock, “Does Design Require a Designer?”
48. Wolpert and Macready, “No Free Lunch Theorems for Optimization.”
49. Brillouin, Science and Information Theory, 269.
50. Personal interview with Robert Marks, June 2008.
51. William Dembski gives a more generalized expression and justification ofthis law in his paper “Intelligent Design as a Theory of Information.”
Chapter 14: The RNA World
1. Gilbert, “Origin of Life.”
2. Meyer, “DNA and Other Designs.” See the write-up in the Chronicle ofHigher Education, March 29, 2000. See also Correspondence, First Things,
October (2000): 2–3.
3. Miller, “How Intelligent Is Intelligent Design?”
4. Szostak, Bartel, and Luisi, “Synthesizing Life,” esp. 387–88.
5. Gilbert, “Origin of Life.”
6. Gilbert, “Origin of Life.”
7. Shapiro, “Prebiotic Cytosine Synthesis”; Waldrop, “Did Life Really Start
Out in an RNA World?”
8. Levy and Miller, “The Stability of the RNA Base; Shapiro, “A Simpler
Origin of Life,” 49.
9. Levy and Miller, “The Stability of the RNA Bases,” 7933.
10. Shapiro, “Prebiotic Cytosine Synthesis,” 4396.

11. Shapiro, “Prebiotic Ribose Synthesis,” esp. 71.
12. Prebiotic chemists have proposed that the nucleotide bases, particularly
adenine, could have been produced on the early earth from hydrogencyanide, HCN, reacting in solutions of ammonium hydroxide, NH4 (OH).
This reaction does produce small yields of adenine and other nitrogenous
bases, at least if the reaction starts with extremely high (though probably
unrealistic) concentrations of hydrogen cyanide. But this reaction also
produces a variety of nitrogenous substances that quash the formose
reaction. This fact creates a formidable difficulty for the RNA-world
hypothesis: the main reaction proposed for producing nucleotide bases in a
prebiotic environment also prevents synthesis of ribose. Yet both substances
are needed to make RNA. See Shapiro, “Prebiotic Ribose Synthesis,” 81–
82.
13. Kenyon and Mills, “The RNA World.”
14. Shapiro, “Prebiotic Ribose Synthesis,” 71.
15. Noller, Hoffarth, and Zimniak, “Unusual Resistance of Peptidyl
Transferase.”
16. Zhang and Cech, “Peptide Bond Formation.”
17. Illangasekare, et al., “Aminoacyl-RNA Synthesis Catalyzed by an RNA.”
18. Joyce, “RNA Evolution and the Origins of Life.”
19. Gil, et al., “Determination of the Core of a Minimal Bacterial Gene Set,”
Table 1, 521–22.
20. Wolf and Koonin, “On the Origin of the Translation System.”
21. Wolf and Koonin, “On the Origin of the Translation System,” 6.
22. Kumar and Yarus, “RNA-Catalyzed Amino Acid Activation.”
23. There is another difficulty associated with generating the enzymatic
capacities of synthetases in an RNA world. The probability of ribozymes
arising with even the limited capacity to catalyze aminoacyl bonds is verysmall. The first researchers who found an RNA molecule capable of selfaminoacylation with phenylalanine had to sift through a preengineered poolof 170 trillion (or 1.7 × 1014) RNA molecules (see Illangasekare, et al.,“Aminoacyl-RNA Synthesis Catalyzed by an RNA”). This suggests that theprobability of finding a single RNA molecules that could catalyze theformation of this bond is roughly one chance in 1014. But to generate an
RNA-based genetic code equivalent to that in the modern translation
system would require not just one such ribozyme, but nineteen others(corresponding to each aminoacyl-tRNA synthetase enzyme) working
together as a system, each with its own specific role. And that would
require sequestering all the components of the system in a compartmentthat prevents interference from useless RNAs. If the other necessary
ribozymes were roughly as rare as the first, then the probability ofsequestering one additional ribozyme that performs the same function witha different amino acid would be the square of the original probability, orless than chance 1 in 1028. The probability of sequestering three suchribozymes in close quarters would be the cube of that initial probability, orless than one chance in 1042. The probability of sequestering twenty suchribozymes in close enough proximity to function as a system—as a part of agenetic code—would be prohibitively small, no better than 1 chance in10280. Overcoming these odds would require a huge infusion of information(930 bits). And still these ribozymes would not be capable of coordinating
the complex two-stage reaction that actual synthetase enzymes perform in
extant cells.
24. Some researchers cite the ability of certain RNA molecules to catalyze
peptide bonds outside the ribosome (albeit in the presence of anothercatalyst) as support for the RNA-world hypothesis (see, e.g., Zhang andCech, “Peptide Bond Formation”). But the mere presence of an RNA
molecule capable of catalyzing peptide bonding would not obviate the needfor a system of proteins to translate mRNA into proteins. This scenario has
two obvious problems, however. (1) Ribozymes facilitating peptide-bondformation in the absence of an mRNA transcript produce random, notsequence-specific, arrangements of amino acids. Yet as shown in Chapters
8–10, formidable probabilistic hurdles face any chance-based scenarios forthe origin of sequence-specific functional proteins. (2) RNA-catalyzed
peptide bonding outside of a ribosome will lead to the production of many
improperly formed and folded peptide chains. Because the side chains ofseveral amino acids feature amino groups and/or carboxyl groups, manybranching peptide linkages can form. This would essentially preclude
structures like natural protein folds. Indeed, papers that have acknowledged
the possibility of rRNA catalyzing peptide bonds in the absence ofribosomal proteins have also acknowledged that the ribosome as a wholeprovides critical “substrate positioning” during protein synthesis. (SeeRodnina, Beringer, and Bieling, “Ten Remarks on Peptide Bond
Formation.”)25. The twenty protein-forming amino acids are capable of a much wider
range of chemical interactions than are the four nucleotide bases. The fourbases are exclusively hydrophilic (water-attracting) molecules, and they
form mainly hydrogen bonds with each other. On the other hand, someamino acids are hydrophilic; some are hydrophobic; some are acidic; andsome are basic. This diversity of properties among its constitutive chemicalgroups allows proteins to attain more complex three-dimensional
geometries than do the simple pairing interactions between bases in RNA
molecules. As a result, proteins play functional roles that RNA does notand, in many cases, probably cannot.
26. By coupling and enabling energetically favorable and unfavorable
reactions, synthetase enzymes catalyze the production of a molecule—acharged tRNA ready for translation—that would not form in any
appreciable quantities otherwise—even with the help of two separate RNA
catalysts. An RNA catalyst might drive forward the energetically favorable
first reaction that activates amino acids with AMP. But even a separate
RNA catalyst will not drive forward the energetically unfavorable second
aminoacylation reaction (unless massive amounts of the reactants areprovided to overcome the unfavorable energetics).
27. Quastler, The Emergence of Biological Organization, 16.
28. Recently, Lincoln and Joyce claim to have produced a fully selfreplicating RNA molecule. Nevertheless, their claim trades upon an
ambiguity in the meaning of self-replication and constitutes little more thana gimmick. True template-directed polymerases can copy any templateusing free-floating bases from their surroundings.
Polymerases do the work of copying a template by sequestering,aligning, and linking bases on a template strand. For a polymerase tofunction as a true replicase, it would likewise have to do the work of
replicating a template, in this case the template provided by itself.
The RNA molecules that Lincoln and Joyce devise do not do this work.
Instead, they simply joined together via a single bond two presynthesized,
specifically sequenced RNA chains to form a longer chain. After theformation of a single phosphate bond, these linked chains resulted in a copyof the original RNA molecule, but only because Lincoln and Joyce first
designed the original RNA molecule and then directed the synthesis of two
specifically sequenced, complementary partial strands to match it. Thus,
Lincoln and Joyce provided the information (did the sequencing work)required to make even this limited form of replication possible. Further,instead of demonstrating RNA-directed self-replication, they demonstrated
investigator-directed replication. Lincoln and Joyce, “Self-Sustained
Replication of an RNA Enzyme,” 1–6. Hayden, “A Never-Ending Dance of
RNA: The Recreation of Life’s Origins Comes a Self-Catalysing Step
Closer.”29. Johnston, et al., “RNA-Catalyzed RNA Polymerization.”
30. Joyce and Orgel, “Progress Toward Understanding the Origin of the RNA
World,” esp. 33.
31. Szostak, Bartel, and Luisi, “Synthesizing Life,” 389.
32. Johnston, et al., “RNA-Catalyzed RNA Polymerization,” 1321.
33. Because of the complex chemical interactions of their amino-acid side
chains, proteins form much more complex three-dimensional shapes thanRNA molecules do. The inability of RNA to form the same kind of subtle

three-dimensional geometries that proteins form may limit the capacity ofribozymes to perform many of the functions that proteins perform. It also
appears to limit the efficiency and usefulness of known ribozymes incomparison to their protein counterparts.
Gerald Joyce has noted, for example, that ribozyme polymerases are notnearly as stable and do not perform nearly as well as wild-type
polymerases. He notes that the RNA-only polymerases have several
limitations that naturally occurring polymerases do not. First, they copy
templates of genetic information more slowly. Second, they can copy only
certain templates of information without stalling. Third, they copy with lessfidelity than wild-type RNA polymerases. Fourth, ribozymes require ahigher concentration of RNA monomers to facilitate copying than do theirwild-type counterparts.
Joyce summarizes the functional problems with the Bartel labengineered 189-nucleotide class 1 ligase: “Closer inspection of thepolymerase ribozyme reveals some of its key limitations. First, although the
kcat for NTP [nucleoside triphosphate, a unit of RNA that includes thesugar-phosphate backbone and the nucleotide base] addition is greater than
1 min–1, the Km for the separate template-primer complex is immeasurablyhigh and in excess of 1 mM. In practical terms this means that if one
employs typical concentrations of 1 M template-primer, about 2 hours are
required for a productive substrate-binding event. For most templates the
ribozyme has little processivity, that is, little ability to add multiple NTPs
before dissociating from the template-primer complex. Thus another 2hours are required for the next productive binding event. The ribozyme issusceptible to hydrolysis of its component phosphodiester linkages, andunder the preferred reaction conditions of 200 mM of MgCl2 at pH 8.5 and
22°C, suffers nonspecific cleavage of one of its phosphodiesters at a rate ofabout 10–2 min–1. Thus in the race between NTP addition and degradationof the ribozyme, it is possible to achieve about 12 NTP additions in 24
hours, but not many more, because by then the ribozyme is largely
degraded.
“A second limitation of the class-I-derived polymerase ribozyme is that,
although for one special template it can add up to 14 successive NTPs, formore typical templates it adds only a few NTPs. Even a very subtle change
in the sequence of the preferred template dramatically reduces the extent of
NTP addition. A third limitation is that, although the fidelity of template
copying is high when measured for the full-length products, the overall
fidelity is considerably lower because incorporation of the wrong NTP
reduces the rate of subsequent extension. A fourth limitation is that theaffinity of NTP binding to the template is determined largely by thestrength of Watson-Crick pairing, thus requiring high concentrations of
NTPs and providing an inherent advantage for GC pairs” (“Forty Years of
In Vitro Evolution,” esp. 6430–31).
34. De Duve, Vital Dust, 23.
35. Joyce and Orgel, “Progress Toward Understanding the Origin of the RNA
World,” 35–36.
36. Joyce and Orgel, “Progress Toward Understanding the Origin of the RNA
World,” 33.
37. In theory, at least, Joyce and Orgel may have assumed an overly
restrictive condition on the origin of a self-replicating RNA system. They
are correct to note that self-replication requires two RNA molecules, one toact as replicator and one to act as template. Moreover, for a ribozyme to
replicate itself in one step, the template RNA would have to be the exact
reverse complement of the replicase RNA.
Nevertheless, it might be possible to copy the original replicator in two
steps if the original replicator had come into contact with another RNA
molecule capable of copying any RNA. This molecule would not need to be
the exact reverse complement of the original, but it would have to have asequence and structure making it capable of general replicase function.
That way, after this second replicase had produced a transcript of theoriginal replicase, the original one could transcribe this transcript. The
result would be an exact a copy of the original RNA replicase.
Extending the RNA world this extra benefit of the doubt, however, does
not improve the odds of it occurring or eliminate the displacement problem.
It only illustrates the displacement problem in a slightly different way. In
this scenario, RNA self-replication would arise after two long functional
RNA replicators had arisen in close spatial and temporal proximity to one
another. These two replicators would not need to be identically sequenced.
But both would need to be sequenced specifically to ensure replicase
function. Thus, instead of displacing a sequence-identity problem from one
molecule to another, this revised RNA-first hypothesis just displaces asequence-specificity problem from one molecule to another.
Because these two sequences would likely possess a roughly equal
degree of functional specificity, the probability of achieving a system of
two molecules capable of RNA self-replication would be roughly thesquare of the probability of achieving one such molecule. Though scientists
don’t know exactly how specified such sequences would have to be in orderto achieve RNA-catalyzed self-replication, the experimental evidence cited
above suggests that RNA molecules with this capacity are exceedingly rare
within the space of possible RNA base sequences. Since, additionally,
Orgel and Joyce made an overly optimistic assumption about the number ofbases necessary (only 50) to make an RNA capable of true self-replication,they probably vastly underestimated the probability of two sequencespecific replicases of any kind (whether identical or not) arising in close
proximity to one another.
Beyond that, ribozyme engineers are finding it more difficult to design
an RNA molecule that can copy any RNA sequence whatsoever. As Joyce
notes, all known RNA polymerases are template dependent, meaning thatthey can copy only complements of themselves with appreciable fidelity.
This means that the scenario that Joyce and Orgel envision—in which an
RNA replicase and its exact complement arise in close proximity to eachother—is still the only realistic one from a chemical—though perhaps not atheoretical—point of view.
38. Joyce and Orgel, “Prospects for Understanding the Origin of the RNA
World,” 35.
39. Shapiro, as quoted in Brockman, ed., Life: What a Concept!, 90.
40. Szostak, Bartel, and Luisi, “Synthesizing Life.”
41. Szostak, Bartel, and Luisi, “Synthesizing Life,” 388; Wolf and Koonin,“On the Origin of the Translation System.”
42. Ekland, Szostak, and Bartel have claimed that functional ribozymes are
more common in pools of RNA sequences than previously thought(“Structurally Complex and Highly Active RNA Ligases,” esp. 364). The
sequence space (the total number of possible combinations of nucleotides)
corresponding to the 220-base RNA molecules that they investigated isvast, 4220, or roughly 1 × 10132 possibilities. Since they were able to isolate
a ribozyme ligase by searching a tiny fraction of the whole space (about 1.4
× 1015 distinct sequences), this does imply that the whole space would
contain a vast number of comparable ligases. But, for such a simple
function to require a one-in-a-trillion sequence raises serious questions
about the rarity of more complex functions. As noted, for example,
ribozyme engineering has not yet produced a single ribozyme capable oftrue polymerase activity. Nor have researchers discovered ribozymes
capable of most other essential enzymatic functions.
43. As Wolf and Koonin note, “Because evolution has no foresight, no system
can evolve in anticipation of becoming useful once the requisite level ofcomplexity is attained” (“On the Origin of the Translation System”).
44. Crick, Life Itself, 88.
45. Dose, “The Origin of Life.”
46. Joyce and Orgel, “Prospects for Understanding the Origin of the RNA
World,” 19.
47. Because of the improbability of producing an RNA molecule with thespecificity necessary to perform self-replication, Christian de Duve andRobert Shapiro have proposed “pre-RNA world” scenarios. These scenarios

envision self-replication arising first in systems of smaller molecules, thus
reducing the probabilistic hurdles facing the RNA-first approach. Thesemodels face a different problem than that of the RNA world, however.
RNA-first models fail to explain the origin of biological information
because of the improbability of specific sequencing arising in molecules solarge (that is, in molecules large enough to perform replicase function).
Small molecule–first hypotheses avoid this probability problem byenvisioning smaller molecules arising first. Nevertheless, pre-RNA-world
scenarios encounter the difficulty of getting sufficient three-dimensional
complexity (and therefore biologically relevant specificity) to arise inmolecules so small. Roughly speaking, an RNA world can produce
complex molecules that aren’t sufficiently specific (to perform biologically
relevant functions). A pre-RNA world can produce specific molecules thataren’t sufficiently complex (to perform relevant functions). Thus, neither
actually solves the problem of the origin of biological information. These
theories are relatively new, however. I will track their progress as necessary
at: http://www.signatureinthecell.com. See de Duve, Vital Dust, 20–45;
Shapiro, “Small Molecule Interactions Were Central to the Origin of Life”;“A Simpler Origin of Life.”
Chapter 15: The Best Explanation
1. Gian Capretti and others explore the use of abductive reasoning by
Sherlock Holmes in the detective fiction of Sir Arthur Conan Doyle.
Capretti attributes the success of Holmesian abductive “reconstructions” toa willingness to employ a method of “progressively eliminating
hypotheses” (“Peirce, Holmes, Popper”).
2. Peirce, Collected Papers, 2:375, emphasis added; “Abduction andInduction.”
3. Capretti, “Peirce, Holmes, Popper,” 143.
4. Whewell, The Philosophy of the Inductive Sciences, 2nd ed., 1:24–25, 78–
80; 2:96, 101–4, 120–22, 397; Chamberlain, “The Method of Multiple
Working Hypotheses”; Scriven, “Explanation and Prediction in
Evolutionary Theory,” 480; “Causes, Connections and Conditions in
History,” 238–46; Meyer, “Of Clues and Causes,” chaps. 1–3; Lipton,
Inference to the Best Explanation; Miller, “The Similarity of Theory
Testing in the Historical and ‘Hard’ Sciences”; Cleland, “Historical
Science, Experimental Science, and the Scientific Method.”
5. See Meyer, “Of Clues and Causes,” chaps. 4–5.
6. Kamminga, “Protoplasm and the Gene,” 1.
7. Shannon, “A Mathematical Theory of Communication”; Shannon andWeaver, The Mathematical Theory of Communication.
8. Crick, “On Protein Synthesis,” esp. 144, 153.

9. Lipton, Inference to the Best Explanation, 32–98.
10. See Chapter 7. See, e.g., Scriven, “Explanation and Prediction in
Evolutionary Theory”; “Causes, Connections and Conditions in History,”
238–64; Sober, Reconstructing the Past, 1–5, 36–69; Lipton, Inference tothe Best Explanation, 32–88; Meyer, “Of Clues and Causes,” chaps. 1–3.
11. Meyer, “The Scientific Status of Intelligent Design,” 151–212; “TheDemarcation of Science and Religion,” 17–23; “Of Clues and Causes,” 77–
140; Sober, The Philosophy of Biology.
12. See, e.g., Charles Lyell’s discussions in Principles of Geology, 1:75–91;
3:1–7.
13. Monod, Chance and Necessity. Chance-based theories invoke processes
that produce particular outcomes with a low probability. Theories ofnecessity invoke processes that produce specific outcomes with a highprobability, typically with a probability of one. For this reason, these twogeneral categories of explanation plus explanations combining them are
generally considered to represent a logically exhaustive set of possible
explanatory approaches, at least from within a materialistic framework.
14. See Ben Stein’s final interview of Richard Dawkins in the documentary
film Expelled: No Intelligence Allowed.
15. Thaxton, Bradley, and Olsen, The Mystery of Life’s Origin, vi.
16. Thaxton, Bradley, and Olsen, The Mystery of Life’s Origin, 102.
17. Shapiro, “Prebiotic Ribose Synthesis.”
18. Thaxton, Bradley, and Olsen, The Mystery of Life’s Origin, 184.
19. Polanyi, “Life Transcending Physics and Chemistry,” esp. 64.
20. Dawkins, The Blind Watchmaker, 47–49; Küppers, “On the Prior
Probability of the Existence of Life”; Lenski, et al., “The Evolutionary
Origin of Complex Features”; Schneider, “Evolution of Biological
Information.”
21. Denton, Evolution, 309–11.
22. Lawrence and Bartel, “New Ligase-Derived RNA Polymerase
Ribozymes.”
23. Polanyi, “Life Transcending Physics and Chemistry,” esp. 64.
24. Paul and Joyce, “A Self-Replicating Ligase Ribozyme.”
25. “The RNA molecules A, B, and T (Fig. 1) were prepared either byautomated solid-phase synthesis or in vitro transcription [i.e., experimenterintervention]. B and T were obtained by transcription of the corresponding
DNA template to yield 5'–pppGAGACCGCAAUCC–3' and 5'–
pppGGAUUGUGCUCGAUUGUUCGUAAGAACAGUUUGAAUGGGUUGAAUAUAGAGACCGCAAUCC–3',
respectively. Due to poor yields obtained for the transcription of A, thisoligonucleotide, having the sequence 5'–
GGAUUGUGCUCGAUUGUUCGUAAGAACAGUUUGAAUGGGUUGAAUAUA–3', was prepared synthetically” (Paul and Joyce, “A
Self-Replicating Ligase Ribozyme,” 12734).
26. Dose, “The Origin of Life”; Yockey, Information Theory and Molecular
Biology, 259–93; Thaxton, Bradley and Olsen, The Mystery of Life’s Origin,
42–172; Thaxton and Bradley, “Information and the Origin of Life”;
Shapiro, Origins.
27. Of course, the phrase “large amounts of specified information” again begs
a quantitative question, namely, “How much specified information orcomplexity would the minimally complex cell have to have before it
implied design?” Recall that Dembski has calculated a universal probability
bound of 1 out of 10139 (which he now rounds to 1 chance in 10150)
corresponding to the probabilistic/specificational resources of the known
universe. Recall further that probability is inversely related to information
by a logarithmic function. Thus, the universal small probability bound of 1out of 10150 translates into roughly 500 bits of information. Thus, chancealone does not constitute a sufficient explanation for the de novo origin ofany specified sequence or system containing more than 500 bits of(specified) information. Further, since systems characterized by complexity(a lack of redundant order) defy explanation by self-organizational laws,
and since appeals to prebiotic natural selection presuppose, but do not
explain, the origin of the specified information necessary to a minimallycomplex self-replicating system, intelligent design best explains the originof the more than 500 bits of specified information required to produce thefirst minimally complex living system. Thus, assuming a nonbiologicalstarting point (see Chapter 13, section headed “The Conservation ofInformation”), the de novo emergence of 500 or more bits of specified
information will reliably indicate design.
28. See the relevant discussion in Peirce, Collected Papers, 2:372–88;“Abduction and Induction”; Fann, Peirce’s Theory of Abduction, 28–34;
Scriven, “Explanation and Prediction in Evolutionary Theory,” 480; Sober,Reconstructing the Past, 1–5; Alston, “The Place of the Explanation,” 23;
Gallie, “Explanations in History and the Genetic Sciences,” 392.
29. Sober, Reconstructing the Past, 1–5; Scriven, “Causes, Connections, andConditions in History,” esp. 249–50.
30. McNeil-Lehrer News Hour, Transcript 19 (May 1992).
31. Moreover, this generalization holds not only for the semantically specified
information present in natural languages, but also for other forms of
information or specified complexity, whether present in machine codes,
machines, or works of art. Like the letters in a section of meaningful text,
the parts in a working engine represent a highly improbable yet functionallyspecified configuration. Similarly, the highly improbable shapes in the rockon Mt. Rushmore conform to an independently given pattern: the faces ofAmerican presidents known from books and paintings. Thus, both systems
have a large amount of specified complexity or information so defined. Not
coincidentally, they also originated by intelligent design, not by chance
and/or physical-chemical necessity.
32. Again, this claim applies at least in cases where the competing causal
entities or conditions are nonbiological—or where the mechanism ofnatural selection can be safely eliminated as an inadequate means of
producing requisite specified information.
33. Meyer, “Of Clues and Causes,” 77–140.
34. Less exotic (and more successful) design detection occurs routinely inboth science and industry. Fraud detection, forensic science, andcryptography all depend on the application of probabilistic or informationtheoretic criteria of intelligent design. See Dembski, The Design Inference,
1–35.
35. Many would admit that we may justifiably infer a past human intelligence
operating (within the scope of human history) from an information-rich
artifact or event, but only because we already know that human minds
exist. But, they argue, since we do not know whether an intelligent agent(s)existed prior to humans, inferring the action of a designing agent that
antedates humans cannot be justified, even if we observe an informationrich effect. Note, however, that SETI scientists do not already knowwhether an extraterrestrial intelligence exists. Yet they assume that thepresence of a large amount of specified information (or even just an
unnaturally modulated radio signal) would establish the existence of one.
Indeed, SETI seeks precisely to establish the existence of otherintelligences in an unknown domain. Similarly, anthropologists have often
revised their estimates for the beginning of human history or civilization,
because they discovered information-rich artifacts dating from times that
antedate their previous estimates. Most inferences to design establish theexistence or activity of a mental agent operating in a time or place where
the presence of such an agency was previously unknown. Thus, to infer theactivity of a designing intelligence from a time prior to the advent of
humans on earth does not have a qualitatively different epistemologicalstatus than other design inferences that critics already accept as legitimate.
See McDonough, The Search for Extraterrestrial Intelligence.
36. Meyer, “The Explanatory Power of Design”; “DNA by Design”; “DNAand Other Designs”; “DNA and the Origin of Life.”
Chapter 16: Another Road to Rome
1. Dembski’s work on elucidating the forms of reasoning by which we infer
design goes back to a seminal paper he wrote on the nature of randomness,

“Randomness by Design.” It was this and another paper that first brought
Dembski to my attention and led to our initial collaboration. In it he showsthat randomness cannot properly be understood except with reference topatterns that random objects systematically violate—once a pattern ismatched, randomness dissolves. From studying the nature of the patterns
used to defeat randomness, Dembski came upon the design inference. He
presented an outline of the design inference in 1993 in Seattle in a paper,“Theoretical Basis for the Design Inference.” He developed these ideas intoa doctoral dissertation about the foundations of probability theory (1996)
and then went on to publish that dissertation in 1998: The Design Inference.
He expanded on this work in his 2002 sequel, No Free Lunch. His idea ofspecification, which sits at the heart of the design inference, is subtle andrequires some technical sophistication to elucidate in full, though theessence of the idea can be illustrated clearly with examples of the kind usedin this chapter. Dembski’s most current formulation of specification appears
in his article “Specification,” available at http://www.
designinference.com/documents/2005.06.Specification.pdf. The most userfriendly treatment of his work on design inferences, with an application tobiology, appears in Dembski and Wells, The Design of Life, chap. 7.
2. Dembski, The Design Inference, 1–35.
3. Dembski, The Design Inference, 1–35, 136–223.
4. Dembski, The Design Inference, 37.
5. Dembski’s initial work on specification focused on two requirements, aconditional independence condition of patterns from backgroundknowledge and a tractability condition for the descriptive complexity ofpatterns. The former could be characterized using conventional probability
theory. The latter could be characterized using Andrei Kolmogorov’s workon algorithmic information theory; see Kolmogorov, “Three Approaches to
the Quantitative Definition of Information.” Dembski held to both theserequirements throughout his books The Design Inference and No Free
Lunch. In 2005, however, he wrote a paper on specification thatsignificantly simplified its characterization, “Specification.” In that paper,
he was able to characterize specification simply in terms of tractability byshowing that conditional independence comes as a consequence oftractability (by tractability he means a pattern of low-descriptive
complexity). Specified complexity as Dembski now defines it signifies
high-probabilistic complexity of an event (i.e., improbability) combinedwith low-descriptive complexity of a pattern to which the event conforms.
6. Dembski illustrates this distinction with an example of an archer shooting
an arrow. If an observer witnesses an archer shooting an arrow and then thearrow hitting a target, the observer can correctly infer something about theskill and intent (design) of the archer. If, however, the archer shoots anarrow and then the observer goes and draws a circle around the arrow andcalls it the target, the observer can infer nothing about the skill of thearcher. In that case, the pattern was fabricated to match the event; it did notexist independently of the event.
7. Of course, intelligent agents can also produce repetitive patterns.
Nevertheless, so can natural causes and processes. For example,
electrostatic forces of attraction between ions form highly repetitive crystal
structures. Wave action on the beach produces patterns of interlocking arcs
of sand. Indeed, what scientists call laws of nature typically describe
natural processes that produce—of necessity—highly regular and repetitive
outcomes. For this reason, repetitive patterns do not necessarily indicate theactivity of intelligent causes, though they may and commonly do indicate
undirected lawlike processes. Knowing this, the croupier rightly suspecteda physical cause for the regularity he observed in the outcomes at theroulette wheel. When he found a physical cause that explained theregularity that he observed—the ball falling in the same hole every time—
he rejected chance as an explanation and elected physical necessity as, atleast, part of the explanation. A smart croupier would realize that design
might still have played a role in the observed regularity, for example, in thecase that someone had intentionally scarred the wheel to produce the defectresponsible for the ball landing in the same hole every time. Thisillustration suggests that explanations involving lawlike necessity anddesign do not necessarily mutually exclude each other, even though thepresence of a regularity by itself does not point decisively or exclusively to
an intelligent cause, but instead typically points to lawlike necessity.
8. Dembski, The Design Inference, 136–74.
9. Dembski, The Design Inference, 1–35, 136–74.
10. Axe, “Estimating the Prevalence of Protein Sequences.” See also
calculations after section head “Symposium at the Wistar Institute” in
Chapter 9.
11. Gamma, et al., Design Patterns.
12. Yockey, “Origin of Life on Earth,” esp. 105.
13. Crick, “On Protein Synthesis,” 144, 153.
Chapter 17: But Does It Explain?
1. In this regard, see, e.g., Elsberry and Wilkins, “The Advantages of Theftover Honest Toil”; Pennock, Tower of Babel, 171–72; Wilson, “Intelligent
Evolution”; Scott and Branch, “‘Intelligent Design’ Not Accepted by Most
Scientists.”
2. Quastler, The Emergence of Biological Organization, 16.
3. Lipton, Inference to the Best Explanation, 32–88.
4. Lipton, Inference to the Best Explanation, 32–88; Meyer, “The ScientificStatus of Intelligent Design”; “The Demarcation of Science and Religion”;“Of Clues and Causes,” 77–140; Sober, The Philosophy of Biology.
5. Shermer, Michael, “ID Works in Mysterious Ways.”
6. See, e.g., Miller “In Defense of Evolution”; Jones, Decision in Kitzmiller et
al. v. Dover Area School Board, No.04cv2688; Ruse, “Keep Intelligent
Design Out of Science Classes.”
7. Rothman, The Science Gap, 65–92; Popper, Conjectures and Refutations,
35–37. See also Oparin’s discussion of the impossibility of spontaneous
generation. Oparin, Origin of Life (1938), 27–28.
8. Meyer, “Of Clues and Causes,” 77–140; Sober, Reconstructing the Past, 4–5; De Duve, “The Beginnings of Life on Earth,” 249–50.
9. Harre and Madden, Causal Powers.
10. Barrow and Tipler, The Anthropic Cosmological Principle, 69.
11. See, e.g., G. S. Hurd, “The Explanatory Filter, Archaeology, andForensics”; Pennock, “The Wizards of ID.”
12. Arguably, the unnaturally modulated electromagnetic signals that SETI
scientists are looking for represent an improbable pattern, but notnecessarily evidence of digitally encoded, functionally specified
information. Some have noted this difference in the criterion that SETI uses
to detect intelligence in order to discredit ID proponents who have cited
SETI to legitimate design reasoning in biology. But, if anything, the SETI
standard for detecting intelligence constitutes a less demanding thresholdfor detecting intelligence than that used by ID advocates. Whereas SETI
requires only evidence of a channel of communication (i.e., an unnaturally
modulated signal), I have argued for design based upon the presence offunctionally specified digital code within the communication channel
represented by DNA and the gene-expression system. If the inference todesign is legitimate based upon the former criterion, it is a fortiori certainly
legitimate based upon the latter. Thus, SETI validates, rather thanundermines, the reasoning used to infer intelligent design as the best
explanation for the origin of biological information. See Sagan, Contact;
McDonough, The Search for Extraterrestrial Intelligence.
13. See Hume, Dialogues Concerning Natural Religion (1980), 61–66.
14. See, e.g., Forrest and Gross, Creationism’s Trojan Horse, 120ff.; Pigliucci,“Intelligent Design—The Classical Argument.”
15. Kay, “Cybernetics, Information, Life”; Who Wrote the Book of Life?
16. Kay, Who Wrote the Book of Life?
17. Kay, Who Wrote the Book of Life? 611–12, 629.
18. Sarkar, “Biological Information,” 199–202.
19. Schrödinger, What Is Life? 82.
20. Alberts, et al., Molecular Biology of the Cell, 21.
21. Watson and Crick, “A Structure for Deoxyribose Nucleic Acid”;

“Genetical Implications”; Crick, “On Protein Synthesis.”
22. Judson, The Eighth Day of Creation, 611.
23. Orgel, The Origins of Life, 189.
24. Davies, The Fifth Miracle, 120.
25. Orgel, The Origins of Life, 189.
26. Dawkins, The God Delusion, 147, 185–86, 188.
27. Dawkins, The God Delusion, 147, 185–186, 188. See also Gleiser, “WhoDesigned the Designer?”; Rosenhouse, “Who Designed the Designer?”
28. Moreover, explanations of particular events not only explain particular
events; they also provide information about conditions in the past, inparticular, the conditions that gave rise to the event in question. That is alsopart of what causal explanations do.
29. A second problem with the objection is that the theory of intelligent
design makes no claims about the nature of the designer. Moreover, some
philosophers question whether agents qua agents must necessarily becomplex. For the sake of argument, however, I grant the assumption that an
agent sophisticated enough to design the first life is both complex andhighly specified.
30. Tommyrot, “The Dawkins Delusion.”
31. Nor has evolutionary theory, or any other theory, given a causal
explanation of the origin of consciousness, the origin of mind itself.
Contemporary Darwinism may provide an explanation for the origin of thephysical features of higher primates including ourselves, but it does notgive anything like an adequate account of how consciousness first arose in
Homo sapiens. Indeed, even explaining what generates consciousness inindividual human agents now has proven utterly intractable from amaterialistic point of view. How does the flow of electricity in our neurons
or the reaction of chemicals in the brain produce my subjective experienceof “red” or my comprehension of mathematical truth or my awareness of
my own thoughts? How does chemistry produce consciousness? Leading
brain physiologists and philosophers of mind have long acknowledged thatthey do not know the answer to this question. Many doubt there is ananswer to it. See, e.g., Chalmers, The Conscious Mind; Eccles, Facing
Reality; Koestler, The Ghost in the Machine; Penfield, The Mystery of theMind, xiii; Popper and Eccles, The Self and Its Brain, viii; Searle, Mind;
Sherrington, Man on His Nature; Smythies, “Some Aspects ofConsciousness,” 235.
32. Additionally, ID advocates do not necessarily conceive of minds ascomplex (and functionally specified) collections of parts (or even symbols).
Some would argue that complexity (whether specified complexity or
irreducible complexity) is a feature that characterizes material effects, notself-conscious minds. Indeed, a long tradition in Western philosophy

conceives of mind as an immaterial entity consisting of no material parts, inwhich case it cannot be characterized as complex at all. This is, at least, a
logically coherent conception of mind. Thus, positing such a mind as thecause of the origin of biological information neither entails a contradiction
nor commits the ID advocate to any inconsistency in reasoning. The rule
“specified complexity always originates from intelligent design” is not
violated if advocates of intelligent design posit a designing mind that is notcomplex (or specified) in the way that material artifacts or communication
systems are.
33. This claim of inconsistency subtly misrepresents the basis of the argument
to intelligent design. Uniform and repeated experience does not show thatspecified complexity always points to an intelligent cause. Instead, uniform
and repeated experience shows that whenever specified information arises—originates—a mind always plays a causal role. Thus, we typically inferthat specified complexity points to an antecedent intelligence because ofthis principle. In those many cases where we have good reason to think thatspecified complexity not only exists, but also first originated in time, wealways infer an antecedent intelligence. Since an uncaused mind did not
originate in time, such a mind would not point to a prior designing
intelligence, even conceding for the sake of argument that minds arecomplex (and specified; see n. 32).
Experience says nothing about whether there might be a self-existentuncaused mind capable of initiating a novel chain of cause and effect. Evenso, that is a logically possible hypothesis for the origin of biologicalinformation. Nor would the existence of such an uncaused mind violate
what we know from experience about information-rich systems always
originating from intelligence. Since we have good reason to think that lifeand biological information originated at a time in the finite past, we havegood reason to think that an intelligent cause played a role in that event.
Nevertheless, inferring an intelligent designer for the origin of that
information does not entail an infinite regress of other designing
intelligences, since the designing intelligence in question may not itself
have originated, that is, begun to exist. The designing mind may haveexisted eternally, as theists think the mind of God has done. Dawkins may
not like this possibility, but an uncaused self-existent designer is, at least, a
logically possible candidate cause. Thus, inferring intelligent design fromthe information that originates with the first life does not entail an infinite
regress of similar designing minds.
Chapter 18: But Is It Science?
1. Jones, Kitzmiller et al. v. Dover Area School District, 400 F.Supp.2d 707,
720–21, 735 (M. D. Pa. 2005).

2. Jones, Kitzmiller et al. v. Dover Area School District, 400 F.Supp.2d 707,764 (M. D. Pa. 2005).
3. Prominent news reports include Wallis, “The Evolution Wars”; Ratliff,“The Crusade Against Evolution”; Wilgoren, “Politicized Scholars Put
Evolution on the Defensive.”
4. Judge Jones falsely claimed that: (1) the theory of intelligent design affirms
a “supernatural creation”—a position that ID proponents who testified incourt denied during the trial; (2) proponents of intelligent design make theircase solely by arguing against Darwinian evolution; (3) no peer-reviewed
scientific publications in support of the theory of intelligent design havebeen published in the scientific literature; and (4) the theory of intelligent
design has been refuted. The judge was presented with credible scientificwitnesses and publications on both sides showing evidence of a scientificdebate; incorrect theories necessarily do not qualify as scientific; the levelof support for a theory determines whether it qualifies as scientific. See
DeWolf, et al., Traipsing into Evolution; DeWolf, West, and Luskin,“Intelligent Design Will Survive Kitzmiller v. Dover”; Luskin, “Will
Americans United Retract Their Demonstrably False Claims?”
5. See Worden, “Bad Frog Beer to ‘Intelligent Design.’”
6. See West and DeWolf, “A Comparison of Judge Jones’s Opinion in
Kitzmiller v. Dover with Plaintiffs’ Proposed ‘Findings of Fact andConclusions of Law.’”
7. The stories of many such scientists are told in the documentary film
Expelled: No Intelligence Allowed. See also Wells, The Politically Incorrect
Guide to Darwinism and Intelligent Design.
8. There are numerous examples of this claim. Some include: “The claim thatlife is the result of a design created by an intelligent cause cannot be tested
and is not within the realm of science” (Skoog, “A View from the Past,” 1–2); “ID has never produced an empirically testable hypothesis” (Forrest and
Gross, Creationism’s Trojan Horse, 235); “The hypothesis of design iscompatible with any conceivable data, makes no testable predictions, andsuggests no new avenues for research” (Miller, Only a Theory, 87).
9. “There is no demarcation line between science and nonscience, or between
science and pseudo-science, which would win assent from a majority ofphilosophers” (Laudan, Beyond Positivism and Relativism, 210).
10. Laudan, “The Demise of the Demarcation Problem.”
11. Gould, “Evolution and the Triumph of Homology, or Why History
Matters,” 60–69.
12. For example, Pennock testified, “Intelligent design needs to have for it to
be a science a way of offering a specific hypothesis that one could the testin an ordinary way. They failed to do that, and so they really don’t get off
the ground with regard to science” (Kitzmiller v. Dover testimony,September 28, 2005, 39).
13. Behe, Darwin’s Black Box.
14. Meyer, et al., “The Cambrian Explosion.”
15. See, generally, Craig, “God, Creation and Mr. Davies,” 163; “Barrow and
Tipler on the Anthropic Principle vs. Divine Design,” 389.
16. Gonzalez and Richards, The Privileged Planet.
17. See Nelson and Wells, “Homology in Biology.”
18. Dembski, The Design Inference, 36.
19. Dembski, The Design Inference, 36–66.
20. As noted in Chapters 7 and 15, considerations of causal existence also
play a role in the evaluation—and testing—of historical scientific theories.
Indeed, historical scientific theories can fail by being unable to meet thiscritical test as well.
21. Shermer, Why Darwin Matters, 75.
22. Miller, Only a Theory, 37, 96–97.
23. See also Kitcher, Living with Darwin, 57.
24. Some advocates of intelligent design think that an intelligent cause isdirectly responsible for only the information present in the first livingorganisms; other ID advocates think intelligent design is responsible for the
information necessary to produce subsequent forms of life as well. Those
who hold the latter view predict that the nonprotein-coding DNA in both
eukaryotes and prokaryotes should perform functional roles. Those who
hold the former view predict that only the noncoding DNA in prokaryotes
should perform functional roles. The discovery that noncoding DNA plays
an important functional role in both prokaryotic and eukaryotic organisms
confirms the prediction of the more expansive ID hypothesis. I hold thislatter view. See Meyer, “The Origin of Biological Information.” In thisbook, however, I have argued only for intelligent design as the best
explanation of the origin of the information necessary to build the firstliving cell.
25. Dembski, “Intelligent Science and Design.” Here’s what Dembski writes
about junk DNA: “[Intelligent] design is not a science stopper. Indeed,
design can foster inquiry where traditional evolutionary approaches
obstruct it. Consider the term ‘junk DNA.’ Implicit in this term is the viewthat because the genome of an organism has been cobbled together througha long, undirected evolutionary process, the genome is a patchwork ofwhich only limited portions are essential to the organism. Thus on an
evolutionary view we expect a lot of useless DNA. If, on the other hand,organisms are designed, we expect DNA, as much as possible, to exhibit
function.”
26. ENCODE Project Consortium, “Identification and Analysis of Functional
Elements.”

27. Von Sternberg and Shapiro, “How Repeated Retroelements Format
Genome Function.”
28. Han, Szak, and Boeke, “Transcriptional Disruption by the L1
Retrotransposon”; Bethany Janowski, et al., “Inhibiting Gene Expression at
Transcription Start Sites”; Goodrich and Kugel, “Non-coding-RNA
Regulators of RNA Polymerase II Transcription”; Li, et al., “Small dsRNAs
Induce Transcriptional Activation in Human Cells”; Pagano, et al., “New
Small Nuclear RNA Gene-like Transcriptional Units”; Van de Lagemaat, etal., “Transposable Elements in Mammals”; Donnelly, Hawkins, and Moss,“A Conserved Nuclear Element”; Dunn, Medstrand, and Mager, “AnEndogenous Retroviral Long Terminal Repeat”; Burgess-Beusse, et al.,“The Insulation of Genes”; Medstrand, Landry, and Mager, “Long Terminal
Repeats Are Used as Alternative Promoters”; Mariño-Ramírez, et al.,“Transposable Elements Donate Lineage-Specific Regulatory Sequences to
Host Genomes.”
29. Green, “The Role of Translocation and Selection”; Figueiredo, et al., “A
Central Role for Plasmodium Falciparum Subtelomeric Regions.”
30. Henikoff, Ahmad, and Malik, “The Centromere Paradox”; Bell, West, andFelsenfeld, “Insulators and Boundaries”; Pardue and DeBaryshe,“Drosophila Telomeres”; Henikoff, “Heterochromatin Function in Complex
Genomes”; Figueiredo, et al., “A Central Role for Plasmodium
Falciparum”; Schueler, et al., “Genomic and Genetic Definition of a
Functional Human Centromere.”
31. Jordan, et al., “Origin of a Substantial Fraction”; Henikoff, Ahmad, andMalik, “The Centromere Paradox”; Schueler, et al., “Genomic and Genetic
Definition of a Functional Human Centromere.”
32. Chen, DeCerbo, and Carmichael, “Alu Element-Mediated Gene
Silencing”; Jurka, “Evolutionary Impact of Human Alu Repetitive
Elements.”; Lev-Maor, et al., “The Birth of an Alternatively Spliced Exon”;
Kondo-Iida, et al., “Novel Mutations and Genotype–Phenotype
Relationships”; Mattick and Makunin, “Non-coding RNA.”
33. McKenzie and Brennan, “The Two Small Introns of the Drosophila
Affinidisjuncta Adh Gene”; Arnaud, et al., “SINE Retroposons Can Be
Used In Vivo”; Rubin, Kimura, and Schmid, “Selective Stimulation ofTranslational Expression”; Bartel, “MicroRNAs”; Mattick and Makunin,“Small Regulatory RNAs in Mammals.”
34. Dunlap, et al., “Endogenous Retroviruses”; Hyslop, et al.,“Downregulation of NANOG Induces Differentiation”; Peaston, et al.,“Retrotransposons Regulate Host Genes.”
35. Morrish, et al., “DNA Repair Mediated”; Tremblay, Jasin, and Chartrand,“A Double-Strand Break in a Chromosomal LINE Element”; Grawunder, etal., “Activity of DNA Ligase IV”; Wilson, Grawunder, and Liebe, “Yeast

DNA Ligase IV.”
36. Mura, et al., “Late Viral Interference Induced”; Kandouz, et al.,“Connexin43 Pseudogene Is Expressed.”
37. Goh, et al., “A Newly Discovered Human Alpha Globin Gene”; Kandouz,
et al., “Connexin43 Pseudogene Is Expressed”; Tam, et al., “PseudogeneDerived Small Interfering RNAs”; Watanabe, et al., “Endogenous siRNAs
from Naturally Formed dsRNAs”; Piehler, et al., “The Human ABC
Transporter Pseudogene Family.”
38. Mattick and Gagen, “The Evolution of Controlled Multitasked Gene
Networks”; Von Sternberg and Shapiro, “How Repeated Retroelements
Format Genome Function.”
39. In 1994, pro-ID scientist and writer Forrest M. Mims III submitted a letterto the journal Science (which was rejected) predicting function for junk
DNA: “DNA that molecular biologists refer to as ‘junk’ don’t necessarily
appear so useless to those of us who have designed and written code for
digital controllers. They have always reminded me of strings of NOP (NoOPeration) instructions. A do-nothing string of NOPs might appear as ‘junkcode’ to the uninitiated, but, when inserted in a program loop, a string of
NOPs can be used to achieve a precise time delay. Perhaps the ‘junk DNA’puzzle would be solved more rapidly if a few more computer scientists
would make the switch to molecular biology” (“Rejected Publications”).
See also Dembski’s prediction already cited.
In 2004, Jonathan Wells argued that the theory of intelligent design
provides a fruitful heuristic (guide to discovery) for genomic researchprecisely because it predicts that noncoding DNA should have latentfunction. As he explained: “The fact that ‘junk DNA’ is not junk has
emerged not because of evolutionary theory but in spite of it. On the other
hand, people asking research questions in an ID framework would
presumably have been looking for the functions of non-coding regions of
DNA all along, and we might now know considerably more about them”(“Using Intelligent Design Theory to Guide Scientific Research”).
Other scientists have noted how materialistic evolutionary theories have
impeded scientific progress in the study of the genome. In 2002, Richard
von Sternberg reported extensive evidence for functional junk-DNA, notingthat “neo-Darwinian ‘narratives’ have been the primary obstacle toelucidating the effects of these enigmatic components of chromosomes”
and concluding that “the selfish DNA narrative and allied frameworks mustjoin the other ‘icons’ of neo-Darwinian evolutionary theory that, despite
their variance with empirical evidence, nevertheless persist in theliterature” (“On the Roles of Repetitive DNA Elements”).
40. Though historical scientists focus primarily on questions about the past,
they clearly also have a secondary interest in questions about the presentoperation and cause-and-effect structure of the world. Indeed, theuniformitarian method requires that historical scientists use knowledge ofthe present cause-and-effect structure of the world to reconstruct what
happened in the past. Nevertheless, that the historical sciences addressquestions about the past at all distinguishes them from many sciences that
focus wholly on questions about how nature generally operates.
41. Gould, “Evolution and the Triumph of Homology,” 61.
42. Meyer, “DNA and the Origin of Life”; Meyer, et al., “The Cambrian
Explosion”; Behe, Darwin’s Black Box; Gonzalez and Richards, The
Privileged Planet; Craig, “Barrow and Tipler on the Anthropic Principle vs.
Divine Design,” 389.
43. This is not to deny that laws or process theories may play roles in supportof causal explanation, as even opponents of the covering-law model, suchas Scriven, admit. Scriven notes that laws and other types of generalprocess theories may play an important role in justifying the causal statusof an explanatory antecedent and may provide the means of inferring
plausible causal antecedents from observed consequents. Nevertheless, asboth Scriven and I have argued elsewhere, laws are not necessary to the
explanation of particular events or facts; and even when laws are present,
antecedent events function as the primary causal or explanatory entity inhistorical explanations. See Scriven, “Truisms as the Grounds,” 448–50;“Explanation and Prediction,” 480; “Causes, Connections and Conditions,”
249–50; Meyer, “Of Clues and Causes,” 18–24, 36–72, 84–92.
44. Dawkins, The Blind Watchmaker, 1.
45. Ayala, “Darwin’s Revolution.”
46. Nagel, “Public Education and Intelligent Design.”
47. For example, Judge Jones asserted in his decision: “We find that ID is notscience and cannot be adjudged a valid, accepted scientific theory as it has
failed to publish in peer-reviewed journals” (Kitzmiller et al. v. Dover Area
School District, 400 F.Supp.2d).
48. Forrest, quoted in Vergano and Toppo, “‘Call to Arms’ on Evolution.”
49. Forrest, Expert Witness Report.
50. For example, Judge Jones asserted: (1) “We find that ID is not science andcannot be adjudged a valid, accepted scientific theory as it has failed to
publish in peer-reviewed journals, engage in research and testing, and gain
acceptance in the scientific community”; (2) “A final indicator of how ID
has failed to demonstrate scientific warrant is the complete absence of peerreviewed publications supporting the theory”; and (3) “The evidence
presented in this case demonstrates that ID is not supported by any peerreviewed research, data or publications” (Kitzmiller et al. v. Dover Area
School District, 400 F.Supp.2d).
51. Holden, “Random Samples”; Giles, “Peer-Reviewed Paper Defends

Theory of Intelligent Design”; Klinghoffer, “The Branding of a Heretic”;
Powell, “Editor Explains Reasons for ‘Intelligent Design’ Article.”
52. See Discovery Institute, Brief of Amicus Curiae (Revised).
53. E.g., Behe, Darwin’s Black Box; Gonzalez and Richards, The Privileged
Planet; Thaxton, Bradley, and Olsen, The Mystery of Life’s Origin.
54. E.g., Dembski, The Design Inference; Campbell and Meyer, eds.,
Darwinism, Design and Public Education; Dembski and Ruse, eds.,
Debating Design.
55. See the five articles advancing the case for the theory of intelligent design
published in Campbell and Meyer, eds., Darwinism, Design and Public
Education, and the four articles published in Dembski and Ruse, eds.,
Debating Design.
56. Minnich and Meyer, “Genetic Analysis of Coordinate Flagellar and Type
III Regulatory Circuits”; Dembski, ed., Mere Creation.
57. Craig, “God, Creation and Mr. Davies,” 163; “Barrow and Tipler on the
Anthropic Principle vs. Divine Design”; Behe, “Self-Organization and
Irreducibly Complex Systems.”
58. Wells, “Do Centrioles Generate a Polar Ejection Force?”; Behe andSnoke, “Simulating Evolution by Gene Duplication”; Dembski and Marks,“The Conservation of Information: Measuring the Information Cost of a
Successful Search”; Voie, “Biological Function and the Genetic Code Are
Interdependent”; Davison, “A Prescribed Evolutionary Hypothesis.” Seealso Lönnig and Saedler, “Chromosomal Rearrangements and Transposable
Elements.”
59. In 2005 my colleagues and I at the Discovery Institute actually opposed
the policy of the Dover school board and urged the school board towithdraw it. Though we think there is nothing unconstitutional aboutteaching students about the theory of intelligent design, we feared thatpoliticizing the issue would result in reprisals against ID proponents inuniversity science departments.
We also objected to the way some members of the Dover board
attempted to justify their policy by invoking religious authority as a reason
to vote for the policy. This justification guaranteed the policy would run
afoul the establishment clause in the courts. From our point of view, thisjustification was entirely gratuitous—and incongruous—since the theory ofintelligent design is not based upon a religious authority, but uponempirical evidence and standard scientific methods of reasoning.
Unfortunately, the school board did not heed our advice. It lost the case,just as we predicted, ultimately causing trouble for ID proponents atuniversities around the country, just as we, alas, also predicted.
60. “The Council [of the Biological Society of Washington, which oversees
the publication of the Proceedings of the Biological Society of Washington]

endorses a resolution on ID published by the American Association for the
Advancement of Science(www.aaas.org/news/releases/2002/1106id2.shtml), which observes that
there is no credible scientific evidence supporting ID as a testable
hypothesis to explain the origin of organic diversity. Accordingly, theMeyer paper does not meet the scientific standards of the Proceedings.” See
http://www.ncseweb.org/resources/news/2004/US/294_bsw_strengthens_statement_repu
61. These charges were clearly misplaced. The president of the council thatoversaw the publication of the Proceedings admitted to the editor in an email: “I have seen the review file and comments from 3 reviewers on the
Meyer paper. All three with some differences among the comments
recommended or suggested publication. I was surprised but concluded that
there was not inappropriate behavior vs a vis [sic] the review process” (RoyMcDiarmid, “Re: Request for information,” January 28, 2005, 2:25 p.m., toHans Sues, Congressional Staff Report, “Intolerance and the Politicizationof Science at the Smithsonian” [December 2006]: 26, at
http://www.discovery.org/scripts/viewDB/filesDB-download.php?
command=download&id=1489). See also Rick Sternberg, “Statement of
Facts/Response to Misinformation,” at http://www.richardsternberg.
org/smithsonian.php?page=statement.
62. As Stephen Jay Gould wrote with other scientists and historians of sciencein a brief to the U.S. Supreme Court in 1993: “Judgments based on
scientific evidence, whether made in a laboratory or a courtroom, areundermined by a categorical refusal even to consider research or views that
contradict someone’s notion of the prevailing ‘consensus’ of scientificopinion…. Automatically rejecting dissenting views that challenge theconventional wisdom is a dangerous fallacy, for almost every generally
accepted view was once deemed eccentric or heretical. Perpetuating thereign of a supposed scientific orthodoxy in this way, whether in a researchlaboratory or in a courtroom, is profoundly inimical to the search fortruth…. The quality of a scientific approach or opinion depends on thestrength of its factual premises and on the depth and consistency of its
reasoning, not on its appearance in a particular journal or on its popularity
among other scientists.” Brief Amici Curiae of Physicians, Scientists, andHistorians of Science in Support of Petitioners, Daubert v. Merrell Dow
Pharmaceuticals, Inc., 509 U.S. 579 (1993).
63. For a comprehensive annotated bibliography of ID publications, see
http://discovery.org/a/2640. Some critics of design have attempted todiscredit the theory because its advocates have published their caseprimarily in books rather than in scientific articles. But as I noted in
Chapter 6 this argument ignores the important role that books have playedin the history of science in establishing new scientific ideas. Anyone whounderstands the role that technical journals play in science will understand
why this is so. Science journals are a highly specialized and conservative
genre. They publish research designed to fill out an established scientificresearch program. They are part of what philosopher of science Thomas
Kuhn calls “normal science” (The Structure of Scientific Revolutions). New
and revolutionary ideas in science are unlikely to appear first in their pages.
If the history of science is any indication, then we should expect most of
the initial work in any fundamentally new scientific perspective to appear
first in books. And this is precisely the pattern of publication we see in thecase of intelligent design. In the last decade or so, new evidence-based
arguments for the theory have made their initial appearance in books. Morerecently, scientific articles have begun to appear, elucidating the theory inmore detail.
Chapter 19: Sauce for the Goose
1. McLean v. Arkansas Board of Education, 529 F.Supp. 1255 (E.D. Ark.
1982).
2. For the purposes of litigation the court defined creation science as a belief
system that affirms: “(1) [The] sudden creation of the universe, energy, andlife from nothing; (2) the insufficiency of mutation and natural selection inbringing about development of all living kinds from a single organism; (3)changes only within fixed limits of originally created kinds of plants andanimals; (4) separate ancestry for man and apes; (5) explanation of theearth’s geology by catastrophism, including the occurrence of a worldwide
flood; and (6) a relatively recent inception of the earth and living kinds”(McLean v. Arkansas Board of Education, 529 F.Supp. 1255, 1264
[E.D.Ark.1982]).
3. McLean v. Arkansas Board of Education, 529 F.Supp. 1255, 1267
(E.D.Ark. 1982).
4. Ruse, “Darwinism,” 21.
5. Ruse, “Darwinism,” 23.
6. Ruse, “A Philosopher’s Day in Court”; “Witness Testimony Sheet,” 301;
“Darwinism,” 21–26.
7. Meyer, “Laws, Causes and Facts.”
8. A law of nature typically describes a general relationship between two or
more different types of events, entities, or properties. Many have the form,“If A occurs, then B will always follow under conditions C,” such as thelaws, “Pure water at sea level heated to 100 degrees C will boil” and “Allunsuspended bodies will fall.” Other laws describe mathematical
relationships between different entities or properties that apply universally,such as the law, “Force equals mass times acceleration,” or the law, “Thepressure of a gas is proportional to its volume times its temperature.” In anycase, laws are not events; they describe relationships (causal, logical, or
mathematical) between different types of events, entities, or properties.
9. Scriven, “Causation as Explanation,” 14; Lipton, Inference to the Best
Explanation, 47–81.
10. In the On the Origin of Species, Darwin proposed both a mechanism(natural selection) and a historical theory—the theory of universal common
descent. Evolutionary biologists debate whether natural selection can beformulated as a law. But within Darwin’s argument, the theory of common
descent had its own explanatory power. Yet it did not explain by reference
to a law of nature. Instead, the theory of common descent explains by
postulating a hypothetical pattern of events (as depicted in Darwin’s
famous tree of life). The theory of common descent makes a claim about
what happened in the past—namely, that a series of unobserved transitionalorganisms existed, forming a genealogical bridge between presently
existing life-forms—to account for a variety of presently observed evidence(such as the similarity of anatomical structures in different organisms or thepattern of fossil progression). Darwin himself referred to common descentas the vera causa (i.e., the actual cause) of a diverse set of biologicalobservations (On the Origin of Species, 195, 481–82). And in the theory ofcommon descent, a pattern of events, not a law, does what I call the“primary explanatory work.”
11. Ruse, Darwinism Defended, 59.
12. Grinnell, “Radical Intersubjectivity”; Scott, “Keep Science Free from
Creationism.”
13. Miller, Only a Theory, 87; Skoog, “View from the Past”; Sober, “What Is
Wrong with Intelligent Design?”
14. Ruse, “Darwinism.”
15. Miller, Only a Theory, 87.
16. Kitzmiller v. Dover School District 04 cv 2688 (December 20, 2005), 22,
77; Riordan, “Stringing Physics Along,” 38.
17. Kitzmiller v. Dover School District 04 cv 2688 (December 20, 2005), 81;
Jack Krebs, “A Summary of Objections to ‘Intelligent Design,’” Kansas
Citizens for Science, June 30, 2001
(http://www.sunflower.com/~jkrebs/JCCC/04%20Summary_Objections.html).
18. Michigan Science Teachers Association, “Evolution Education and the
Nature of Science,” February 3, 2007 (http://www.mstamich.org/downloads/about/2007–02–03.doc).
19. See www.signatureinthecell.com and, specifically, “The Scientific Status
of Intelligent Design: The Methodological Equivalence of Naturalistic and
Non-Naturalistic Origins Theories” and “The Demarcation of Science andReligion.” See also Philip Kitcher’s new book, Living with Darwin, 9–14.

Kitcher is a leading philosopher of science who rejects intelligent design,
but thinks that the attempts to refute it using demarcation arguments fail.
Kitcher argues that the problem with intelligent design is not that it isn’tscientific, but that it is “discarded” or “dead science.” He argues this largely
because he thinks that intelligent design cannot explain the accumulation of“junk DNA.” He does not, however, address the empirical argument for
intelligent design based upon the information-bearing properties of DNA orthe evidence showing that nonprotein-coding DNA plays a crucial
functional role in the cell.
20. Meyer, “A Scopes Trial for the ’90s”; “Open Debate on Life’s Origin.”
21. Scott, “Keep Science Free from Creationism.”
22. Grinnell, “Radical Intersubjectivity.”
23. Meyer, “Of Clues and Causes,” 120; Darwin, On the Origin of Species,
398; Hull, Darwin and His Critics, 45.
24. For example, in 1999 the U.S. National Academy of Sciences (NAS)issued a statement against intelligent design that claimed that ID is “notscience because [it is] not testable by the methods of science.” Yet the NAS
simultaneously stated, “Molecular data counter a recent proposition called‘intelligent design theory’” and asserted that “scientists have considered the
hypotheses” of intelligent design and “rejected them because of a lack ofevidence.” National Academy of Sciences, Science and Creationism, 21, ix.
Similarly, Gerald Skoog argues that “the claim that life is the result of adesign created by an intelligent cause cannot be tested and is not within therealm of science.” Then in the next paragraph he states, “Observations of
the natural world also make these dicta [concerning the theory of intelligent
design] suspect” (“View from the Past”). Yet clearly something cannot beboth untestable in principle and made suspect by empirical observations.
25. Pennock, Expert Witness Report. Emphasis added.
26. Scott, “Keep Science Free from Creationism.”
27. Judson, The Eighth Day of Creation, 157–90.
28. Francis Darwin, ed., Life and Letters, 1:437, emphasis added.
29. Miller, Only a Theory, 87.
30. Meyer, “The Return of the God Hypothesis.”
31. Miller, Finding Darwin’s God, 238.
32. Similarly, neither type of theory describes events that will necessarily
occur repeatedly, but both use uniform and repeated experience of cause
and effect to make inferences about the most likely cause of various
singular happenings.
33. Laudan, “The Demise of the Demarcation Problem”; “Science at the Bar—Causes for Concern”; Quinn, “The Philosopher of Science as Expert
Witness.”
34. For example, in the Arkansas trial Ruse had asserted that to qualify asscientific a theory must be falsifiable. He argued that young-earth
creationism (creation science) wasn’t scientific because it wasn’t falsifiable.
But other philosophers have pointed out that young-earth creationism mademany testable claims and that these claims were nothing if not falsifiable.
As one of Ruse’s critics, Larry Laudan, points out, to claim that
“Creationism is neither falsifiable nor testable is to assert that Creationism
makes no empirical assertions whatever. That is surely false. Creationists
make a wide range of testable assertions…. Creationists say that the earth isof very recent origin…. They argue that most of the geological features ofthe earth’s surface are diluvial in character…. They assert the limited
variability of species. They are committed to the view that, since animals
and man were created at the same time, the human fossil record must be
paleontologically coextensive with the record of lower animals.” But as
Laudan argues, “no one has shown how to reconcile such claims with theavailable evidence—evidence which speaks persuasively to a long earth
history, among other things. Thus, these claims are testable, they have beentested, and they have failed those tests.” It follows from Ruse’s definition
that creation science should qualify as scientific theory. Since it had beenfalsified, it was falsifiable. Since it was falsifiable, it qualified—paradoxically—as a scientific theory. (Laudan, “Science at the Bar—
Causes for Concern.”)
Conversely, Laudan shows that many of Ruse’s criteria would also,
depending on how strictly they were applied, have the opposite effect of
disqualifying mainstream evolutionary theories. Advancing his own
criticism of Ruse’s use of the “must explain by natural law” criterion,
Laudan notes that scientists often make “existence claims” about past
events or present processes without knowing the natural laws on which they
depend. As he says, “Darwin took himself to have established the existenceof [the mechanism of] natural selection almost a half century before
geneticists were able to lay out the laws of heredity on which natural
selection depended.” Thus, Ruse’s second demarcation criterion would
require, if applied consistently, classifying classical Darwinism (as well as
much of neo-Darwinism) as unscientific. As Laudan notes, if we took
Ruse’s second criterion seriously “we should have to say that…Darwin[was] unscientific; and, to take an example from our own time, it would
follow that plate tectonics is unscientific because we have not yet identified
the laws of physics and chemistry which account for the dynamics of
crustal motion.”
35. Laudan, “The Demise of the Demarcation Problem,” 349; “Science at the
Bar—Causes for Concern”; Quinn, “The Philosopher of Science as Expert
Witness”; Kitcher, Abusing Science, 125–27. Kitcher allows for the
possibility of a testable theory of divine creation. He argues that thepresence of unobservable elements in theories, even ones involving anunobservable Divine Creator, do not mean that such theories cannot be
evaluated empirically. He writes: “Even postulating an unobserved Creatorneed be no more unscientific than postulating unobserved particles. Whatmatters is the character of the proposals and the ways in which they arearticulated and defended.” Nevertheless, Kitcher argues creationism wastested and found wanting in the nineteenth century.
36. Speech at Annual Meeting of the American Association for the
Advancement of Science.
37. Ruse, Monad to Man, 511–17.
38. Eger, quoted in Buell, “Broaden Science Curriculum,” A21. Larry Laudan
also categorically rejects the efficacy of demarcation arguments. As hesummarizes, “If we could stand up on the side of reason, we ought to drop
terms like ‘pseudoscience’…They do only emotive work for us” (“TheDemise of the Demarcation Problem”).
39. Jones, Kitzmiller et al. v. Dover Area School Board. As Judge Jones
explained, “This self-imposed convention of science, which limits inquiry
to testable, natural explanations about the natural world, is referred to byphilosophers as ‘methodological naturalism.’”
40. Murphy, “Phillip Johnson on Trial,”
33. Nancey Murphy is a philosopher and seminary professor who strongly
affirms methodological naturalism. Here’s what she says in full: “Sciencequa science seeks naturalistic explanations for all natural processes.
Christians and atheists alike must pursue scientific questions in our era
without invoking a Creator…. Anyone who attributes the characteristics ofliving things to creative intelligence has by definition stepped into the arenaof either metaphysics or theology.”
41. Some might object to my description of methodological naturalism as aprinciple that excludes all intelligent causes from science. They could point
out, correctly, that some scholars construe the principle of methodologicalnaturalism (MN) as forbidding, invoking only supernatural intelligent
causes, not intelligent causes in general, within science. Nevertheless,nothing follows from this objection. Interpreting the principle of MN in thismore limited way doesn’t justify disqualifying intelligent design fromconsideration as a scientific theory. If methodological naturalism merely
forbids reference to supernatural causes in science, then the theory ofintelligent design should qualify as a scientific theory. Why? The theory
itself claims to do no more than establish an intelligent cause, not asupernatural intelligent cause, as the best explanation for the origin of
biological information.
Clearly, some advocates of the theory of intelligent design think (as I do)that the designing intelligence responsible for life is most likely to be asupernatural deity. But again, not much follows from that about thescientific status of the theory itself.
Some advocates of the theory of intelligent design do indeed think thatthe theory has theistic implications. So what? Even if one concedes adefinition of science that forbids reference to supernatural entities, all that
follows is that the putative implications of intelligent design, not the claimsof the theory itself, are unscientific. Some scientists may even offer
arguments in support of their contention that the designing intelligence
responsible for life is most likely to be a supernatural deity. A definition ofscience that forbids reference to supernatural entities would, in that case,require classifying those arguments as unscientific. Nevertheless,classifying such arguments as unscientific or metaphysical or religious
would not necessarily refute them.
42. Some scientists attempt to justify methodological naturalism by using
another demarcation argument against intelligent design: ID does not cite amechanism as the cause of the origin of biological form or information. But
this demarcation argument assumes without justification that allscientifically acceptable causes are mechanistic causes. To insist that all
causal explanations in science must be mechanistic is to insist that all
causal theories must refer only to material entities. A “mechanism” is, after
all, just another word for a material cause. Yet this requirement is merely
another expression of the principle of methodological naturalism, for which
there is no noncircular justification.
As I have argued throughout this chapter, scientists have tried to justify acategorical exclusion of intelligent causes from science (i.e.,
methodological naturalism) by reference to specific demarcation criteriasuch as “testability,” “observability,” or “must explain by natural law.” But,
as I have shown, these arguments have failed to justify the exclusion of
intelligent causes from science and, thus, the principle of methodologicalnaturalism as a rule for science.
The failure of demarcation arguments against intelligent design (or infavor of methodological naturalism) has left scientists without any
justification for treating methodological naturalism as a normative rule ofscientific practice. Attempts to justify this convention simply restate it inanother form: “Scientific theories must cite mechanisms.” Thus, it provides
no grounds for excluding consideration of intelligent causes in scientifictheories, even if the intelligence in question is ultimately immaterial.
This demarcation argument against intelligent design clearly assumes thepoint at issue, which is whether there are independent and metaphysicallyneutral reasons for preferring exclusively materialistic causal explanationsof origins over explanations that invoke entities such as creative
intelligence, conscious agency, mental action, intelligent design or mind—

entities that may not ultimately be reducible to matter alone. Since
demarcation arguments have failed to provide such reasons, and since weknow from first-person experience that our choices and actions as
conscious intelligent agents do cause certain kinds of events, structures, andsystems to arise—that minds have real causal powers—there does not seemto be any reason for prohibiting scientists from considering this type ofcause as a possible explanation for certain kinds of effects. Instead, there is
every reason to consider intelligent causes as explanations for effects thatare known to arise only from intelligent activity.
43. Some have argued that the theory of intelligent design is unscientificbecause it doesn’t cite a mechanism to explain how the designing
intelligence responsible for life arranged the constituent parts of life. But
this is also true in our own experience. We do not know how our minds
influence the material substrate of our brains, the actions of our bodies, or
through them the material world around us. Nevertheless, we have goodreason to think that our conscious thoughts and decisions do influence thematerial world. Moreover, we can often know or infer that intelligent
thought played a role in the arrangement of matter or outcome of events
without knowing exactly how mind influences matter.
It’s hard to see how this limitation in our understanding makes the theoryof intelligent design unscientific. Many scientific theories do not explain
events, evidence, or phenomena by reference to any cause whatsoever, letalone a mechanistic one. Newton’s universal law of gravitation was no lessa scientific theory because Newton failed—indeed, refused—to postulate amechanism or cause for the regular pattern of attraction his law describes.
In addition, many historical theories about what happened in the past
stand on their own without any mechanistic or causal theory about how theevents to which such theories attest could have occurred. The theory of
universal common descent is generally regarded as a scientific theory eventhough some scientists do not think there is currently an adequate
mechanism to explain how transmutation between lines of descent wasachieved. In the same way, there seems little justification for asserting thatthe geological theory of continental drift became scientific only after theadvent of plate tectonics. Although the mechanism provided by plate
tectonics certainly helped render continental drift a more persuasive theory,
it was nevertheless not strictly necessary to know the mechanism by which
continental drift occurs (1) to know or theorize that drift had occurred or(2) to regard a theory of continental drift as scientific.
In a similar way, advocates of design can affirm and detect that
intelligence played a causal role in the design of life without knowing
exactly how mind exerts its influence over matter. All that follows from thisadmission is that intelligent design does not provide an answer to every

question, not that it is an unscientific (or unjustified) answer to the questionthat it does answer.
44. Jones, Kitzmiller et al. v. Dover Area School Board.
45. There are many types of scientific inquiry in which the convention of
methodological naturalism does no damage to the intellectual freedom ofscientists. Consider a scientist investigating the question, “How does
atmospheric pressure affect crystal growth?” The answer, “Crystals were
designed by a creative intelligence” (or, for that matter, “Crystals evolved
via undirected natural processes”) entirely misses the point of the question.
The question demands an answer expressed as a general relationshipdescribing the interaction of two physical entities: gases and crystals. A
scientific law expressing such a relationship necessarily describes entirely
materialistic entities.
Methodological naturalism does not limit the freedom of scientists totheorize in this case. Instead, the question motivating their inquiry ensuresthat scientists will only consider certain kinds of answers and that theseanswers will necessarily describe materialistic entities. In this case,methodological naturalism does no harm, though neither is it necessary to
guide scientists to appropriate theories. Instead, the implicit question
motivating the inquiry will do this on its own.
Methodolocal naturalism and its prohibition against inferring creative
intelligence does inhibit inquiry in historical sciences such as archaeology,
forensics, paleobiology, cosmology, anthropology, and origin-of-life
studies, however, because in the historical sciences researchers are
addressing different kinds of questions. They are asking about the causal
histories of particular events, events in which the purposeful design ofintelligent agents might have played a role. Scientists investigating theorigin of life, for example, are motivated by the question, “What happened
to cause life to arise on earth?” Since, conceivably, an intelligent designer
could have played a causal role in the origin of life, any rule that prevents
scientists from considering that possibility prevents scientists fromconsidering a possibly true hypothesis.
46. Bridgman, Reflections of a Physicist, 535.
Chapter 20: Why It Matters
1. This quotation is from a court case in which the 3rd Circuit Court ofAppeals defined religion for legal purposes. See Africa v. Pennsylvania,
662 F.2d 1025, 1035–36 (3d Cir. 1981), cert. denied, 456 U.S. 908 (1982).
See also Alvarado v. City of San Jose, 94 F.3d 1223, 1227 (9th Cir. 1996).
For a more extensive analysis of whether intelligent design qualifies as anestablishment or religion, see DeWolf, Meyer, and DeForrest, “Teaching
the Origins Controversy.”

2. See, e.g., Center for Science and Culture, Discovery Institute,
http://www.discovery. org/csc. See also the Biologic Institute at
http://www.biologicinstitute.org.
3. Skoog, “A View from the Past”; Pennock, Expert Witness Report.
4. Michael Denton, an agnostic, argues for intelligent design in Evolution: A
Theory in Crisis, 326–43. Antony Flew, a longtime champion of atheism,
recently announced his abandonment of atheism based on the evidence ofintelligent design, but emphasized that his religion was far fromconventional (much less sectarian): “I’m thinking of a God very different
from the God of the Christian and far and away from the God of Islam,because both are depicted as omnipotent Oriental despots, cosmic Saddam
Husseins” (“Famous Atheist Now Believes in God,”
http://abcnews.go.com/US/wireStory?id=315976). See also Flew, There Is
a God.
5. Logically, a statement or proposition (call it A) implies another proposition(call it B), if B is more plausible or more likely to be true if A is true.
Logically, a proposition (call it A) entails another proposition (call it B), if
B is necessarily true if A is true. Implication refers to any kind of epistemic
support short of deductive proof, whether that support is provided by weakforms of abductive inference, induction, confirmation of hypothesis,
Bayesian inference, or a strong abductive inference to the best explanation.
6. Similarly, archaeology often yields data or artifacts that may prove
supportive or subversive of particular religious traditions. Yet the
implications of archaeological theories do not make archaeology a religion
or its theories necessarily false (or true).
7. Hawking and Penrose, “The Singularities of Gravitational Collapse andCosmology.”
8. Eddington, “The End of the World.”
9. Kenyon and Steinman, Biochemical Predestination, 6.
10. Dawkins, The Blind Watchmaker, 6.
11. Dawkins, The Blind Watchmaker, 6.
12. Simpson, The Meaning of Evolution, 344, emphasis added.
13. Futuyma, Evolutionary Biology, 5.
14. “The implications of modern evolutionary biology are inescapable….
Evolutionary biology undermines the fundamental assumptions underlying
ethical systems in almost all cultures, Western civilization in particular”(Provine, “Evolution and the Foundation of Ethics”).
15. Gould, Ever Since Darwin, 33.
16. Dawkins, The Blind Watchmaker, 6.
17. Dawkins, The Blind Watchmaker, 6.
18. Gould, Ever Since Darwin, 33, 147, 267.
19. Miller and Levine, Biology, 658. A later edition of the textbook deletedthis language.
20. Purvis, Orians, and Heller, Life, 14.
21. Futuyma, Evolutionary Biology, 5.
22. “In many respects, evolution is the key to understanding our relationshipwith God” (Miller, Finding Darwin’s God, 291).
23. Dawkins, The Blind Watchmaker, 6.
24. Miller, Finding Darwin’s God, 291.
25. Russell, Mysticism and Logic, Including a Free Man’s Worship, 10–11.
Epilogue: A Living Science
1. Kitcher, Living with Darwin, 11.
2. Newton, Newton’s Principia.
3. Carninci, Yasuda, and Hayashizaki, “Multifaceted MammalianTranscriptome”; Gerstein, Bruce, Rozowsky, et al. “What Is a Gene, PostENCODE?”
4. Amaral and Mattick, “Noncoding RNA in Development.”
5. Squires and Berry, “Eukaryotic Selenoprotein Synthesis.”
6. Pesole, “What Is a Gene?”; Gerstein, Bruce, Rozowsky, et al. “What Is aGene, Post-ENCODE?”
7. Savarese and Grosschedl, “Blurring Cis and Trans in Gene Regulation.”
8. Kapranov, Willingham, and Gingeras, “Genome-wide Transcription and the
Implications for Genomic Organization.”
9. Sowden, Ballatori, Jensen, Reed, and Smith, “The Editosome for Cytidine
to Uridine mRNA Editing has a Native Complexity of 27S”; Sperling,
Azubel, and Sperling, “Structure and Function of the Pre-mRNA Splicing
Machine.”10. Fischer, Butler, Pan, and Ruvkun, “Trans-splicing in C. elegans Generates
the Negative RNAi Regulator ERI–6/7.”
11. Hudson and Goldstein, “The Gene Structure of the Drosophilamelanogaster Proto-Oncogene, Kayak, and Its Nested Gene, fos-Intronic
Gene.”
12. Huang, Zhou, He, Chen, Liang, and Qu, “Genome-wide Analyses of Two
Families of snoRNA Genes from Drosophila melanogaster”; Saini, Enright,
and Griffiths-Jones, “Annotation of Mammalian Primary microRNAs.”
13. Chung, et al., “A First Look at the ARFome.”
14. Ohno, “So Much ‘Junk’ DNA in Our Genome”; Dawkins, The Selfish
Gene; Orgel, Crick, Sapienza, “Selfish DNA”; Orgel and Crick, “Selfish
DNA.”
15. Shapiro and von Sternberg, “Why Repetitive DNA Is Essential to Genome
Function.”
16. Denoeud, et al., “Prominent Use of Distal 5^ Transcription Start Sites.”
17. Michalak, “Coexpression, Coregulation, and Cofunctionality ofNeighboring Genes in Eukaryotic Genomes.”
18. Miles, et al., “Intergenic Transcription, Cell-Cycle and theDevelopmentally Regulated Epigenetic Profile of the Human Beta-Globin
Locus.”
19. Gierman, et al., “Domain-wide Regulation of Gene Expression in the
Human Genome”; Goetze, et al., “The Three-Dimensional Structure of
Human Interphase Chromosomes.”
20. Miller, Only a Theory, 37, 56.
21. Kapranov, Willingham, and Gingeras, “Genome-wide Transcription andthe Implications for Genomic Organization”; Wu, et al., “Systematic
Analysis of Transcribed Loci in ENCODE Regions.”
22. Costantini, et al., “Human Chromosomal Bands.”
23. Carvalho, et al., “Chromosomal G-dark Bands Determine the Spatial
Organization of Centromeric Heterochromatin in the Nucleus.”
24. Lynch writes that he is striving for “a general theory of the evolution of
the gene that incorporates the universal properties of random genetic drift
and mutation pressure” and that “at this point it is difficult to reject the
hypothesis that the basic embellishments of the eukaryotic gene originatedlargely as a consequence of nonadaptive processes operating contrary to the
expected direction of natural selection” (“Origins of Eukaryotic Gene
Structure,” 464).
25. According to Gehring, “…the mouse gene [Pax–6] was also shown to becapable of inducing eyes [in Drosophila]” (Master Control Genes in
Development and Evolution, 204).
26. Panganiban and Rubenstein, “Developmental Functions of the Distalless/Dlx Homeobox Genes.”
27. Newman, “The Developmental Genetic Toolkit and the Molecular
Homology-Analogy Paradox.”
28. Carroll, Endless Forms Most Beautiful: The New Science of Evo Devo.
“The discovery that the same sets of genes control the formation andpattern of body regions and body parts with similar functions (but very
different designs) in insects, vertebrates, and other animals has forced acomplete rethinking of animal history, the origins of structures, and thenature of diversity. Comparative and evolutionary biologists had longassumed that different groups of animals, separated by vast amounts ofevolutionary time, were constructed and had evolved by entirely different
means…. So prevalent was this view of great evolutionary distance that in
the 1960s the evolutionary biologist (and an architect of the Modern
Synthesis) Ernst Mayr remarked: ‘Much that has been learned about gene
physiology makes it evident that the search for homologous genes is quite
futile except in very close relatives….’ This view was entirely incorrect.
The late Stephen Jay Gould, in his monumental work The Structure ofEvolutionary Theory, saw the discovery of Hox clusters and common bodybuilding genes as overturning a major view of the Modern Synthesis” (71–72). See also Ron Amundson, “Development and Evolution,” in Sarkar andPlutynski, eds., A Companion to the Philosophy of Biology, 248–68; esp.
264: “By the early 1990s molecular geneticists were beginning to identify
genes on the basis of their molecular composition. The first shock was thediscovery that certain genes were widely shared among nearly all animalgroups, from mammals to insects to flatworms. The mere fact of widely
shared genes was inconsistent with the expectations of major adaptationists.
But the nature of those genes was even more surprising. They acted at the
deepest and earliest stages of embryonic development…Adaptationists like
Dobzhansky and Mayr had predicted just the opposite—the absence of any
important homologous genes.”
29. “Insect and vertebrate legs,” note Erwin and Davidson, “are constructedentirely differently and it is difficult to imagine a morphogenetic processthat would produce a version ancestral to both, i.e., beyond the initialpatterning stage.” Erwin and Davidson, “The Last Common Bilaterian
Ancestor.”
30. Erwin and Davidson, “The Last Common Bilaterian Ancestor.”
31. Nelson and Wells, “Homology in Biology,” esp. 317.
32. Goodwin, “What Are the Causes of Morphogenesis?”; Nijhout,“Metaphors and the Role of Genes in Development”; Sapp, Beyond theGene; Müller and Newman, “Origination of Organismal Form.”
33. Harold, “From Morphogenes to Morphogenesis”; Moss, What Genes
Can’t Do.
34. Harold, The Way of the Cell, 125.
35. Harold, The Way of the Cell, 125.
36. McNiven and Porter, “The Centrosome.”
37. Lange, Faragher, March, and Gull, “Centriole Duplication and Maturationin Animal Cells”; Marshall and Rosenbaum, “Are There Nucleic Acids in
the Centrosome?”
38. Lange, Faragher, March, and Gull, “Centriole Duplication and Maturationin Animal Cells.”
39. Sonneborn, “Determination, Development, and Inheritance of theStructure of the Cell Cortex”; Frankel, “Propagation of Cortical Differencesin Tetrahymena”; Nanney, “The Ciliates and the Cytoplasm.”
40. Moss, What Genes Can’t Do.
41. Harold, “From Morphogenes to Morphogenesis.”42. On his Michigan State University Web page, philosopher of biology
Robert Pennock notes that his major 2003 Nature paper using the program
Avida was intended to rebut Michael Behe’s arguments about the origin of
irreducibly complex systems. “Though not discussed explicitly in thepaper,” he writes, “the experiment refutes Behe’s claim that evolution
cannot produce ‘irreducible complexity.’” This claim occurs underPennock’s Web-page heading “Rebutting Michael Behe and Irreducible
Complexity.” See https://www.msu.edu (last accessed February 9, 2009).
See also Thornton, “Implications for Intelligent Design,” and Minnich andMeyer, “Genetic Analysis of Coordinate Flagellar and Type III Regulatory
Circuits in Pathogenetic Bacteria.”
Appendix A: Some Predictions of Intelligent Design
1. Dembski and Marks, “The Conservation of Information”; “The Search for a
Search.”2. Shapiro, “A 21st Century View of Evolution,” 124.
3. Wells, “Do Centrioles Generate a Polar Ejection Force?”; “Using
Intelligent Design Theory to Guide Scientific Research”; “A Possible Link
Between Centrioles, Calcium Deficiency and Cancer.”
4. Meyer, Ross, Nelson, and Chien, “The Cambrian Explosion.”
5. Ayoub, “On the Design of the Vertebrate Retina”; Denton, “Selected
Excerpts: The Inverted Retina.”
6. It might be objected that a beneficent designer skilled enough to produce
biological systems would not allow natural processes to degrade them. But
this objection inevitably raises a theological question about what a theistic
designer would or would not have done, one moreover addressed bytraditional theological formulations. For more on the subject see Hunter,
Darwin’s God; Wiker and Witt, A Meaningful World.
7. Minnich and Rohde, “A Rationale for Repression and/or Loss of Motility
by Pathogenic Yersinia in the Mammalian Host.”
8. Chain, et al., “Insights into the Evolution of Yersinia pestis ThroughWhole-Genome Comparison with Yersinia pseudotuberculosis.”
9. Miller, “The Flagellum Unspun.”
10. As microbiologist Milton H. Saier Jr. has noted: “In considering thelogistics of the conclusions made by Gophna et al. [10], it should be notedthat Fla[gellar] systems have been able to diverge in structure so as to spaneither one membrane or two in the envelopes of Gram-positive and Gramnegative bacteria, respectively. So why haven’t T3SSs? The most plausible
explanation considers that T3SSs arose late from preexisting Gramnegative bacterial Fla[gellar] systems” (“Evolution of Bacterial Type III
Protein Secretion Systems”). Additionally, as Saier observes, mutationdensity studies have shown that the T3SS and flagellar motor genes haveroughly equivalent ages. Saier regards these studies as less definitive thanphylogenetic studies because of their assumption of a constant rate ofevolutionary change, i.e., that mutation rates function as a consistent“molecular clock.” But even if these studies yield accurate conclusions,they are consistent with the possibility of independent design or with anextremely rapid devolution of the T3SS from the flagellar motor, both ofwhich are possibilities that would challenge the co-option hypothesis. Thisis the case, because it would presumably take longer to build a new systemthan to offload parts of an old one. Saier himself has recently changed his
mind about his 2004 claim that the flagellar motor is older than the T3SS.
He now favors the view that both the T3SS and the flagellar motor share acommon ancestor. Nevertheless, he offers little empirical justification forthis new view.
11. Axe, “Estimating the Prevalence of Protein Sequences Adopting
Functional Enzyme Folds.”
Appendix B: Multiverse Cosmology and the Origin of Life
1. Koonin, “The Cosmological Model.”2. Calculations predicated on the standard big-bang cosmology tell us thatradiation arriving from opposite directions in the sky would have originallybeen separated, when the universe was 300,000 years old, by about 100
horizon distances. (A horizon distance is the distance light could have
traveled since the beginning of the universe.) Thus, these calculations
imply that there would have been no opportunity for the backgroundradiation in far flung corners of the universe to have “thermalized” or comeinto a thermal equilibrium by mixing. Instead, the only way to explain thehomogeneity in the temperature of the background radiation given thestandard big-bang model is to posit that the initial conditions of theuniverse were extremely finely tuned.
3. In current models, inflation begins at around 10–37 seconds after the bigbang and lasts until 10–35 seconds, during which space itself expands by afactor of 1060 or so. At the beginning of the inflationary epoch theobservable universe was, say, about 10–60 meters in size and at the end of it
about a meter across. At the start of inflation, however, the horizon distance(the distance light could have traveled since the big bang) was 10–37 lightseconds, which is far larger than the tiny patch that was destined to growinto our observable universe. According to the inflationary model, someresidual inhomogeneity in the background radiation might have remainedeven after the initial thermalization occurred. Nevertheless, if it did it
would have existed only in the parts of the early universe that lay beyond
the patch that would become our observable universe. Thus, theinflationary process not only distributed the homogeneous backgroundradiation throughout the observable universe, it also would have distributed
all remaining inhomogeneity beyond the edge of the observable universe aswell.

4. See Garriga and Vilenkin, “Many Worlds in One”; Vilenkin, Many Worlds
in One. The many-worlds-in-one model has the consequence that all
macroscopic sequences of events not forbidden by physical conservation
laws not only occur somewhere in an eternally inflating universe, but occur
over and over again without limit as inflation endlessly spawns new
expanding regions of space-time. For instance, the model suggests there arean unlimited number of macroscopically exact copies of the earth andeverything that exists on it, even though the probability of any given
observable region of the universe containing such a copy is vanishingly
small.
5. Koonin, “The Cosmological Model of Eternal Inflation and the Transition
from Chance to Biological Evolution in the History of Life.”
6. Penrose, “Difficulties with Inflationary Cosmology,” 249–64. Penrose, The
Road to Reality: A Complete Guide to the Laws of the Universe, 746–57.
7. Hawking and Page, “How Probable Is Inflation?”
8. Collins, “The Fine-tuning Design Argument,” esp. 61.
9. Penrose, “Difficulties with Inflationary Cosmology,” 249–64. Penrose, The
Road to Reality: A Complete Guide to the Laws of the Universe, 746–57,
esp. 730, 755.
10. Dyson, Kleban, and Susskind, “Disturbing Implications of a Cosmological
Constant.”

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Searchable Terms

Note: Entries in this index, carried over verbatim from the print edition of thistitle, are unlikely to correspond to the pagination of any given e-book reader.
However, entries in this index, and other terms, may be easily located byusing the search feature of your e-book reader.

Page references followed by fig indicate an illustrated figure.
abductive reasoning: comparing inductive and, 153; description andapplication of, 153–54; historical sciences employment of, 409
Abrams, Dan, 439–40, 441
The Abrams Report (MSNBC show), 439
Adami, Christopher, 286 affirming the consequent fallacy, 153
Agassiz, Louis, 145 agent causation, 341–44
American Association for the Advancement of Science (AAAS), 31, 421, 433
American Civil Liberties Union (ACLU), 2–3, 397, 411, 416, 432, 433
American Humanist Manifesto III, 448
amino acids: chemical affinity related to chemical structure of, 233–34;
condensation reaction of, 206fig; examining how common or rare aresequences of, 209–13; geological/geochemical evidence on prebiotic
atmosphere and, 224–26; peptidyl-transferase reaction and activation of,306; research on protein synthesis role of, 119–20; RNA-world hypothesis
and issue of, 303; sequence hypothesis and sequences of, 12, 100–105,
109, 365–67; specified biological information contained in, 197–200;
standard genetic code showing, 102fig; translation of mRNA at ribosome
resulting in sequencing of, 128fig; tRNA showing anticodon and, 129fig;
two optical isomers of the same, 207fig
AMP (adenosine monophosphate), 130 annus mirabilis (Einstein’s miracleyear), 139
Aquinas, St. Thomas, 37
argument from ignorance, 375–79, 380–81
Aristotle, 11, 36, 43, 143
Arkansas Board of Education, Mclean v., 416, 432
ASCII (American Standard Code for Information Interchange), 23, 116–17fig,
118

Astbury, William, 93, 94
At Home in the Universe (Kauffman), 265
ATP (adenosine triphosphate), 130, 131–32, 202, 261, 306, 309, 344
Avery, Oswald, 66, 67, 68, 104
Avida algorithm, 286–91, 337
Page references followed by fig indicate an illustrated figure.
Axe, Douglas, 209fig–10, 211–12, 213
Ayala, Francisco, 9, 19, 410
Bakh, A. N., 49
Bary, Heinrich Anton de, 45
Bazell, Robert, 439
Beadle, George, 101, 459
Behe, Michael, 5, 412, 480
Bell Laboratories, 87
Bergmann, Max, 94
Berlinski, David, 282
Berzelius, Jöns Jakob, 33
big-bang theory, 24, 157–58, 165–66, 444
biochemical predestination: description of, 230; origins and development of,
232–34. See also self-organization theories
Biochemical Predestination (Kenyon and Steinman), 230, 237
Biochemistry (journal), 210
Biochemistry (Lehninger), 195 bioinformatics: emergence of, 14–15; Human
Genome Project (2000) mapping of, 14–15 biological information: Avida
algorithm presupposing, 288–90, 337; COI (conservation of information)
principle of, 292–94; combination lock demonstration of specified, 349–
50, 352–53; Dembski’s theory and information-processing system of,
367–69; description of, 85; “displacement problem” of, 267–70; energy as
producing order as opposed to, 255–57; examining the origin of, 17–18;
extragenomic or ontogenetic, 473–77; Human Genome Project (2000)
mapping of, 14–15, 16; intelligent design cause of specified, 341–44;
Kauffman’s self-organization model of, 260–67; limits of selforganizational algorithms to produce, 258–59; probabilistic resources
calculating random chance of, 191–93, 215–19, 294; RNA polymerase
sequence, 22–23; specified DNA, RNA, and amino acids, 197–200. See
also DNA information; information; living organisms
biological science: acceptance of evolutionary theory within, 11–12;
designing hypothesis in early, 139–47; on design of living organisms, 20–
22; emerging fields of genomics and bioinformatics, 14–15; enzymatictheory of, 47; failure of spontaneous generation theory, 39; protoplasmic

theory of, 44–45, 47; rational foundation of, 142–47; recent advances in,
13–14; teleological thinking of, 21–22; theories of heredity in, 60–69. See
also DNA (deoxyribonucleic acid); science; scientific theories
Biologic Institute, 138, 369
Biology (Miller and Levine), 446 “blind watchmaker” thesis, 445
Boeing plant (Seattle), 120–21
Bonaparte, Napoleon, 153
Bondi, Sir Herman, 141–42
Boole, George, 87
Borel, Emile, 217
Boyle, Robert, 11, 38, 144, 147
Bradley, Walter, 4, 26
Bragg, Lawrence, 93, 95
Bragg, William Lawrence, 58, 70, 71, 93 branching tree diagram, 424
Brenner, Sydney, 119
Brillouin, Leon, 292
Brooks, James, 225
Brown, Titus, 286
Bungenberg de Jong, H. G., 53
Burgess Shale, 151
Bush, George W., 3
CAD-CAM technology, 120–21, 369
Cairns-Smith, Alexander, 198
Calvin, Melvin, 229
Cambridge University: Bondi and Hoyle at, 141–42; historic influence of,
142–47; support for design hypothesis at, 145–47; Wilkins’s lecture at,
136–38
Capretti, Gian, 324, 325cassette mutagenesis technique, 208
The Cat in the Hat Comes Back (Dr. Seuss), 271–72
causal adequacy: as cause criteria, 159–60, 161; hypothesis testing based on,405; intelligent design (ID) theory, 328–44
causal existence criterion, 166–68
causal-existence requirement, 381–83
causal history, 157–58
causality/cause: assessing competing possible causes, 327–28, 409; causal
adequacy criteria, 159–60, 161, 328–44, 405; causal existence criterion,
166–68; causes now in operation or presently acting, 160; criteria for
determining best cause among group of, 159, 163–66; explanatory filter
schematic for determining, 354, 355fig; IBE (inference to the best

explanation) approach to, 155–59, 343–44; natural selection andmutation, 204–5, 458, 461, 464, 476, 479; progressively eliminating
hypotheses used to determine, 324–26; retrospective causal analysis, 161,
168–69; schematic of logical problem of retrodiction, 162fig; scientifictesting of, 169–70; Sober on establishing, 161–63; uniquely plausible
cause, 161; vera causa (actual cause), 160, 171
Cavendish Laboratory, 70, 145
cells: genetic mutations in, 204–5, 458, 461, 464, 476, 479; improbability of
producing genes and proteins in, 201–3; information processing systemof, 344–46. See also DNA (deoxyribonucleic acid)cell theory, 61Center for Comparative Genomics and Bioinformatics (Penn State
University), 464
Center for Protein Engineering, 209
Chamberlain, Thomas, 154chance association, 195
chance elimination: examining, 179–83; justifying, 219–22; moving tointelligent design from, 354–56
chance hypothesis: applied to origin of life, 195–96; Axe’s work confirming
skepticism about the, 209–14; chance as negation and, 177; conditional
probability and, 220–22; environmental factors related to origin-of-life,
224–26; evaluating the, 194, 222–24; ex post facto pattern recognition
and, 188–89; Fisher’s statistical hypothesis testing, 178, 179–82, 183–84,
193; functionally significant patterns and, 106, 107fig, 185–88, 199–200,
208, 251–52; pattern recognition in context of, 189–91, 199–200, 356–59;
probabilistic resources and, 191–93, 215–19, 294; reasonable rejection of,
192. See also scientific theories
Chance and Necessity (Monod), 173, 174
chance/probability: of amino acid sequences, 209–13; calculating odds of life
arising by, 226–28; conditional probability, 220–22; Darwinian
optimization principle and, 281–83; description of, 175–76; dichotomy
between necessity and, 230; elimination of, 178–83, 219–22, 354–56;
improbability paradox and, 183–88; improbability of producing genes andproteins, 201–3, 209–13; Monod’s examination of, 173–74; as negation,
177; questioning the legitimacy of, 194–95; “rejection region” and,181fig–82
Chargaff, Erwin, 68, 69, 74, 140, 387
Chemical and Engineering News, 237“chemical evolution,” 24–25
chemical evolutionary theories: chemical two-step theory, 46–47; foundationsof Oparin’s, 48–50; Miller-Urey experiment based on Oparin’s, 54, 56–
57; Oparin’s account of first organisms, 52–54; Oparin’s early earthorigin-of-life scenario (1936), 51fig–52; simplified schematic of Oparin’s
1936 scenario of, 55fig“chicken and egg” dilemma, 134, 344–45
chromatin (chromosomes), 62
Chronicle of Higher Education, 2Chung, W.-Y., 464
Cicero, 11
Clinton, Bill, 15
coacervate structure, 53fig
Cohn, Ferdinand, 45
COI (conservation of information) principle, 292–94
coin-flipping criminal scenario, 191–93
Collins, Francis, 15
Columbus, Christopher, 253
combination lock demonstration, 349–50, 352–53
combinatorial problem: Darwinian mechanism and, 204–13; description of,204
complex information, 106, 107fig
condensation reaction, 206fig
conditional probability, 220–22
conservation of energy, 41
Contact (science-fiction novel), 383
Copernicus, 139, 145
cosmology: big-bang theory of, 24, 157–58, 165–66, 444; steady-state theoryof, 165–66
creationism, 417
Crick, Francis: on difficulty of origin-of-life problem, 321; DNA molecule
discovery by, 6, 11, 12, 15, 58–59, 60, 69–84; early partnership between
Watson and, 70; equating complex sequences with information, 387; on
evolution of living organisms, 20; on genetic code as “frozen accident,”
195; “Molecular Structure of Nucleic Acids:” by Watson and, 82;
photographs of, 13fig, 83fig; on qualitative characteristics of life, 238;
research on DNA coding by, 114–20; sequence hypothesis of, 12, 100–
105, 109, 365–67; two helix DNA model developed by Watson and, 76–
84, 139, 145, 427; Wilkin’s recount of working with, 136–38
cyanide experiment, 33–34, 40
cyanogen radical (-CN), 47
Dalai Lama, 3
Darwin, Charles: anniversary of birth of, 31; branching tree diagram of, 424;
on difficulty of explaining origin of life, 39; influence on Oparin by, 49;

on design question, 32; limited experimental science work by, 139; On theOrigin of Species by, 7, 9, 11, 12, 31, 35, 139, 160, 169, 348, 404, 410;
photograph of, 18fig; scientific precedent set by, 42–43, 151; vera causa(actual cause) approach used by, 160. See also natural selection theory
Darwinian optimization principle, 281–83
Darwin’s Black Box (Behe), 5, 412, 480
Darwin, Solomon, 234–35
Davidson, Eric, 472
Davies, Paul, 387
Dawkins, Richard: “blind watchmaker” thesis of, 445; on computer-like codeof genes, 12, 21, 23, 368; genetic algorithms developed by, 281–84, 292;
The God Delusion by, 31, 389; on illusion of design, 4; on implications of
DNA role in heredity, 238; motivations of, 448; on study of biologicaldesign, 18, 410; who designed the designer, objection to the ID argument,
389–94; YouTube mock interview regarding, 391
Day, William, 57
deamination process, 302
Declaration of Independence, 23“defeater” arguments, 417
demarcation arguments against ID, doesn’t explain by natural law, 418–21;
invokes unobservables, 423–35; isn’t testable, 425–31
demarcation problem, 400–401, 419, 430–31
Dembski, William: design detection method used by, 351, 364–67, 370–71;
The Design Inference by, 5, 351, 352, 404, 412; “explanatory filter”
schematic by, 354, 355fig; on Fisher’s chance elimination, 179–83, 354–
56; on the improbability paradox, 183–88; on probabilistic resources,
189–93, 215–19, 294; independence as design detection condition, 356–
63; on indicators of intelligent design, 352–54; on logic of information
storage and processing, 367–69; on moving from chance elimination to
intelligent design, 354–56; on nonprotein-coding DNA, 407; on patternrecognition, 185–89, 356–59; photograph and endearing eccentricities of,
178fig–79“The Demise of the Demarcation Problem” (Laudan), 432–33
Democritus, 37
deoxyribose sugar structure, 64fig
Derrida, Bernard, 267
design: appearance of biological, 410–11; biological study of living organism,
20–22; detecting, 349–59; genome DNA information storage evidence of,464–70; natural selection theory on “without designer,” 18–20. See also
intelligent design (ID) theory; living organisms
design detection: combination lock demonstration of, 349–50, 352–53;
converging complexity and specification for, 370–72; Dembski’s criteria

applied to DNA, 364–67; meaningful English phrase or sentence as
specification, 363fig; Dembski’s method of, 351; “explanatory filter”
schematic for, 354, 355fig; functional patterns used for, 359–63;
importance of independence for, 356–63; mathematical expression of
Dembski’s theory for, 363; pattern recognition used for, 359; small
probability specifications for, 370
design hypothesis: evidence of viewpoint at Cambridge, 145–47; historic
abolition of, 147–49; Judeo-Christian beliefs supporting, 143–44;
Newton’s belief in the, 139, 145–47. See also intelligent design (ID)theorydesign inference: argument from ignorance and, 375–79, 380–81; causalexistence requirement for, 381–83; dependent on present knowledge, 377,
378; genomic information storage system, 464–70; uniformitarianism
principle and, 377–78
The Design Inference (Dembski), 5, 351, 352, 364, 404, 412
Design Patterns, 369
Dialogues Concerning Natural Religion (Hume), 383
dinosaur extinction causal history, 157–58
Discovery Institute, 8, 138, 369
displacement problem: Quastler’s approach to origin of life and, 278–79; selforganization, 267–70
distal-less (Dlx) gene, 471
DNA (deoxyribonucleic acid): Crick’s “sequence hypothesis,” 12, 100–105,
109, 365–67; Dembski’s design detection criteria applied to, 364–67;
early examination as ID evidence, 4–8; examining origin of life role of,
25–31; exons and introns of, 125, 463; Flew’s announcement on
intelligent design and, 2; hereditary traits transmitted through, 64–69;
improbability of producing genes and proteins by chance and, 201–3;“intelligent cause” theory on origin of, 26–31; measuring amount of
information-carrying capacity of, 108–10; natural selection and mutationof, 204–5, 458, 461, 464, 476, 479; nonprotein-coding DNA (junk DNA),
125, 257, 367, 406, 407, 454, 455, 461, 464; origin of life
reconceptualized by, 132–35; pattern of hydrogen bonds in structure of,
115–16fig; questions regarding the enigma of, 12–14; replication of, 131–
32fig; ribosomes as missing, 119; as storing functionally specified
information, 109–10, 251–52; structural formulas of each chemical partsof, 65fig; tetranucleotide hypothesis on, 65–66, 68; Watson and Crick’s
discovery of the structure of, 6, 11, 12–13, 15, 58–59, 60, 69–84. See also
biological science; cells“the DNA enigma”: assessing chance as best explanation of, 223–24;
biochemical predestination approach to, 230, 232–34; calculating odds ofchance alone for, 226–28; chance dilemma of, 173–93; chance hypothesis

applied to, 195–96; Dembski’s design detection criteria applied to, 364–
67; description of, 8, 14; evolutionary algorithms attempt to solve the,283–95; intelligent design as causally adequate explanation of, 328–44;
introduction to and overview of, 24–31; natural selection as explanationof, 18–20; Oparin’s revised chemical evolutionary theory and, 273–77;“progressively eliminating hypotheses” to investigate, 325–26; questions
regarding the, 12–14; RNA-world hypothesis and, 295, 298–321. See also
living organisms; origin-of-life problem; self-organization theories
DNA-first model, 277–79, 313, 322
DNA information: alternative sequences of, 86; stored in eukaryotic cell, 96–
97; examining the source of, 12–14, 66; “forces of potential” energy andorganization of, 249–52; concentration in the genome, 461–67;
information theory applications to, 91–92; measuring DNA informationcarrying capacity, 108–10; nucleosome spooling and density of, 97–98fig;
Polanyi’s arguments on reductionism-vitalism debate and, 240–46;
protein molecules and role of, 92–100; “sequence hypothesis” and, 12,
100–105, 109, 365–67; using Shannon’s equations for computing capacityof, 327; significance and implications of, 14–15; storage of, 92. See also
biological information; genetic codes; information
DNA models: A-form, 71; antiparallel strands entwined around each other,
79fig; B-form, 71, 73, 75; Chargaff’s rules used to establish, 74–75;
chemical structure depicting main chemical bonds, 242fig; Franklin’s
monoclinic C2 symmetry pattern, 79; large-scale form of helix showing
grooves, 116fig; Maltese cross X-ray image of DNA crystal, 74fig, 137,
427; mistakes of Watson and Crick’s early, 70–71; Pauling’s triple-sugarphosphate backbone, 71, 72, 75, 76; sugar phosphate backbone, 78fig–79;
triple helix versus double helix controversy, 70–71, 75–76; Watson andCrick’s two helix, 76–84, 136–38, 139, 145, 427
Dobzhansky, Theodosius, 275
Donohue, Jerry, 81
Doolittle, Russell, 26Dose, Klause, 321
Dover trial (2005), 396, 397, 398, 411, 415, 426, 434, 436, 441
Dretske, Fred, 251
de Duve, Christian, 190, 199, 200, 227, 245, 269, 275, 314
Dyson, Freeman, 138, 280
Easter Island, 390–91
E. coli bacterium, 304
Eden, Murray, 211
editosomes, 462

Eger, Martin, 433
Eigen, Manfred, 258, 279
Einstein, Albert, 6, 139élan vital, 40
energy: conservation of, 41; first law of thermodynamics, 41; order produced
through, 255–57; self-organization of living organisms and role of, 254–
55
Enlightenment, 37
environmental factors: biochemical predestination and, 230, 232–34;“chemical roulette” and, 226; evidence on prebiotic atmosphere, 224–26
enzymatic theory, 47
enzymes: comparing RNA catalysts to, 310–12; hypercycle system of RNA
and, 279–81; RNA-world hypothesis on role of, 298–300, 302–3, 304;
self-reproducing metabolic cycle of, 262–63; synthesizing RNA
molecules and role of, 119–20. See also ribozymes
Erwin, Douglas, 471
eukaryotic cell, 96–97
Ev algorithm, 283–86, 290
evolutionary algorithms: Avida, 286–91, 337; causal adequacy of intelligent
design demonstrated by, 335–37; COI (conservation of information)
principle of, 292–94; Ev, 283–86, 290; failures of, 290–94; NFL (no freelunch) theorem of, 291–92
evolutionary theory: biological science acceptance of, 11–12; early
development of the, 151–52; ID addresses specific question in, 410–11;
intelligent design (ID) theory challenge to, 4; metaphysical or religious
implications of, 444–46; neo-Darwinian version of, 57, 204–13, 445;
precedent and implications of, 42–44. See also natural selection theory;
scientific theories
exons, 463
Expelled: No Intelligence Allowed (film), 120
experimental evidence: confirming casual adequacy of intelligent design,
333–41; cyanide experiment, 33–34, 40; evolutionary algorithms as, 283–
94, 335–37; fruit fly experiments, 62–63fig; historical versus, 150;
injected mice experiment on heredity, 66, 67fig; Mendel’s heredity, 61–62; Miller-Urey experiment based on Oparin’s theories, 54, 56–57; on themind as source of information, 340–41; prebiotic simulation, 334–35;
ribozyme engineering, 318–19, 338–40. See also science
experimental sciences, 150
explanandum, 326–27“explanatory filter” schematic, 354, 355fig
extinction: causal history of dinosaur, 157–58; Irish elk, 167
extragenomic information, 473–77

Fersht, Alan, 209
first law of thermodynamics, 41
Fisher, Ronald A., 178, 179–82, 183–84, 193
Flew, Antony, 2, 445
formose reaction, 302–3
Forrest, Barbara, 396, 411, 447, 448
Franklin, Rosalind, 58–59, 70, 71, 73fig, 79, 137
fruit fly experiments, 62–63fig
Fry, Iris, 261
Fuller, Steve, 143
functionally significant patterns: chance hypothesis and, 106, 107fig, 185–88,
199–200; considering how common or rare are, 208; design detection
using, 359–63; in DNA sequences, 251–52; meaningful English phrase or
sentence as, 362fig. See also pattern recognition
functional specificity, 388
Futuyma, Douglas, 445
Gallie, Walter Bryce, 163
Gamow, George, 114fig
Gates, Bill, 12, 368, 385
gene expression system: description of, 103–4; example of, 102fig; sequence
hypothesis confirmed through, 104–5; simplified schematic of, 104fig;
transcription stage of, 122–27, 134; translation stage of, 127–31, 134; as
two-stage process of information transfer, 122. See also protein synthesis
General Scholium (Principia introduction), 147
genes/genomes: computer-like machine code of, 12, 21, 23, 368–69;
concentration of information in DNA, 461–67; context dependence
modular elements of, 472–73; distal-less (Dlx), 471; extragenomic or
ontogenetic information in, 473–77; hierarchical organization of, 467–70;
improbability of producing proteins and, 201–3, 209–13; informational
context of, 471–73; new genomics study of, 459–60; Pax–6, 471
genetic code: amino acids that DNA base triplets specify, 102fig; comparing
ASCII code to, 116–17fig, 118; description of, 102; See also DNA
information
genetic fallacy, 447–48
genomics: emergence of, 14–15; examining new discoveries of, 459–73;
Human Genome Project (2000) mapping of, 14–15; study ofconcentration of information in DNA, 461–67
Gilbert, Walter, 296, 297fig, 300, 302
Gilder, George, 16glycolysis, 131–32
God: arguments against existence of, 31–32; creationism and belief in, 417;
ID and meaningful “search for,” 450–51; ID testability and, 428–29; ID
and theistic implications related to, 443–46. See also religion
The God Delusion (Dawkins), 31, 389
God Is Not Great, 31
Goodsell, David, 133
Gould, Stephen Jay, 150, 156, 402, 445
Graham, Loren, 48
Gray, Asa, 169
Griffith, Frederick, 66, 67fig
GTP (guanosine triphosphate), 131
Guest, Howard, 155
Haeckel, Ernst, 41, 42, 46–47, 61
Hamlet (Shakespeare), 281
Heller, H. C., 446
Helmholtz, Hermann von, 40–41
heredity: cell theory of, 61; DNA role in, 64–69; fruit fly experiments on, 62–
63fig; injected mice experiment on, 66, 67fig; Mendel’s experiments on,
61–62; Omne vivum ex vivo (all life comes from life), 60; pangenesis
theory of, 60; specificity complexity used to describe, 387–88
Hereen, Fred, 242
Himalayas, 419–20
histones, 97
historical science/inference/explanation: assessing the competing possible
causes for, 327–28; assessing competing possible causes, 327–28; case for
ID exemplifies reasoning of, 408–10; causal existence criterion for, 166–
68; causal-existence requirement of, 381–83; characterizing the effect orjust explanandum, 326–27; criteria for determining cause, 159, 163–66;
IBE (inference to the best explanation) approach to, 155–59, 343–44;
intelligent design and causal-existence criterion of, 341–44; progressively
eliminating hypotheses used, 324–26; retrospective causal analysis, 168–69; schematic of logical problem of retrodiction, 162fig, 398; scientifictesting in, 169–70. See also intelligent design (ID) theory
historical sciences, 150, 168–69, 402
Hobbes, Thomas, 37
Hodge, Jonathan, 152
Hodgson, Peter, 144
holoenzyme, 124–25fig
homology, 403

Homo sapiens, 391
Hooker, Joseph, 43
Hopkins, Sir Fredrick Gowland, 34
Hoskin, Michael, 140, 141
Hoyle, Sir Fred, 142, 213, 257
Human Genome Project (2000), 14–15, 16
Hume, David, 28, 37, 383–84
Huxley, Thomas Henry, 45, 46hydrogen bonds, 80–82
hydrolysis, 131
hypercycles, 279–81
hypotheses. See scientific theories
IBE (inference to the best explanation) reasoning, 155–59, 343–44
idealism, 37
improbability paradox, 183–88
independently experienced patterns: Dembski’s design detection using, 360–
63; description of, 356–59
inductive reasoning, 153
Inference to the Best Explanation (Lipton), 154–55, 158, 327
information: ASCII (American Standard Code for Information Interchange),23, 116–17fig, 118; COI (conservation of information) principle of, 292–
94; combination lock demonstration of specified, 350–51, 352–53;
definitions of, 16, 86, 90; “displacement problem” of, 267–70; elusive
character of, 15; intelligent design as explanation of specified, 106,
107fig, 349–50; limits of self-organizational algorithms to produce, 258–
59; as metaphor, 386–88; the mind as source of, 340–41; problem ofaccessing encoded, 112–13; Shannon’s information theory of, 87–92,
105–10; specified, functional, complex, and compressible, 106, 107fig.
See also biological information; DNA information
information-carrying capacity (or Shannon information), 106
information content, 106, 107
information as metaphor, 386–88
information theory: applications to DNA, 91–92; description of, 87–90;
equating reduction of uncertainty to transmission of information, 250–51;
illustrations of, 105–10; limitations of, 90–92; measuring DNA
information-carrying capacity, 108–10; small probability specifications
and, 370–71. See also Shannon, Claude
Inherit the Wind (film), 3, 397
initiation factors, 127–28
initiator codon (AUG), 128injected mice experiment, 66, 67fig
intelligent design and causal adequacy: examining elements of, 328–30, 347–
48; experimental evidence confirms causal adequacy of ID, 333–41; ID isthe only known cause of specified information, 341–44; no other causallyadequate explanations, 330–33
intelligent design (ID) theory: causal adequacy of, 328–44, 347–48, 405;cell’s information processing system suggesting, 344–46; combination
lock demonstration of, 349–50, 352–53; controversy surrounding the, 1–
2, 31–32, 138–39, 375–76; Discovery Institute research on, 138; Dovertrial (2005) on the, 396, 397, 398, 411, 415, 427, 435, 436, 441;
evolutionary theory challenged by, 4; examined as possible scientificexplanation, 170–71; examining criteria as scientific theory to justify,
421–31; Flew’s announcement on DNA evidence of, 2; Hoyle’s comments
on, 142; independence used to detect, 356–63; indicators of, 352–54;“intelligent cause” formulation of, 26–31; as “living science,” 455–56;
moving from chance elimination to, 354–56; new research directions on,
477–81; ongoing explanatory power of, 456–59; origins and earlyarguments for, 4–8; peer-reviewed scientific literature supporting, 411–14;
personal and philosophical issues raised by, 448–51; plausibility of, 269–
70; professional attacks against supporters of, 398–99; as religion
masquerading as science, 396–402, 439–48; religious beliefs used todiscredit, 448; religious motivations of advocates of, 447–48; semantic
dispute over, 399–402; as testable theory, 404–7; theistic implications of,
443–44. See also design; designing mind; historicalscience/inference/explanation; origin of life; origin-of-life problem;
scientific theories
introns, 125, 463
Irish elk extinction, 167
ISSOL (International Society for the Study of the Origin of Life) [1989], 321
Jacob, François, 119
Jerusalem Post (newspaper), 3
Jones, John E., III, 396, 397, 398, 411, 415, 431, 432, 434, 435, 441
Journal of Experimental Medicine, 68
Journal of Molecular Biology, 210
Joyce, Gerald, 315, 317, 322
Judeo-Christian beliefs: designing mind, 143–44; vision of the world
according to, 144. See also religion
Judson, Horace, 95, 115, 119
junk DNA, 125, 257, 367, 406, 407, 454, 455, 461, 464

Kamminga, Harmke, 35, 42, 60, 237
Kant, Immanuel, 140
Kauffman, Stuart: At Home in the Universe by, 265; photograph of, 260fig;
self-organization model of, 260–67
Kay, Lily, 386–87
Kendrew, John, 72, 95–96
Kennedy, John F., 343, 453
Kenyon, Dean: exploration of the DNA enigma by, 26, 325; photograph of,
231fig; on the RNA-world hypothesis, 303; San Francisco State detractorsof, 398, 423, 426; self-organization theory by, 229–37, 245, 257; selforganization theory later rejected by, 397–98
Kepler, Johannes, 38, 143, 145, 147
Kitcher, Philip, 406, 432, 454–55
Koonin, Eugene, 306
Krebbs, Norman, 28
Küppers, Bernd-Olaf, 13, 244, 281–84
Laboratory for Molecular Biology (LMB), 94
Laplace, Pierre, 38, 39
Large Hadron Collider, 425
Laudan, Larry, 432
Lehninger, Albert L., 195
Lenksi, Richard, 286
Lenoir, Timothy, 21–22
Leucippus, 37
Levene, P. A., 64–65, 66, 94
Levine, Joseph, 446
Lewontin, Richard, 18, 133, 134, 410
Leybig, Franz, 45
Life: The Science of Biology (Purvis, Orians, and Heller), 446“Life’s Irreducible Structure” (Polanyi), 237
LINEs (Long Interspersed Nuclear Elements), 468
Linnacus, Carl, 145
Lipton, Peter: on IBE (inference to the best explanation) reasoning, 155–59,
343–44; photograph of, 155fig
Living with Darwin (Kitcher), 454
living organisms: classical reductionismvitalism debate on, 237–40;
distinction between anorgana (nonliving) and, 41–42; distinguished by
specified complexity, 110; élan vital of, 40; environmental impact on
development of, 43–44; evolutionary theory implications regarding, 42–
44; flawed analogy between human artifacts and, 383–86; qualitative

distinction between nonliving and, 238; self-organization and energy in,
254–55; theories on heredity and, 60–69. See also biological information;
design; “the DNA enigma”; origin of life
Loew, Oscar, 47
Luria, Salvador, 69
Lyell, Charles, 38, 151, 160
Lynch, Michael, 470
McCarty, Maclyn, 68
Mclean v. Arkansas Board of Education, 416, 432
MacLeod, Colin, 68
Macready, William, 291
Maimonides, Moses, 11, 36
Marks, Robert, 284–85, 292, 341
Marxist doctrine, 49
Massachusetts General Hospital, 119
materialistic worldview: description of, 36–37; protoplasmic theory and, 44–
45; two-step chemical process theory as, 46–47; vitalist doctrines of, 41–
42“Mathematical Challenges to Neo-Darwinism” (1966 conference), 204–13
The Mathematical Principles of Natural Philosophy (Newton), 139, 146, 147“The Mathematical Theory of Communications” (Shannon), 88
matter-first worldview, 37–38
Maxwell, James Clark, 145 “The Mechanistic Interpretation of Life”(Virchow), 41–42
Medawar, Sir Peter, 204
Medical Research Council Centre (Cambridge), 79, 95, 136, 209–10
MEDLine, 411
Mendel, Gregor, 61–62
methodological equivalence, 422
methodological naturalism, 436–37
Meyer, Elaine, 34
Miescher, Friedrich, 61–62
Miller, Kenneth R., 296–97, 431, 446, 448
Miller, Stanley, 54, 56–57, 224, 226, 302, 334
Miller-Urey experiment, 54, 56fig–57
mind (designing mind), 144
mind-first worldview, 37–38
Mohl, Hugo von, 45“Molecular Structure of Nucleic Acids: A Structure for Deoxyribose Nucleic
Acid” (Watson and Crick), 82

Monk (TV show), 324, 326
Monod, Jacques, 100, 116, 133–34, 173–74, 230, 387
Mora, P. T., 223–24
Morgan, Thomas Hunt, 62–63, 66
Morowitz, Harold, 276
Morris, Simon Conway, 151, 152
mRNA (messenger RNA): early research on protein synthesis and, 119–20;
failure of self-organization theory to explain, 246–49; gene expressionrole of, 103; translation and role of, 127–28. See also RNA (ribonucleicacid)
Mt. Rushmore, 353, 359
Muller, Hermann J., 69, 274
Murphy, Nancey, 434
Museum of Natural History (Smithsonian), 1
mutations: as cause of evolutionary change in conjunction with natural
selection, 458, 464; in DNA, 461, 476, 479; neo-Darwinian theory on
random, 204–5
Mycoplasma bacteria, 122, 201
myoglobin protein, 95fig
The Mystery of Life’s Origin (Thaxton, Bradley, and Olsen), 26, 29
Nägeli, Karl Wilhelm von, 45, 47
NASA’s SETI program, 344, 383, 395, 436
National Academy of Sciences, 31
National Center for Biotechnology Information, 306
National Center for Science Education, 146, 423
National Institutes of Health, 119, 223, 283
naturalistic worldview, 37
natural law, 418–21
natural selection: Avida algorithm of, 286–91, 337; “blind watchmaker” thesis
and, 445; Darwinian optimization principle simulation of, 281–83;
Darwin’s structure of, 169, 276; as “the DNA enigma” explanation, 18–
20; Ev algorithm on, 283–86, 290; examining the reasoning of, 458–59;
influence on Oparin, 49; metaphysical or religious implications of, 444–
46; mutation role in conjunction with, 204–5, 458, 461, 464, 476, 479; On
the Origin of Species (Darwin) on, 7, 9, 11–12, 35–36, 38, 139, 160, 169;
Oparin’s revised theory of prebiotic, 272–77; precedent and implications
set by, 42–44. See also Darwin, Charles; evolutionary theory; neoDarwinian theory; scientific theories
nature: Greek philosophers’ approach to, 143; scientific rationality andapproach to, 142–47

Nature (journal), 2, 82, 95, 96, 290
nebular hypothesis, 38, 39
negation (chance as), 177
negative generalizations, 379–81
Nelson, Paul, 178, 179, 472–73
neo-Darwinian theory: combinatorial problem associated with, 204–13;
design in living organisms denied by, 445; homology support of, 403;
“Mathematical Challenges to Neo-Darwinism” (1966 conference)challenging, 204–13; on random mutations of genetic information, 204–5,
458, 461, 464, 476, 479. See also natural selection theory
Neumann, John von, 276
Neurospora (bread mold), 101
New Atheists, 31
Newman, Stuart, 471
New Orleans Secular Humanist Association, 448
Newton, Sir Isaac, 11, 37, 38, 139, 145–47, 419, 456
New York Times (newspaper), 3
NFL (no free lunch) theorem, 291–92
N-glycosidic bond, 243
Nicolis, Grégoire, 254, 255, 258
Niemann, Carl, 94
Nirenburg, Marshall, 119
nonprotein-coding DNA (junk DNA), 125, 257, 367, 406, 407, 454, 455, 461,
464
nucleic acid, 63
nucleosomes, 97
nucleosome spooling, 97–98fig
null hypothesis, 177, 187–88
objections to intelligent design: Abrams’s attacks and, 439–40; argumentsfrom ignorance, 375–79; causal-existence requirement as, 381–83;
Hume’s flawed analogy argument, 383–86; ID isn’t science, 421–23; on
ID as religion masquerading as science, 396–402, 439–48; information as
metaphor, 386–88; Pennock’s arguments, 426–30; related to testability,
omnipotence, and supernatural, 426–30; as semantic dispute, 399–402;
who designed the designer? question as, 388–94
observability, 423–25
observational testing, 426–28
Ochoa, Severo, 119
Ofria, Charles, 286
Ohm’s law, 238–39

Olsen, Roger, 4, 26
Omne vivum ex vivo (all life comes from life), 60
On the Origin of Species by Means of Natural Selection (Darwin), 7, 9, 11,
12, 31, 35, 139, 160, 169, 348, 404, 410
ontogenetic information, 473–77
Oparin, Aleksandr, 44, 48fig–57, 59–60, 232, 233, 410, 448
Oparin’s chemical evolutionary theory: account of first organisms, 52–54;
chemical reactions foundations of, 48–50, 272; early earth origin-of-lifescenario (1936), 51fig–52; Miller-Urey experiment based on, 54, 56–57;
revised version including DNA explanation, 273–77; simplified schematicof 1936 scenario of, 55fig
Opticks (Newton), 11, 145
The O’Reilly Factor (TV news show), 3
Orgel, Leslie, 110, 315, 317, 322, 387, 388
Orians, G. H., 446
origin of life: discussion with Sir Herman Bondi on the, 141–42; enzymatictheory of, 47; examining DNA role in the, 25–31; failure of spontaneous
generation theory to explain, 39; lack of satisfactory “chemical evolution”
theory of, 24–25; Miller-Urey experiment demonstrating, 54, 56fig–57;
nebular hypothesis and, 38, 39; Oparin’s chemical evolutionary theory on,
48–57; Oparin’s early earth origin-of-life scenario (1936), 51fig–52;
Oparin on solving problem of, 59–60; Oparin’s revised theory of, 272–77;
reconceptualized following discovery of DNA, 132–35; two-stepchemical process theory of, 46–47; Wöhler’s cyanide experiment
implications for, 33–34. See also living organisms
The Origin of Life (Oparin, 1924 edition), 48
The Origin of Life (Oparin, 1938 edition), 48
origin-of-life problem: assessing chance as best explanation of, 223–24;
calculating odds of life arising by chance alone, 226–28; chance
hypothesis applied to, 195–96; displacement problem of Quastler’s
approach to, 278–79; environmental factors related to, 224–26;
hypercycles approach to, 279–81; causal adequacy of intelligent design as
solution to, 328–44, 405; natural selection and, 7, 9, 11, 12, 18–20;
Quastler’s DNA-first model on, 277–79, 313, 322; RNA-world hypothesison, 295, 298–321; self-organization approach to, 230; specified biologicalinformation issue of, 197–200; worldview conflicts at heart of debate
over, 37–38. See also “the DNA enigma”; intelligent design (ID) theory
pangenesis theory, 60
Pasteur, Louis, 39
past reconstruction: causal existence criterion for, 166–68; criteria fordetermining cause, 159, 163–66; IBE (inference to the best explanation)
approach to, 155–59, 343–44; retrospective causal analysis, 168–69;
schematic of logical problem of retrodiction, 162fig; scientific testing of,
169–70Pattee, Howard, 274
pattern recognition: CAD-CAM technology and, 120–21, 369; chance
hypothesis in context of, 189–91, 199–200; ex post facto, 188–89;
independently experienced, 356–63; probability resources and, 189–93,
215, 219, 294; “string of improbable events” and, 269. See alsofunctionally significant patterns
Pauling, Linus, 58, 71
Pauling, Peter, 72
Pax–6 gene, 471
Peirce, Charles Sanders, 153, 154, 156, 325
Pennock, Robert, 286, 290, 396, 403, 426–30, 442, 447
Penn State University, 464
Pera, Marcello, 6
Perutz, Max, 58, 70, 95
Pflüger, Eduard, 47
phenotypic plasticity, 478
plagiarism scenario, 186–87
Plato, 11, 36, 186
Pneumococci bacteria, 66–67
Polanyi, Michael, 237–38, 239–41, 249–50, 251
polynucleotides, 277–78
polypeptides: Darwinian optimization principle and nonfunctional, 282;
Kauffman’s model on, 263–64; Oparin’s revised theory explanation on,
273–74
Popper, Sir Karl, 134
prebiotic atmosphere, 224–26
prebiotic natural selection, 272–77
prebiotic simulation experiments, 334–35
predestination theory, 230predictions: demarcation problem and making, 430–31; intelligent design,
testability and, 430–31; scientific method and, 401; testing historicalscientific theory and, 405“prevision,” 450–51
Prigogine, Ilya, 254, 255, 256, 257, 258, 267, 268
Principia (Newton), 139, 146, 147, 456
The Principles of Geology (Lyell), 151–52, 160
probabilistic resources, 191–93, 215–19, 294
probability. See chance/probability

Proceedings of the Biological Society of Washington (journal), 1, 5, 412
Proceedings of the National Academy of Sciences, 210“progressively eliminating hypotheses,” 325–26
prokaryotic cells: DNA of, 97; DNA replication in, 131–32; new research on,
477
proscriptive generalizations, 379–81
protein molecules: critical functions performed by, 92; DNA coding“expressed function” represented in, 203; Gamow’s direct template model
and, 114fig–15; improbability and producing genes and, 201–3, 210–13;
research on centrality to life of, 93–100; Snap-Lock Beads analogy ofamino-acid sequencing and, 99–100; specificity of arrangement in, 98–99;
specificity of sequence in, 99–100; specificity in shape of, 96–97;
studying changes in DNA sequences on, 102; three-dimensional structureof myoglobin, 95fig; X-rays used to examine, 93–94“proteinoids,” 234
protein synthesis: CAD-CAM technology to depict, 120–21; Expelled: (film)
animation of, 120; research of process of, 119–20; transcription stage of,
122–27, 134; translation stage of, 127–31, 134; as two-stage process of
information transfer, 122. See also gene expression systemprotoplasmic theory of life, 44–45, 47
Provine, William, 445
Ptolemy, 143 purines, 80, 81fig
Purvis, W. H., 446
pyrimidines, 80, 81fig
Quastler, Henry, 277–79, 313, 376
Quinn, Philip, 432
reasoning: abductive, 153–54, 409; case for ID exemplifies historicalscientific, 408–10; IBE (inference to the best explanation), 155–59, 343–
44; inductive, 153; Judeo-Christian beliefs influence on, 143–44;“progressively eliminating hypotheses” approach to, 325–26; of specified
information of intelligent design, 341–44
Reconstructing the Past (Sober), 161
reductionism-vitalism debate: applied to DNA, 240–46; description of, 237–
40
rejection region, 181fig–82
religion: creationism and, 417; disputing intelligent design as, 441–42;
examining ID theistic implications and, 443–44; ID and meaningful“search for,” 450–51; intelligent design and implications related to, 444–
46; intelligent design as science masquerading as, 396–402, 439–48;

motivations related to science and, 447–48. See also God; Judeo-Christianbeliefs
retroduction problem schematic, 162fig
retrospective causal analysis, 161, 168–69
Revolutionary War letter scenario, 463, 466
De Revolutionibus orbium coelestrium (Copernicus), 139
ribose, 302–3
ribose sugar structure, 64fig
ribosomes: comparing ribozyme functions to, 309; lack of DNA in, 119;
peptide-bond formation within, 304
ribozymes: comparing functions of enzymes to, 310–12; comparing ribosome
functions to, 309; RNA translation and role of, 302–3, 304, 309. See also
enzymes ribozymes engineering: causal adequacy of ID demonstrated by,
338–40; procedures on RNA polymerases, 318–19
RNA catalysts. See ribozymes
RNA polymerases: holoenzyme of, 124–25fig; Mycoplasma, 122; ribozymeengineering procedures and, 318–19, 338–40; sequence of, 22–23;
transcription activities reflected in structure of, 123fig–24; transcription
process and, 122–27
RNA (ribonucleic acid): chance hypothesis applied to DNA and, 195–96;
chemical structure and constituents of, 301fig; hypercycle system of
enzymes and, 279–81; improbability of producing genes and proteins bychance and, 201–3; minimal requirements for template-directed selfreplication, 317fig; new genomics study of, 459–60; self-replication
possibilities corresponding to bases of, 315; specified biologicalinformation contained in, 197–200; spliceosomes and editosomes of, 462;
structural formulas of each chemical parts of, 65fig; transfer RNAs(tRNAs), 103, 128–29fig, 130, 246–48, 305. See also mRNA (messenger
RNA)RNA Tie Club, 115
RNA-world hypothesis: “chicken and egg” dilemma answered by, 134, 344–
45; description of the, 295, 298–300; problems related to the, 301fig–21;
research on the, 296–98; seven steps of, 299fig
RNA-world hypothesis problems: 1. RNA building block are hard tosynthesize/easy to destroy, 301fig–4; 2. ribozymes are poor substitutes forproteins, 304; 3. RNA-based translation and coding system is implausible,
305–12; 4. RNA-world doesn’t explain the origin of genetic information,
312–17fig; 5. ribozyme engineering does not simulate undirectedchemical evolution, 318–21 Rockefeller Institute, 66
Rose, George, 94–95
Rosetta Stone, 344, 351
roulette playing scenario. See Slick’s roulette playing scenario

Royal Tyrrell Museum (Canada), 151
Ruse, Michael, 20–21, 416–17fig, 419, 420, 421, 433
Russell, Bertrand, 450
salt crystal structure, 94fig
Santa Fe Institute, 260
Sauer, Robert, 208, 209, 211
Schleiden, Matthias, 60
Schneider, Thomas, 283, 284, 286
Schrödinger, Erwin, 387
Schwann, Theodor, 60
science: abductive reasoning in, 153–54, 409; affirming the consequent
fallacy in, 153; conservation of energy, 41; demarcation problem of, 400–
401, 419, 430–31; designing mind affirmed in early, 139–47; diversity of
relevant resources used in, 138; early definition of information in, 16; firstlaw of thermodynamics of, 41; historical, 150, 168–69, 402; intelligent
design as “living,” 455–56; intelligent design as religion masquerading as,
396–402, 438–48; negative or proscriptive generalizations in, 379–81;
rational foundation of, 142–47. See also biological science; experimental
evidence
Science (journal), 2, 237
Scientific American (journal), 88
scientific methods: demarcation problem and, 400–401; ID advocates use of
established, 403–4; of multiple competing hypotheses, 327–28, 409;
variety of, 401–2
scientific revolution (1300–1700), 37
scientific theories: big-bang theory, 24, 157–58, 165–66, 444; cell theory, 61;
chemical evolutionary, 46–57; different approaches for testing, 401–2;
enzymatic theory, 47; examining ID in light of criteria for, 421–31;
information theory, 87–92, 105–10, 250–51, 370–71; methodologicalnaturalism, 436–37; neo-Darwinian theory, 204–13, 403, 445; pangenesis,
60; predestination theory, 230; protoplasmic, 44–45, 47; Ruse on criteria
for, 416; scientific vs. unscientific, 435; spontaneous generation, 39;
steady-state, 165–66; testability of, 425–31; theistic implications of, 443–
44; two-step chemical process, 46–47. See also biological science; chance
hypothesis; evolutionary theory; intelligent design (ID) theory; natural
selection; self-organization theories
The Scientist (journal), 2
Scopes “monkey trial” (1925), 3, 397
Scott, Eugenie, 146–47, 423, 426, 439, 448
Scriven, Michael, 161, 166, 168

Seattle Post-Intelligencer (newspaper), 1
Sekai Nippo (Tokyo newspaper), 3
Self-Organization in Nonequilibrium systems (Prigogine and Nicolis), 254
self-organization theories: applied to DNA, 240–46; description of, 230;
“displacement problem” of, 267–70; doubts about the, 234–37; external
forces and, 254–55; failure to explain DNA sequencing, 246–49;
Kauffman’s model of, 260–67; Kenyon’s repudiation of, 397–98; limits ofalgorithm to produce biological information, 258–59; ordered patterns
and, 255–57. See also biochemical predestination; “the DNA enigma”;
scientific theories
sequence hypothesis: confirmed through gene expression, 104–5; descriptionof, 12; DNA sequential arrangement verifying, 109, 365–67; specificity
and, 100–105
SETI (search for extraterrestrial intelligence), 344, 383, 395, 436
Shakespeare, William, 453
Shannon, Claude, 87fig–88, 90–92, 370. See also information theory
Shapiro, James, 21
Shapiro, Robert, 264, 280, 302, 303, 316
Shaw, Gordon, 225
Shermer, Michael, 406, 448
Shultze, Max, 45
Simpson, George Gaylord, 445
SINEs (Short Interspersed Nuclear Elements), 468
Skoog, Gerald, 442
Slick’s roulette playing scenario: chance application to, 175–76, 255–56;
chance elimination illustrated by, 180, 182–83; improbability paradox
illustrated by, 184–85; independently experienced patterns in, 357–58;
probabilistic resources applied to, 218–19
small probability specifications, 370–71
Smith, John Maynard, 280
Smithsonian Institution, 1
Snap-Lock Beads analogy, 99–100
Sober, Elliott, 161–63
Southern Methodist University, 24
specificity complexity: phenomenon of heredity described as, 387–88; protein
arrangement, 98–99; protein shape, 96–97; sequence hypothesis and, 12,
100–105, 109, 365–67; sequence of protein, 99–100
specified information: combination lock demonstration of, 349–50, 352–53;
sequences of DNA, RNA, and amino acids as biological, 197–200;
examples of, 106, 107fig; intelligent design as only known cause of, 341–
44
Der Spiegel (German newspaper), 3

spliceosomes, 462
spontaneous generation idea, 39
Stalnaker, Robert, 250
steady-state theory, 165–66
Stein, Ben, 120
Steinman, Gary, 230, 233–34
Sternberg, Richard, 2, 3
“string of improbable events,” 269
student plagiarism scenario, 186–87
Summer for the Gods (Larson), 397
supernatural being, 428–29
Sutton, Walter, 62
Tacoma Narrows bridge, 175
Tatum, Edward, 101, 459
teleological thinking, 21–22
testability. See intelligent design testability tetranucleotide hypothesis, 65–66,
68
Thaxton, Charles: analysis of argument used by, 169–70; on argument for
intelligent design, 4, 30, 31, 34, 148, 150, 151, 325; on Kenyon’s doubts
about self-organization, 237; The Mystery of Life’s Origin by Bradley,
Olsen, and, 26, 29; photograph of, 27fig
theistic worldview, 37
Theoretical Physics Institute (Copenhagen), 114
theory of intelligent design. See intelligent design (ID) theory
Time (magazine), 3, 57
Times (India newspaper), 3
Times (London newspaper), 3
Timiriazev, Kliment Arkadievich, 48, 49
Tommyrot, Dr. Terry (YouTube mock interview), 391
Tracy, Spencer, 3transcription: initiation phase of, 126fig; process of, 122–27, 134
translation: “chicken and egg” dilemma of, 134, 344–45; implausibility ofRNA-based, 305–12; main molecular components of, 307fig; process of,
127–31
tRNAs (transfer RNAs), 103, 128–29fig, 130, 246–48, 305
two-step chemical process theory, 46–47
uniquely plausible cause, 161
University of Cambridge, 25, 30
Urey, Harold, 54, 56

USA Today (newspaper), 411
U.S. Commission on Civil Rights, 146
U.S. House Committee on Government Reform, 2
U.S. Office of Special Counsel, 2
Van de Sande, Bret, 217 vera causa (actual cause), 160, 171
Victoria Harbor (Canada), 353
Virchow, Rudolf, 42
Wald, George, 195
Wall Street Journal (newspaper), 398
Washington Post (newspaper), 3
Watson, James: DNA molecule discovered by, 6, 11, 12, 15, 58–59, 60, 70–84; early life and career of, 69–70; early partnership between Crick and,70; equating complex sequences with information, 387; “Molecular
Structure of Nucleic Acids:” by Crick and, 82; photographs of, 13fig,
83fig; two helix DNA model developed by Crick and, 76–84, 139, 145,
427; Wilkin’s recount of working with, 136–38
Weaver, Warren, 90
Weisbuch, Gerard, 267
Weismann, August, 47
Weisskopf, Victor, 204“Whether ID Is Science” (Jones), 397
Whewell, William, 151
Whitehead, Alfred North, 142, 143
who designed the designer? objection, 388–94
Wigner, Eugene, 141, 276
Wilkins, Maurice, 58, 69, 70, 136–37
Williams, George, 15, 17
Wistar Institute symposium (1966), 204–13
Woese, Carl, 296
Wöhler, Friedrich, 33–34, 36, 40, 50
Wolfe, Stephen, 123
Wolf, Yuri, 306
Wolpert, David, 291
worldview (Weltanschuung): idealism, 37; material reality, 36–37; matter-firstvs. mind-first, 37–38; naturalism or materialism, 37, 41–42; theism, 37
Yockey, Hubert, 17, 110, 255, 257, 368–69
Yucatan Peninsula impact crater, 158

Zamecnik, Paul, 119Acknowledgments

I would like to thank my editor, Roger Freet, at HarperOne for his expert
guidance and patience and for permitting this manuscript to bear a big burdenof proof. I’d also like to acknowledge the Harper production staff, particularly
Lisa Zuniga and Ann Moru, for their professionalism and exquisite attention
to detail. I would also like to thank my agent, Giles Anderson, for believing inthis project and for connecting me to the good people at Harper.
This book was extensively reviewed for scientific and technical accuracy.
For their work reviewing chapters I’d like to acknowledge: Doug Axe, Bruce
Gordon, Anne Gauger, William Dembski, Tony Mega, Dean Kenyon, RobertMarks, Richard Sternberg, Jonathan Wells, Paul Nelson, and Alistair Noble.
I’d also like to acknowledge Jonathan Witt and David Klinghoffer for
their coaching, editing, and help in framing the narrative structure of thebook, and Bruce Chapman and Logan Gage for their careful editing of latechapter drafts. Thanks to John West for his willingness to assume additionalburdens of management and for creating space for me to write, and to Janet
Oberembt for her invaluable assistance in proofreading, entering sources, andmanaging chapter drafts, the bibliography, and the overall flow ofcommunication. I’d also like express my gratitude to Joseph Condeelis for his
inspired animation work.
I’d like to express my deep gratitude to my parents, Chuck and Pat Meyer,
for their encouragement and support of my scientific education andphilosophical interests from my earliest years.
And finally, I’d like to thank Elaine—who has held so much together
during the last two years—for her courage as well as her love, and forpartnership in this adventure from the beginning.

About the Author
DR. STEPHEN C. MEYER received his Ph.D. from the University ofCambridge in the philosophy of science. A former geophysicist and college
professor, he now directs the Center for Science and Culture at the Discovery
Institute in Seattle. In 2004, Meyer ignited a firestorm of media and scientificcontroversy when a biology journal at the Smithsonian Institution publishedhis peer-reviewed scientific article advancing intelligent design. Meyer has
been featured on national television and radio programs, including The
NewsHour with Jim Lehrer, CBS’s Sunday Morning, NBC’s Nightly News,
ABC’s World News, Good Morning America, Nightline, FOX News Live, andthe Tavis Smiley show on PBS. He has also been featured in two New YorkTimes front-page stories and has garnered attention in other top-national
media.
Visit www.AuthorTracker.com for exclusive information on your favorite
HarperCollins author.

Advance Praise for Signature in the Cell:“Meyer demolishes the materialist superstition at the core of evolutionary
biology by exposing its Achilles’ heel: its utter blindness to the origins of
information. With the recognition that cells function as fast as supercomputers
and as fruitfully as so many factories, the case for a mindless cosmos
collapses. His refutation of Richard Dawkins will have all the dogs barking
and the angels singing.”—George Gilder, author of Wealth and Poverty and Telecosm“A ‘must read’ for all serious students of the origin-of-life debate. Not only is
it a comprehensive defense of the theory of intelligent design, it is a lucid andrigorous exposition of the various dimensions of the scientific method.
Students of chemistry and biology at all levels—high school, undergraduate,or postgraduate—will find much to challenge their thinking in this book.”—Alastair Noble, Ph.D., chemistry, former BBC education officer and
Her Majesty’s Inspector of Schools for Science, Scotland“The origin of life remains one of the great unsolved mysteries of modern
science. Looking beyond the biochemistry of the problem and focusing
instead on the origin and information content of the ‘code of life,’ Meyer has
written an eminently readable and engaging account of the quest to solve thismystery. I recommend this book to laypeople and accomplished professionals
alike.”—Edward Peltzer, Ph.D., ocean chemistry, from Scripps Institution of
Oceanography“How does an intelligent person become a proponent of intelligent design?
Anyone who stereotypes IDers as antiscientific ideologues or fundamentalists
should read Dr. Meyer’s compelling intellectual memoir.”—Dr. Marvin Olasky, provost, The King’s College, New York City,and editor-in-chief, World“In this engaging narrative, Meyer demonstrates what I as a chemist havelong suspected: undirected chemical processes cannot produce the exquisite
complexity of the living cell. Meyer also shows compelling positive evidence
for intelligent design in the digital code stored in the cell’s DNA. A decisivecase based upon breathtaking and cutting-edge science.”—Dr. Philip S. Skell, National Academy of Sciences and Evan Pugh
Professor, emeritus, at Pennsylvania State University

“In Signature in the Cell, Stephen C. Meyer gives us a fascinating explorationof the case for intelligent design theory, woven skillfully around a compelling
account of Meyer’s own journey. Along the way, Meyer effectively dispels
the most pernicious caricatures: that intelligent design is simply warmed-overcreationism, the province of deluded fools and morons, or a dangerous
political conspiracy. Whether you believe intelligent design is true or false,
Signature in the Cell is a must-read book.”—Dr. Scott Turner, professor of environmental and forest biology,
State University of New York, and author of The Tinkerer’s
Accomplice: How Design Emerges from Life Itself“Signature in the Cell is at once a philosophical history of how information
has come to be central to cutting-edge research in biology today and one
man’s intellectual journey to the conclusion that intelligent design provides
the best explanation for that fact. In his own modest and accessible way,
Meyer has provided no less than a blueprint for twenty-first-century
biological science—one that decisively shifts the discipline’s center of gravity
from nineteenth-century Darwinian preoccupations with fossils and fieldstudies to the computerized, lab-based molecular genetics that underwrites theincreasingly technological turn in the life sciences. After this book, readers
will wonder whether anything more than sentimentality lies behind thecontinued association of Darwin’s name with ‘modern biology.’”—Dr. Steve Fuller, professor of sociology of science, University of
Warwick, and author of Dissent from Descent“The astonishing complexities of DNA have raised questions which the rulingscientific orthodoxy cannot begin to answer. As one of the scientists arguingfor ‘intelligent design’ as the crucial missing link in our understanding of how
life came to be, Steve Meyer guides us lucidly through that labyrinth ofquestions opened by discoveries in molecular biology on the frontier ofscientific knowledge.”—Christopher Booker, The Sunday TelegraphCredits
Cover design: www.levanfisherdesign.com
Cover photo: Magictorch/Photographer’s Choice RF/Getty Images
Illustrations © 2009 Ray BraunCopyright

SIGNATURE IN THE CELL: DNA and the Evidence for Intelligent Design.

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