Article
DOI: https://doi.org/10.56315/PSCF3-23Isaac

Randy Isaac

Rethinking Abiogenesis:
Part III, Meaning in the Light ofComplexity, Information, andSimplification
Randy Isaac and Stephen Freeland

Stephen FreelandThis article concludes a three-part series on abiogenesis that suggests meaningful configurations of organic components can arise spontaneously, leading to organisms thatsuccessfully survive and reproduce. While the complexity of the disorganized abiotic
world makes it appear to some that such a feat of organization would require the nonnatural intervention of an intelligent mind (including but not limited to supernaturalintervention), we suggest that careful considerations of thermodynamics and information lead to the plausibility of a natural occurrence. No specific, complete pathway forabiogenesis has yet been identified, but the fundamental processes that enable such apath can be articulated more clearly now than ever before as a result of new and continuing research in multiple disciplines. This concluding article centers on the way inwhich complex states of molecular organic information can be generated to produce themost meaningful configurations.
Keywords: Abiogenesis, origin of life, information, complexity, simplification, evolution, systems
chemistry, natural selectionT

he quest to understand the origin oflife may be as ancient as humanity
itself. With little physical evidence
to guide understanding, most early cultures evolved narratives of origins thatreflected the interaction of a spiritual
world with our physical universe.1 The
biblical account of creation forms thebasis for the dominant narrative of origins
in the western world. The interpretationof that account varies widely from a literalinterpretation of a specific English translation to a deeply metaphorical perspective.

Randy Isaac is Executive Director Emeritus of the American ScientificAffiliation. He holds a PhD in physics from the University of Illinois at
Urbana-Champaign. His career was 28 years at IBM Research where hebecame Research Vice-President of Systems, Science, and Technology.
Stephen Freeland (PhD, Cambridge University) is professor of biology,
director of the Interdisciplinary Studies Program, and associate dean ofundergraduate academic affairs at the University of Maryland, Baltimore
County. His research uses computers to study life’s early evolution,
especially the evolution of the amino acid alphabet with which organisms
construct genetically encoded proteins.
16

One particular dichotomy of assumptions about the transition from nonlife tolife concerns us here. On the one hand,
many are convinced that the biblicalaccount reflects one or more miraculous
occurrences that do not conform to any
discernible scientific process.2 On theother hand, many think that God createdthe universe to enable a natural transition from a nonliving universe to a world
teeming with an amazing diversity of
species.3 Scientific observations do not,as yet, provide compelling evidence for
irrefutably distinguishing between thesetwo perspectives. While God is certainly
capable of using any means of God’s
choosing for the creation of life, it is theconviction of the authors that it is demonstrably worthwhile to explore plausible
naturalistic paths by which God may
have enabled the appearance of life from
nonlife.

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Randy Isaac and Stephen Freeland

In Part I, we argued that it may be useful, evenimportant, to perceive the origin of life as a seamlessly continuous (and arguably incomplete) process.
This contrasts with a more traditional view that
abiogenesis refers to a specific point in time or evolutionary history.4
In Part II, we proposed that life may be considered asa simplification of the nonliving universe, such thatthe beginning of life (abiogenesis) may be usefully
perceived as a reduction of complexity from the randomness of the prebiotic world.5 This stands in sharp
contrast to a more typical view of perceiving life as
an increase in complexity from simpler molecules.
Certainly, the intuitively awesome intricacy of life
as we know it is remarkable, especially comparedwith what seems to some a less interesting, nonliving
universe. And yet, the perspective of asking about
increasing complexity (e.g., through synthetic chemistry or artificial life) has not yet yielded a plausible,
comprehensive pathway from nonlife to life. Thisdifficulty leads some to conclude that a supernatural agent or cause must have been necessary for theappearance of life.6
In this Part III, we delve deeper into definitions of
information, complexity, and meaning so as to offer
more explicit precision about how simplification in
the prebiotic world enables the possibility of a natural pathway for abiogenesis.
In the first section of this article, we explore the concepts of complexity and meaning, comparing themwith the concept of information. Next, we seek toshow that in at least some clear sense(s) of the word,
simplification through natural selection is as muchan inherent aspect of the evolutionary process as
increasing complexity. By turning next to consider
abiogenesis explicitly, we conclude by arguing why
it may be instructive to consider the importance ofsimplification in the prebiotic world in the sense offorming a relatively stable and more ordered structure from a disorganized, complicated environment.

The Complicated Concept ofComplexity as It Relates toInformation and Meaning

There is no single, universal definition of complexity
within science,7 and that important fact is a foundation for the more nuanced ideas we seek to introduce
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here. Put another way, a range of possible interpretations must be placed into context before careful
discussion may proceed. Such discussion can helpfully consider complexity in comparison with two
related concepts, namely information and meaning. In
what follows, we explain from where we derive thefollowing brief distinctions:
Information is a measure of the uncertainty in any
system that can exist in a number of different
states. The uncertainty is resolved when the stateof the system is determined.8 The amount of information reflects the number and probabilities ofall possible alternative states. This is often called“Shannon information.” Unless specified otherwise, in this article, the word “information” willrefer to Shannon information.
Complexity refers to the minimum work required todescribe a particular state of information.9 Stateswith a higher diversity of components are more
complex than those with lower diversity because
the former are more difficult to describe.
Meaning, from the perspective of information theory,is the value or usefulness of a particular state of
information.10 It may, for example, refer to matching a preconceived pattern or blueprint, conveying knowledge or an idea, or causing a particularbiological function.11 Meaning is often determinedby an “interpreter” that exhibits the usefulness.12
This definition of meaning overlaps with what,
in common usage, we often call “information,”(e.g., “Detectives have received information leading to a number of arrests”). This overlap offers
ample opportunity for confusion, leading Claude
Shannon and Warren Weaver to caution against
confusing the two.13
To illustrate the definitions given above, their overlap and their differences, let us consider the popular
game Wordle.14 The Wordle website selects a secretfive-letter word that the player seeks to determine
by a series of guesses. After each guess, the website
responds by identifying which letters in the guessoccur in the secret word and whether or not they
are in the right location. The player wins if the secretword is determined in six or fewer guesses. Wordle
helps us illustrate the way in which context is essential to all of the three concepts under consideration(information, complexity, and meaning).

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Rethinking Abiogenesis: Part III, Meaning in the Light of Complexity, Information, and Simplification
Beginning with information, we may determine howmuch is contained in each secret word of this game.
The answer depends on a number of assumptions insetting up the game.
1. Of which language will we use the alphabet(e.g., English, French, Ancient Greek)?
2. Are special symbols allowed (such as accents,
hyphens, or spaces)?
3. Can any five-letter sequence be used or mustthe word exist within a specified dictionary?
In light of these assumptions, it becomes clear thatthe amount of information depends on the context.
In our word game, if we assume that we use the contemporary English alphabet with no special symbols
and it need not be a word, then there would exist 265
possibilities. The amount of information is 23.5 bits.15
But if we restrict the combinations of letters to thosethat comprise a 5-letter word, then there exist only
12,478 possible words16 and the amount of information is reduced to 13.6 bits. There is less information
because of interdependence between the letters.
Sequence matters. For example, if the first letter is a“b,” then the second letter is most likely a vowel or
one of only a few consonants. Many consonants havezero probability of occurring. Because there are fewer
possible states, the amount of information decreases
as well as the complexity, but the capacity for meaning is maintained or increased. Other changes to ourassumptions can significantly change the amount of
information. Increases could come from choosing toallow hyphenated words or choosing to recognize
the typeface, font size, or font color. Anything thatcan be different may be employed in an information
system. For this reason, context is essential.
Once the context is fixed, it is possible to physicallychange both the amount and the state of information.
A physical change in the sequence of the letters in
the example of Wordle changes not only the word,
or state of information but potentially the amount of
information as well. In general, any physical energy
flux (i.e., input or output of energy) in a system can
modify and increase or decrease information.17
Complexity, in contrast, deals with a single, specificstate of the system. One of the most common metrics for complexity is “Kolmogorov complexity.”18
This measures the number of bits of the shortest possible lines of code that a universal coding machine(e.g., a Turing machine) would need to describe the18state of a system. This is the primary type of complexity used in algorithmic information theory.19
It can be thought of as measuring the diversity ofthe specific information states of the system. But
Kolmogorov complexity, like information, depends
on the assumptions being used. Like information, thecomplexity of a given sequence of letters decreases
if we know ahead of time that the letters must forma word in a specified language, since only a few ofthe letters may be needed to determine the rest. Moregenerally then, a repetitive sequence is less complex
than one that is all random.
Turning to considering meaning brings new challenges to thinking clearly about both information andcomplexity. In Wordle, the secret word is restricted
to the set of words preselected by the author of thegame. In contrast to the case of a random set of fiveletters, relationships between the letters and theirsequences are important. If there is a high degree ofrepetition of letters, not only is the complexity low,
but there is less likelihood of having a meaning. A
random set of letters with no pattern, on the other
hand, will have high complexity while also having a
low likelihood of meaning. In the intermediate range
where there is some degree of order, the complexity
is medium, but the potential for having a meaning ishigher.
Warren Weaver, one of the earliest scientists/mathematicians to address complexity, recognized the roleof order in complexity in 1947.20 He suggested a primary distinction between two types of complexity:
disorganized complexity and organized complexity.
Disorganized complexity occurs wherever a system
involves minimal order between components, suchas a collection of letters for which there is no particular sequence. Organized complexity occurs wherever
the ordering between elements of the system is very
important; for example, if those same letters occur ina sequence, such that one ordering produces a word
whereas another produces a nonsense anagram ofthat word, then there is organized complexity.
To measure complexity that accounts for organization within the system, Charles Bennett proposed a
related measure called “logical depth.”21 It attempted
to quantify the role of meaning, or “message value”
as he put it. The logical depth can be thought of as
the time required, rather than the minimum size, fora universal computing machine to compute a string
Perspectives on Science and Christian Faith

Randy Isaac and Stephen Freelandof elements for the Kolmogorov ­complexity. Morerecently, Terrence Deacon and Spyridon Koutroufinissought to offset shortcomings of “logical depth” byproposing a measure of “dynamical depth.”22 They
seek to account for nested interacting organizationlevels rather than just the structural complexity.
When the presence of meaning, or message value, ispart of the context for considering information, we
need to think carefully about what kind of meaning
is relevant. Stephen Meyer stresses the importanceof distinguishing between meaning that conveys “apiece of knowledge known by a person” versus “asequence of characters or arrangements of somethingthat produce a specific effect” and that “it is also
necessary to distinguish Shannon information frominformation that performs a function or conveys ameaning.”23 The latter is typically called “specifiedcomplex information.”
For our discussion of abiogenesis and evolution,
the difference between these two aspects of meaning is crucial. While conveying a piece of knowledge
requires an intelligent agent as an interpreter, producing a specific effect or function is different. That
which enables an organism to survive and reproduce
is a self-sustaining interpretation or meaning createdbetween an organism and its environment, for which
no intelligent receiver or interpreter is required.24
This point may be clarified by comparing the wordgame we have been discussing with human genetics.
When one player thinks of a five-letter word, they
may write it down with pencil and paper, in whichcase the meaning has been transformed from one
medium to another through the agency of an intelligent mind. A dictionary might pass superficially for a
non-intelligent agent that validates this meaning, but
considered more carefully, it is merely documentation of the abstract relationships to which intelligent
minds have previously agreed.
In contrast, the human genome contains a segment ofDNA, called a “gene,” that codes for a specific protein.25 A suite of molecular machinery, coordinatedby the ribosome, translates this sequence of nucleotides (i.e., the gene) into a corresponding sequence ofamino acids, which link together to form a protein.
More accurately, the protein is a complex, 3-dimensional shape formed when the linked sequence ofamino acids folds up spontaneously, because theshape (of the folded protein) yields both structure
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and function.26 Protein sequences with meanings(functions) that tend to help the genome achieve successful replication are, by definition, those which“reward” their corresponding genes to flow forward
in time and increase in frequency. Such functions
often involve catalyzing specific (bio)chemical reactions to occur faster than they would otherwise.
In this sense, we may say that the genome has anembodied meaning to build proteins. But there is noneed or process for the meaning of the protein (orgenome) to involve matching any predetermined
specification. A theologically and philosophicallyinteresting discussion about meaning must look far
deeper than a limited view of genomes (or proteins)
creating meaning: it must instead explore whether
and how one might perceive an intelligent agency
causing and sustaining the fundamental physics
of the universe. In the words of Loren Haarsma, in
a 1995 letter on behalf of the American ScientificAffiliation to the National Association of Biology
Teachers,
While each of these mechanisms can be modeled
as a purely natural process, this does not tell us
whether the entire evolutionary process is ultimately supervised or unsupervised. That question
goes beyond the realm of science, into philosophy
and religion.27

To illustrate the difference, we note that some haveargued a role for abstract interpretation within thegenetic code by which genes are translated into proteins.28 From the perspective of natural science, here
we find nothing more than a straightforward chainof cause and effect. Translation involves the interpretation of a nucleotide gene sequence by molecularmachinery (mainly tRNA molecules coordinated bya ribosome) as a set of mini-sequences, each of them
3 nucleotides in length. Each mini-sequence is known
as a codon; every possible codon that can be constructed from an “alphabet” of 4 nucleotides (43 = 64)means a specific amino acid, so that the set of possible codons and their meanings together compose
an elaborate genetic code.29 Like the example withan intelligent mind, genetic information is transformed from one physical medium (nucleotides/gene) to another (amino acids/protein). But unlike
the example with an intelligent mind, the meaningof the gene and protein sequence requires no abstract
interpretation. Abstract interpretations by humanresearchers may, however, see deeper by moving
to other disciplinary perspectives, but in doing so,
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Rethinking Abiogenesis: Part III, Meaning in the Light of Complexity, Information, and Simplification
they change assumptions as they move to arguments
about meaning of the material universe, and theylose any unique need for direct, intelligent agency
within molecular biochemistry or life’s origins.
To summarize the core concepts from which we now
proceed, information, complexity, and meaning are allcontextual and depend on the assumptions and thevariables being assessed. Information reflects theuncertainty arising from the number of different possible states of the system while complexity reflects
the effort required to describe the structure, relative
ordering, and dynamic interaction of a particularstate of the system. A particular state of the system
may have meaning either by conforming to a predesignated (abstract) state or by causing a specificphysical effect.

Complexity and Simplification in the
Evolution of Living Organisms

Before turning to consider the origin of life, it is
helpful to see first how the concepts of information,complexity, and meaning apply to changes overtime in the biological world that we observe today.
For this part of the discussion, our focus will be thechange in complexity over one generation of a population, from birth until these offspring produce thenext generation.
Let the collective set of genomic and epigenomic
information of all members of a given generation, g0,
be characterized by a reference complexity, c0. Once
that population undergoes a reproductive cycle,
there will be a new collective set of information of
the offspring generation, g1, that is characterized bycomplexity, c1. Usually, this new set of information
will be more complex (c1 > c0). Typical causes for thisincreased complexity include increased population
size (since the number of offspring is often greaterthan the number of parents) and increased genetic
diversity (as mutations of all kinds create new variations of genetic material30).
By most measures of complexity, more individuals
and/or more genetic diversity represent an increasein the total complexity of genomic and epigenomic
information of the next generation. This increase
is, however, temporary. As the offspring generation, g1, progresses through its life cycle, the effects(changes in function) of different genetic variations
20

become manifest. Members of that population carrying variations which produce functions that are lesswell adapted to their environments will contributeless, by reproduction, to the next generation: at anextreme, their carriers may die before reproduction.
In this way, the complexity of the information of g1
reduces over time, from fertilization until the second
offspring generation, g2, is produced (c1at time of reproduction < c1at time g1 is created). The complexity, c2, of g2
is, again, greater than c1 (c2at time g2 is created > c1at timeof reproduction). Generation 2 undergoes simplification, though, depending on the degree and type of
adaptation; the ultimate complexity, c2, at time of
reproduction may be greater than that of g1 at timeof reproduction (c2at time of reproduction of g2 > ~c1at timeof reproduction of g1).
A detailed example that illustrates this concept is presented in box 1. Within this life cycle, the complexity
reduction phase is a simplification through selective
elimination, commonly called “natural selection.”
But in the terminology of information theory, theselection process of simplification is effectively afeedback mechanism that injects meaning into thesystem. It identifies what genomic information ismost capable of persisting in a given environment.
Thus, nature itself provides a go/no-go decision on
the subset of genomic information (the sequence ofgenetic chemical structures) which continues to thenext reproductive cycle. More broadly, organizedcomplexity may gradually increase by iteration ofcomplexification and simplification. Nature explores
a wider and more complex set of configurations ineach generation and the competition for resources(“survival of the fittest”) results in finding a set thathas good persistence.
From within the perspective of the natural sciences,the meaning of the sequence and configuration of the
genome and epigenome is the ability of the organism to survive and reproduce. This ability reflects therelationship between function and environment. The
result of repeatedly iterating a sequence of complexification and simplification can result in an increase inorganized complexity. Complexification occurs naturally in the interaction between the environment andthe reproducing organisms. Simplification occurs
naturally in the ability to survive and reproduce.
This is the core process of evolution by natural selection that Charles Darwin proposed in the nineteenth
century.
Perspectives on Science and Christian Faith

Randy Isaac and Stephen FreelandBox 1: Example of Simplification in a Generation of Humans
Consider a generation, g0, consisting of a theoretical population of 10,000 humans, half of which are men and half
are women, all of whom remain alive during their reproductive years. This generation comprises 10,000 uniquegenomes and is characterized by a complexity, c0. There is a spectrum of differences between these genomes
and some theoretical reference genome that characterizes the entire population. According to the Population
Reference Bureau’s 2021 World Population Data Sheet, the global total fertility rate of women was 2.3 births per
woman, down from 3.2 in 1990.1 Assuming a value of 3 for our example, we would expect 15,000 births to occur
in this theoretical g0. It is estimated that approximately 30–40% of all conceptions result in miscarriage.2 Taking
1/3 as an average number, we would expect a total of 22,500 conceptions to have taken place. This means thatthe 10,000 people in g0 would produce 22,500 fertilized eggs comprising the offspring generation, g1. This set ofgenomes has greater complexity, c1at time of conception, than that, c0, of the genomes of g0 for two primary reasons.
One is the larger number of genomes. The other is that each genome of g1 differs from every other genome in g1
due to crossover in gamete formation and mutations such as SNP’s, HGT’s, and retroviruses.3
It is further estimated that approximately half of the 30–40% miscarriage rate is due to some type of chromosomal
abnormality. This most likely corresponds to significant harmful deviations from a viable genomic sequence. The
miscarriages of 7,500 fertilized eggs in our example is the first and largest selection process that eliminates the
genomes that cannot survive. Following birth, the infant mortality, currently approximately 0.9%, partially reflects
genomic structures that are less able to survive. Additional selection occurs through an approximately 10% infertility rate. The population of g1 that is able to reproduce to create the second offspring generation, g2, would be
about 13,365. Thus, we have a simplification of the population of g1 from 22,500, c1at time of conception, to 13,365,
c1at time of birth. This simplification process produces a set of g1 genomes that may contain individuals with more
complexity than those of g0, in which the more complex changes enable better survival in the slightly modified
environment of g1 compared with g0.
For humans, as with most species that reproduce sexually, there is an additional cycle of complexification andsimplification in the pre-fertilization phase. On average, a man produces on the order of a trillion (1012) sperm in
his lifetime. Each sperm contains a gamete with a single set of chromosomes formed through meiosis. A woman
is born with about a million (106) ovarian follicles that potentially could become mature eggs, of which there areultimately about 500 in a lifetime. Each follicle contains a gamete that similarly has a single set of chromosomes
formed through meiosis. The set of sperm from each man and follicles from each woman comprises a vast complexification from their respective genomic sequences. The dominant process of simplification is the selection ofwhich sperm and which egg will mature and be able, given the opportunity, to produce a fertilized egg. This meansthat each fertilized egg is selected from about 1018 potential combinations, each with a genomic sequence that did
not exist before and, unless selected, will never occur again. While the numbers are different for each species,
the process is essentially the same—a combinatorial complexification with mutations from which a very few areselected; this is effectively a simplification that can be expressed by the relation c1potential gamete combinations >> c1fertilization attempts >> c1at time of conception >> c1at time of birth > c1at time of reproduction. This simplification of g1 leads to a c1at timeof reproduction that may be greater than the c0at time of reproduction of g0.

Notes

Population Reference Bureau, “Impact of COVID-19 Pandemic on Global Birth and Death Rates Unclear, with Many Countries Lacking
Reliable Data,” August 17, 2021, accessed November 16, 2021, https://www.prb.org/news/2021-world-population-data-sheet-released/.
2
Department of Gynecology and Obstetrics, The Johns Hopkins University School of Medicine, Baltimore, Maryland, The Johns
Hopkins Manual of Gynecology and Obstetrics, 4th edition (New York: Lippincott Williams & Wilkins, 2012), 438–39.
3
Ultimately the source of all genetic variation is mutation. Although introductory textbooks might distinguish genetic crossover (if dealing
with sexual reproduction between diploid organisms that undergo meiosis) from mutations (e.g., Single Nucleotide Polymorphisms,
insertions, and deletions) and even exotic events which involve the addition or deletion of genetic letters through horizontal genetransfer or the action of retroviruses, none of these would introduce new information had not mutation acted somewhere in theirevolutionary history.
1

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Rethinking Abiogenesis: Part III, Meaning in the Light of Complexity, Information, and Simplification

Life as Simplification of the
Nonliving Universe

Having first introduced information, complexity, andmeaning, then discussed how these concepts apply
to evolution by natural selection, we now turn to asking how far this framework of understanding can go
in describing a plausible pathway to the emergenceof life within a nonliving universe: abiogenesis.
The Russian physical chemist Ilya Prigogine pioneered a field of study in statistical mechanics,
identifying a category of systems he called “dissipative structures.” Prigogine received the 1977 Nobel
Prize in Chemistry31 for his work on these systems
which are thermodynamic systems that exist in astate far from equilibrium.32 For example, a harboron the ocean is a body of water in equilibrium at
sea level. A lake in the mountains is far from equilibrium due to its gravitational potential energy. In
the language of thermodynamics, this lake would
be called “a metastable state” because it is stable,
but at a higher state of energy than the global equilibrium (sea level). When containment of the lake isbreached, water rushes toward sea level, exhibiting
behavior very different from that of water in equilibrium. As long as meteorological conditions sustain acycle of ocean evaporation and condensation in themountains to replenish the lake, the system of rushing water, converting potential energy to kinetic
energy, will be a steady state of flow which can becalled “a dissipative structure.” Prigogine’s insightwas that there exist physical dissipative systems far
from equilibrium that self-organize into metastablestates which may exhibit lower entropy.

In his chapters on thermodynamics in The Mystery ofLife’s Origin, Walter Bradley’s critique was that nothing here could speak meaningfully to life’s origins
because dissipative systems fail to account for theconfigurational work needed to assemble life.33 Workis needed to bring order from chaos: to find, fromwithin a vast entropy, a specific configuration of molecules that reproduces and evolves. Bradley’s specificfocus was on the component organic molecules thatmust be assembled in a particular configuration(sequence) out of nearly countless possibilities.
In 2022, we may address the criticism by placing
Prigogine’s ideas into an interdisciplinary sandwich
formed by subsequent work in two related (butdistinguishable) subfields (fig. 1). On one side lie
developments in non-equilibrium physics; on theother side lie developments in systems chemistry.
Combined with the foundations laid by Prigogine,
this body of theory argues for the relevance ofexactly the sort of experiments in organic synthetic
chemistry that we find currently at the leading edgeof abiogenesis research.
From physics, the pioneering work of Jeremy
England shows how thermodynamics allows non­living systems to transition to lower states of entropy
in the presence of an energy source: a cyclical driving force can modify a system in remarkable waysthat simply cannot happen at equilibrium. In more
precise, technical terms, non-equilibrium thermodynamics can enable an open system (i.e., one thatis absorbing a flux of energy) to move to any of alarge number of metastable states, some of which are
likely to have lower entropy.34 Metastable states withlower entropy might well have a shorter lifetime,

Figure 1. Three overlapping areas of research have emerged since the late twentieth century to explain how matter can self-organize into
states which exhibit organized complexity and lower entropy.

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Randy Isaac and Stephen Freeland

but they may also modify the system’s response to
the incoming energy flux. Modifications which dissipate energy more efficiently can stabilize the lower
entropy state. Writing about all this for a lay audience, England uses his Jewish faith to notice and use
the metaphor of the bush which Moses saw burning,
without becoming consumed: contrary to our lived
experience, within this vision it is the very act ofburning which stabilizes the persistence of the bush.
Indeed, the centrality of this metaphor to his work
comes across from the title of his book: Every Life Is
on Fire.

the water vapor to higher land where the cooler aircauses the water to condense as rainfall in mountainous regions. Gravity compels the water to find its
way back to the ocean. Before any riverbed existed,
the paths for water to flow down were varied andcomplex. Those paths that were most favorable
underwent the greatest erosion, gradually increasing
their ability to facilitate the flow of water. Eventually
the most favorable path became a riverbed, establishing a river with dynamic kinetic stability as a resultof a process of simplification. (By the way, there wasno sense in which an intelligent agent was required
to identify this optimal or near optimal path to theocean).

In more precise, technical language England’s
research team has studied simple physical systems
to which an oscillating energy source is applied. One
study modeled 20 idealized particles that make andbreak catch-bond springs35 with each other,36 anothersimulated spin glasses responding to time-driven
external fields.37 In all cases, the system showed thecapability (which may appear to some as agency) torestructure itself into a new steady state that alters
how further energy is received. The spectrum ofwork absorption can shift to either increased or
decreased energy absorption, and the direction ofthis shift depends upon the specific parameters ofthe driving force relative to the spring characteristics.
Of particular interest, when a large concentration ofelements is repeatedly exposed to cyclical energy
sources, it will find the most efficient configurations
for maintaining metastable states. In other words, a
simplification process will eliminate those states thatare least stable and select a state that persists in thepresence of that driving force.

Pross argues that while natural selection as we know
it in the biological world cannot act in the prebiotic
world, there is an equivalent process of “kinetic selection.” He points to work by Sol Spiegelman39 andGerald Joyce40 to show how competitive exclusion
operates in the chemical world of organic systems
just as it does in natural selection. Building on thatperspective, Pross goes on to show that the biologicalconcept of fitness is the same principle as DKS in theprebiotic world. This is consistent with our perspective on continuity in the origin of life. If one looks
backward from a biological perspective, it is hard tosee reproduction with variation and survival of thefittest occurring in the prebiotic world. But if one
looks forward from the competitive world of organic
chemical reactions, the principles of kinetic selection
and DKS can be seen as the basis for natural selection
and fitness in the far more complex world of livingorganisms.

Complementing these foundations in physics wefind the systems chemistry of Addy Pross, and specifically the concept of “dynamic kinetic stability”(DKS) by which non-equilibrium, open systems cansustain metastable states.38 Living organisms aremetastable systems that are more efficient than nonliving metastable systems in utilizing energy flux tosustain their existence, and are therefore favored todevelop. Pross illustrated DKS with the example ofa river. A river is stable even though the water molecules that compose a river are constantly moving andchanging (no man can cross the same river twice!).
The river has dynamic kinetic stability. As described
previously, it is a metastable, non-equilibrium system that is sustained by energy flux. Warmed by thesun, water in the ocean evaporates. Winds convey

But back to our major theme: we might re-express
the shifts to new steady states of energy absorption
shown by England and colleagues, in the language ofsystems chemistry, as examples of dynamic kinetic
stability. Thus, recent physics tells us that nonliving
systems can transition into lower states of entropy inthe presence of an energy source, particularly a cyclical driving force. Physical chemistry tells us that low
entropy states can exist as stable states far from equilibrium so long as they dissipate energy into entropy.
Systems chemistry tells us that non-equilibrium,
open systems can be sustained in metastable states.
From this theoretical framework in physics andchemistry (fig. 1), we finally may turn to consider
explicitly those studying the sort of organic synthetic
chemistry which produces molecular biochemistry.

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Rethinking Abiogenesis: Part III, Meaning in the Light of Complexity, Information, and Simplification

Turning to Abiogenesis

The research community tackling abiogenesis comprises many different approaches and disciplines.
One useful way to sketch a map of this sprawling
frontier is to distinguish those who choose to work
“top down” and those who work “bottom up.”41
Here “bottom up” research focuses on the chemistry
and physics of the nonliving universe, often asking what processes and conditions are conducive toforming the two classes of biological polymer thatare central to life as we know it: nucleic acid andprotein. “Top down” research comprises those whowork backwards toward the prebiotic world fromthese central facts of “modern” (post-LUCA) biology: genes, proteins, and the molecular machinerythat translates the former into the latter. Within thisschema, at the interface where top down meets bottom up, lies a subcommunity of researchers who
have explored for decades the extent to which “wet/dry cycles” can cause monomeric building blocks—
amino acids and nucleotides—to join together intopolymers: RNA and protein (DNA is thought to havearisen later as a derivative of RNA).

Wet/dry cycles, as the name implies, refer to an
environment in which watery conditions alternatewith periods of evaporation/drying. In various
incarnations,42 this environment has been pictured as
the shoreline of a primordial ocean, streams that flow
ephemerally after rain, springs or pools of water withdrying edges, or even their opposite counterpart ofgeothermal fields where water sinks into the crustand dries as it encounters ever hotter depths.43 What
unites all such specific instances is that the organic
chemistry of an aqueous solution shifts back andforth with the different organic chemistry that takes
place as water is removed from the system.
The regime of wet/dry cycles was first conceived
as one conducive to the formation of biopolymers
simply because the chemical reaction which forms
both nucleic acids from nucleotides, and proteinsfrom amino acids, involves the removal of watermolecules (see fig. 2).44 Simply put, the relevantmonomers form under a range of different chemistries and pathways within an aqueous solution, andthe subsequent removal of water favors these monomers further reacting together to form polymers.

Figure 2. The chemical reactions by which life’s chemical building blocks form into polymers involve the removal of water molecules.
(A) nucheotides link together through a phosphodiester linkage into sequences of RNA; (B) amino acids link together through peptide
bonds to form proteins.

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Randy Isaac and Stephen Freeland

As such, the concept of wet/dry cycling has been
applied over the decades to study every aspect of
biopolymer formation, from the “dehydration condensation” reaction at the heart of polymerization,45
to processes involving spontaneous self-purificationof the resulting polymers46—it even offers anotherpathway towards monomer formation.47 But from an
initial motivation of chemistry (seek a process thatfavors the removal of water molecules) it is interesting to note how pioneers of this approach haveincreasingly come to absorb and reflect the maturingphysics and chemistry of non-equilibrium systems.
We may, for example, illustrate how seamlessly anddirectly wet/dry cycling research meshes with all wehave written about how meaningful complexity is
generated. To do so, let us consider the specific casestudy of Bruce Damer and David Deamer who have
explored iterative wet/dry cycles around pools fed
by hot springs.48 They present the rationale for theirapproach as follows.
Energy-driven cycles are central to life’s ability
to maintain itself in a far-from-equilibrium stateagainst the trend toward ever increasing entropy.
Therefore, it is reasonable to consider the possibility that life’s origin also depended on cycles …
Significantly, cycling also drives a series of naturalexperiments that undergo combinatorial selectionin the form of encapsulated polymers.49

They go on to examine in detail a variety ofenergy-driven cycles. One is the distillation process resulting from evaporation and condensation.
Another is the alternate drying and hydration ofpools. They describe the process this way:
Hydrothermal pools undergo wet-dry cycles resulting from precipitation and fluctuating waterlevels … Polymers are synthesized by condensation reactions occurring within these dried films… During the hydration phase, vesicles bud
off, ­
encapsulating systems of polymers to form
proto­cells … This process generates random setsof polymers captured in vesicles to form vast
numbers of protocells. Frequent cycling of these
populations initiates the combinatorial selection
process that drives chemical evolution and enables
the emergence of ever more robust protocells. As
the protocells continue to undergo the stresses ofcycling, most will be disrupted, their components
leaking out or dispersing through disrupted membranes, but a rare few are likely to contain polymers
that enhance their survival … The products ofVolume 75, Number 1, March 2023s­ election that cycling systems generate can lead tothe stepwise emergence of increasingly functional
polymers …”50

Note that the “combinatorial selection process” towhich they refer is directly equivalent to the configurational entropy work that Walter Bradley identified
as missing from the body of thought developed by
Prigogine.
It remains to be seen how fruitful Damer andDeamer’s particular scenario will be in showing theplausibility of abiogenesis. Other researchers explore
how life began with the formation of RNA,51 or RNA
fragments,52 or RNA-like fragments.53 We, along with
Damer and Deamer, would argue however that thisgradual dissipation over time of strong claims for“RNA first” within the scientific literature has muchfurther to go, and would look for energy flux to produce a sequence of chemistries that lead onward
toward RNA. Rather than RNA first, we would
hypothesize RNA itself as an evolutionary outcome.
For the purposes of this manuscript, it matters little.
Whichever molecule or starting point is considered,a perceived increase in complexity comes only fromlimiting one’s focus to the evolving genetic content.
But genetic, hereditary material is changing overtime in response to the environment, and once webroaden our vision to embrace this broader system,
we can perceive the genetic material as a simplified
representation of the environment. In this light, thework of Damer and Deamer illustrates how someof the earliest steps might have involved a drying,aqueous environment favoring monomers to join
together. A dimer looks more complex than two
monomers, but is (from our perspective) a simplified, coded reflection of the environment.
Indeed, the words of Damer and Deamer express, ina different disciplinary language, the ideas of nonequilibrium physics from England. We might, forexample, re-express Damer and Deamer’s ideas assuggesting that energy flux from various sources,
such as the cyclical driving force from the sun,seasons, tides, etc., provides the iterative complexification/simplification process that is inherent in the
reproduction with variation/natural selection cycleof true biological evolution. In non-equilibrium systems, an increase in energy enables the system to
explore a complex set of states with higher energy,
many of which may have lower entropy. A dissipation in energy allows the system to settle into
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Rethinking Abiogenesis: Part III, Meaning in the Light of Complexity, Information, and Simplification
a metastable state which may be a lower entropy
simplification from the higher energy complexity.
Those metastable states that are capable of persisting(Pross’s DKS) modulate the system response to thedriving force (England’s non-equilibrium physics),
and thereby are those which evolve into primitive
precursors of life. The local environment provides
the information necessary to guide which polymer
sequences persist and multiply. This has created thespecified complexity we find in gene sequences. The
gene sequence is an encoding of the environment,
and the encoding is simpler than the full environment in all its dimensions. As successive rounds of
selection proceed, the encoding may grow in scope
but always as a fraction of the information which it
now reflects. There is no need for an intelligent interpreter at any point.

Conclusion

Within this series on “Rethinking Abiogenesis,” wehave described ideas traditionally presented from aperspective and language of evolutionary biology
in the different disciplinary language of informationand complexity theory, in order to show how they
integrate in a fully consistent, broader frameworkof scientific research, including the leading edge of
abiogenesis research.
Part I of this series on abiogenesis emphasized thecontinuity in time of the transition from nonlife tolife.54 That continuity is exemplified in our inability
to offer a clear demarcation between nonlife and life.
Part II expanded that view to consider continuity inspace with a close connection between life and theexternal environment.55 It was suggested that thisconnection is helpful to perceive life as simplification,
whereby the complexity inherent to an environmentis incorporated over time into a biological structurethat is increasingly robust within the environment atthat time. In this sense, life is a reflection of its environment—and reflections contain less information
than that which they reflect.
Here in Part III, by considering carefully the definition of complexity, we discussed in detail the cyclicalprocess of complexification and simplification that
produces this reflection. The prebiotic world is highin random, disorganized information with a wide
diversity of elements and molecules, both organicand inorganic. Given an environment with multiple high-energy driving forces (whether incoming
26

radiation from the sun on a prebiotic shoreline,
or dissipation of interior planetary energy from ahydrothermal vent), a process of self-organization
can occur that is effectively one of simplification.
Configurations that are simpler and more orderly
than the random environment are possible when
they modify the system response to the energy flux.
Configurations that are more stable have an enduring existence (persistence) that forms the basis forfurther cycles of complexification and simplification.
This can be perceived as a forerunner to the processof reproduction with heritable variation that is ubiquitous to evolution by natural selection, as sciencecurrently understands that phrase within biology.
Implicitly, this perspective suggests that abiogenesis
should not be sought in the immediate, spontaneous
assembly of structures seen within modern life, butrather, in the process which led to such structures
over time. In this explicit sense, we suggest that for
research into the origins of life, it may be instructive
to consider the importance of simplification in theprebiotic world in the sense of forming a relatively
stable and more ordered structure from a highly random environment.
Our account of the prominent role of a cycle of complexification and simplification in abiogenesis claims
no novel process or mechanism. Rather, it uses thelanguage of information and complexity theory todescribe the familiar concept of differential reproductive success in evolutionary theory. While thechallenge of understanding abiogenesis remains far
from being resolved, we suggest that this accountteaches us that a naturalistic origin of life cannot be
ruled out and merits further study. We suggest thatreaders of this journal recognize that the mainstream
scientific community studying the origin of life pursues naturalistic abiogenesis, not primarily becauseof a bias against supernatural intervention, but
because of its potential plausibility.
☼

Acknowledgment

We gratefully acknowledge the helpful and thoughtful suggestions by two reviewers and are gratefulto ASA member Larry Funck for pointing us to thework of Addy Pross.

Notes

Philip Freund, Myths of Creation (New York: Washington
Square Press, 1965).

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Randy Isaac and Stephen Freeland
Several examples include the following: Young-Earth
Creationism—Dr. Terry Mortenson, “Young-Earth Creationist View Summarized and Defended,” in Answers in
Depth, February 16, 2011, https://answersingenesis.org/creationism/young-earth/young-earth-creationist-view-summarized-and-defended/; Reasons to Believe Model(aka Old Earth Creationism or Progressive Creationism)—
Hugh Ross, “Summary of Reasons to Believe’s Testable
Creation Model,” Reasons to Believe, updated March 1,
2022, https://reasons.org/explore/publications/articles/summary-of-reasons-to-believes-testable-creation-model-1; and Intelligent Design—“What Is Intelligent Design?,” accessed January 16, 2023, https://
intelligentdesign.org/whatisid/.
3
Evolutionary Creation aka Theistic Evolution—https://biologos.org/common-questions/what-is-evolutionary-creation.
4
Emily Boring, J. B. Stump, and Stephen Freeland, “Rethinking Abiogenesis: Part 1, Continuity of Life through Time,”
Perspectives on Science and Christian Faith 72, no. 1 (2020):
25–35, https://www.asa3.org/ASA/PSCF/2020/PSCF3-20BoringStumpFreeland.pdf.
5
Emily Boring, Randy Isaac, and Stephen Freeland,“Rethinking Abiogenesis: Part II, Life as a Simplificationof the Nonliving Universe,” PSCF 73, no. 2 (2021): 100–
113, https://www.asa3.org/ASA/PSCF/2021/PSCF6-21
BoringIsaacFreeland.pdf.
6
In a 2010 survey of American Scientific Affiliation members, while 60.2% accepted compelling evidence ofevolutionary development for all life from a common
ancestral form, only 39.6% accepted a form of abiogenesis. “ASA Survey on Origins: Final Results June 1, 2010,”
https://cdn.ymaws.com/network.asa3.org/resource/resmgr/OriginsResults.pdf.
7Charles H. Bennett stated, “… the problem of defining
complexity is itself complex, and there are many satisfactory definitions of different kinds of complexity” in“Complexity in the Universe,” in Physical Origins of Time
Asymmetry, ed. J. J. Halliwell, J. Pérez-Mercader, andW. H. Zurek (Cambridge, UK: Cambridge University
Press, 1996), 33–46.
8
Claude Shannon, “A Mathematical Theory of Communication,” in The Bell System Technical Journal 27, no. 3 (July
1948): 379–423, https://doi.org/10.1002/j.1538-7305.1948.tb01338.x.
9
Called Kolmogorov-Solomonoff-Chaitin complexity, thisdefinition can be traced back to A. N. Kolmogoroff, “On
Tables of Random Numbers,” Theoretical Computer Science 207, no. 2 (November 6, 1998): 387–95, https://doi.org/10.1016/S0304-3975(98)00075-9, which is a reprint ofKolmogorov’s original Russian paper in 1960.
10
Charles H. Bennett, “Logical Depth and Physical Complexity,” in The Universal Turing Machine—A Half-Century
Survey, ed. Rolf Herken (New York: Oxford University
Press, 1988), 227–57.
11Francis Crick, “On Protein Synthesis,” in Symposia of
the Society of Experimental Biology 12 (Cambridge, UK:
Cambridge University Press, 1958), 138–63; and Sahotra
Sarkar, “Decoding ‘Coding’: Information and DNA,”
BioScience 46, no. 11 (1996): 857–64, https://doi.org/10.2307/1312971.
12Peter R. Wills, “Genetic Information, Physical Interpreters
and Thermodynamics; The Material-Informatic Basis of
Biosemiosis,” Biosemiotics 7, no. 1 (2014): 141–65, https://doi.org/10.1007/s12304-013-9196-2.
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Volume 75, Number 1, March 2023

Claude Shannon and Warren Weaver, The Mathematical
Theory of Communication (Urbana, IL: University of Illinois
Press, 1949), 8.
14
In 2021, Josh Wardle introduced a word game called
Wordle, now owned by the New York Times, https://nytimeswordle.io/. It is similar to games like MasterMind
and Bulls and Cows in the information sense.
15
The equation I = –i=N
i=1 pi log2 pi is for the amount of information where N is the number of states in which a system
can be, pi is the probability that the system is in state i, andI is given in number of bits, from Shannon and Weaver,
The Mathematical Theory of Communication.
16
List of All Five Letter Words, accessed August 20, 2022,
https://www.bestwordlist.com/5letterwords.htm. This
number depends on the specific dictionary used and theextent to which uncommon words are excluded.
17
Here, and throughout the discussion that follows, we use
the word “system” to refer to any collection of objects thatcomprises focus of attention, from atoms to individuals
within a population. Every possible physical state of a system, including its very existence, constitutes information,
but we can choose to confine our usage and analysis to amuch more convenient subset of possible states.
18
Peter Grünwald and Paul Vitányi, “Shannon Information and Kolmogorov Complexity,” arXiv:cs/0410002v1,
August 8, 2005, https://arxiv.org/pdf/cs/0410002.pdf;
https://arxiv.org/abs/cs/0410002; https://doi.org/10.48550/arXiv.cs/0410002.
19
Ray Solomonoff, A Preliminary Report on a General
Theory of Inductive Inference (US Air Force, Office of Scientific Research, February 4, 1960), https://std.com/~rjs/rayfeb60.pdf. Revision published November 1960,
http://raysolomonoff.com/publications/z138.pdf.
20
Warren Weaver suggested that the two main types ofcomplexity were disorganized and organized complexity. Warren Weaver, “Science and Complexity,” American
Scientist 36, no. 4 (1948): 536–44, https://www.jstor.org/stable/27826254.
21Charles H. Bennett, “Information, Dissipation, and theDefinition of Organization” and “Dissipation, Information, Computational Complexity and the Definition ofOrganization,” in Emerging Syntheses in Science, ed. David
Pines (Santa Fe, NM: The Santa Fe Institute Press, 1985),
297–313.
22
Terrence Deacon and Spyridon Koutroufinis, “Complexity and Dynamical Depth,” Information 5, no. 3 (2014):
404–23, https://doi.org/10.3390/info5030404.
23
Stephen C. Meyer, Signature in the Cell: DNA and the Evidence for Intelligent Design (New York: HarperOne, 2009),
91.
24
Randy Isaac, “The Significance of The Mystery of Life’s
Origin,” Perspectives on Science and Christian Faith 73,
no. 3 (2021): 158–62, https://www.asa3.org/ASA/PSCF/2021/PSCF9-21Isaac.pdf.
25
George W. Beadle, Edward L. Tatum, and Joshua Lederberg, The Nobel Prize in Physiology or Medicine 1958,
https://www.nobelprize.org/prizes/medicine/1958/summary/. See also George W. Beadle and Edward
L. Tatum, “Genetic Control of Biochemical Reactions in
Neurospora,” Proceedings of the National Academy of Sciences 27, no. 11 (1941): 499–506, https://doi.org/10.1073/pnas.27.11.499.
26
Christian B. Anfinsen, Stanford Moore, and William H.
Stein, The Nobel Prize in Chemistry 1972, https://www.nobelprize.org/prizes/chemistry/1972/summary/. See
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Rethinking Abiogenesis: Part III, Meaning in the Light of Complexity, Information, and Simplification
also Christian B. Anfinsen, “Principles That Govern the
Folding of Protein Chains,” Science 181, no. 4096 (1973):
223–30, https://doi.org/10.1126/science.181.4096.223.
27
Loren Haarsma, “Letter to the National Association of
Biology Teachers,” November 22, 1996, https://www.asa3.org/ASA/resources/Haarsma1996.pdf.
28
Meyer, Signature in the Cell, 91.
29
Har Gobind Khorana, The Nobel Prize in Physiology or
Medicine 1968, https://www.nobelprize.org/prizes/medicine/1968/summary/.
30
Ultimately the source of all genetic variation is mutation. Although introductory textbooks might distinguish
genetic crossover (if dealing with sexual reproduction
between diploid organisms that undergo meiosis) frommutations (single nucleotide polymorphisms, insertions
and deletions) and even exotic events which involve theaddition or deletion of genetic letters through horizontal
gene transfers or the action of retroviruses, none of thesewould introduce new information had not mutation acted
somewhere in their evolutionary history.
31
Ilya Prigogine—Facts, https://www.nobelprize.org/prizes/chemistry/1977/prigogine/facts/.
32
See some of his work in Ilya Prigogine and Isabelle
Stengers, Order out of Chaos: Man’s New Dialogue with
Nature (Toronto, ON: Bantam Books Canada, 1984); Gregoire Nicolis and Ilya Prigogine, Exploring Complexity(New York: W. H. Freeman and Company, St. Martin’s
Press, 1989); Gregoire Nicolis and Ilya Prigogine, SelfOrganization in Nonequilibrium Systems: From Dissipative
Structures to Order through Fluctuations (Hoboken, NJ: John
Wiley & Sons, 1977); and William C. Schieve and Peter M.
Allen, editors, Self-Organization and Dissipative Structures:
Applications in the Physical and Social Sciences (Austin, TX:
University of Texas Press, 1982).
33
Charles Thaxton, Walter Bradley, and Roger Olsen, The
Mystery of Life’s Origin: Reassessing Current Theories (NewYork: Philosophical Library, 1984).
34
Jeremy England, Every Life Is on Fire: How Thermodynamics Explains the Origins of Living Things (New York: Basic
Books, 2020).
35
Catch-bond springs have increased bond lifetime whenmechanically stretched. See Micah Dembo et al., “TheReaction-Limited Kinetics of Membrane-to-Surface Adhesion and Detachment,” Proceedings of the Royal Society of
London Series B Biological Sciences 234, no. 1274 (1988): 55–83,
https://doi.org/10.1098/rspb.1988.0038; and Yuriy V.
Pereverzev et al., “The Two-Pathway Model for the CatchSlip Transition in Biological Adhesion,” Biophysical Journal
89, no. 3 (2005): 1446–54, https://doi.org/10.1529/biophysj.105.062158.
36
Tal Kachman, Jeremy A. Owen, and Jeremy L. England,“Self-Organized Resonance during Search of a Diverse
Chemical Space,” Physical Review Letters 119, no. 3(2017): 038001, https://doi.org/10.1103/PhysRevLett.119.038001.
37
Jacob M. Gold and Jeremy L. England, “Self-Organized Novelty Detection in Driven Spin Glasses,”arXiv:1911.07216v1, November 17, 2019, https://doi.org/10.48550/arXiv.1911.07216.
38
Addy Pross, What Is Life? How Chemistry Becomes Biology(Oxford, UK: Oxford University Press, 2012), chapter 7.
39
D. R. Mills, R. L. Peterson, and S. Spiegelman, “An Extracellular Darwinian Experiment with a Self-Duplicating
Nucleic Acid Molecule,” PNAS 58, no. 1 (July 1, 1967):
217–24, https://doi.org/10.1073/pnas.58.1.217.
28

Sarah B. Voytek and Gerald F. Joyce, “Niche Partitioning in the Coevolution of 2 Distinct RNA Enzymes,” in
PNAS 106, no. 19 (May 12, 2009): 7780–85, https://doi.org/10.1073/pnas.0903397106.
41
H. James Cleaves, The Origins of Life: A Review of Scientific
Inquiry (West Conshohocken, PA: John Templeton Foundation, 2020), https://www.templeton.org/wp-content/uploads/2021/07/JTF_Origins_of_Life_Final.pdf.
42
N. Lahav and D. H. White, “A Possible Role of Fluctuating Clay-Water Systems in the Production of Ordered
Prebiotic Oligomers,” Journal of Molecular Evolution 16,
no. 1 (September 1980): 11–21, https://doi.org/10.1007/BF01732066.
43
Thomas D. Campbell et al., “Prebiotic Condensation
through Wet-Dry Cycling Regulated by Deliquescence,”
Nature Communications 10 (2019): article number 4508,
https://doi.org/10.1038/s41467-019-11834-1.
44
N. Lahav and S. Chang, “The Possible Role of Solid Surface
Area in Condensation Reactions during Chemical Evolution: Reevaluation,” Journal of Molecular Evolution 8, no. 4(1976): 357–80, https://doi.org/10.1007/BF01739261.
45
Ben K. D. Pearce et al., “Origin of the RNA World: The
Fate of Nucleobases in Warm Little Ponds,” Proceedingsof the National Academy of Sciences USA 114, no. 43 (2017):
11327–32, https://doi.org/10.1073/pnas.1710339114.
46
Jay G. Forsythe et al., “Ester-Mediated Amide Bond Formation Driven by Wet-Dry Cycles: A Possible Path to
Polypeptides on the Prebiotic Earth,” Angewandte Chemie 54, no. 34 (2015): 9871–75, https://doi.org/10.1002/anie.201503792.
47
Sidney Becker et al., “Wet-dry Cycles Enable the Parallel
Origin of Canonical and Non-Canonical Nucleosides by
Continuous Synthesis,” Nature Communications 9 (2018):
article 163, https://doi.org/10.1038/s41467-017-02639-1.
48
Bruce Damer and David Deamer, “The Hot Spring Hypothesis for an Origin of Life,” Astrobiology 20, no. 4 (2020):
429–52, https://www.liebertpub.com/doi/10.1089/ast.2019.2045.
49
Ibid., 435–36.
50
Ibid., 441.
51
Michael P. Robertson and Gerald F. Joyce, “The Originsof the RNA World,” Cold Spring Harbor Perspectives in Biology 4, no. 5 (2012): article 003608, https://doi.org/10.1101/cshperspect.a003608.
52
Paul G. Higgs and Niles Lehman, “The RNA World:
Molecular Cooperation at the Origins of Life,” Nature
Reviews Genetics 16, no. 1 (2015): 7–17, https://doi.org/10.1038/nrg3841.
53
Nicholas V. Hud et al., “The Origin of RNA and ‘My Grandfather’s Axe,’” Chemistry & Biology 20, no. 4 (Apr 18 2013):
466–74, https://doi.org/10.1016/j.chembiol.2013.03.012.
54
Boring, Stump, and Freeland, “Rethinking Abiogenesis:
Part 1, Continuity of Life through Time.”
55
Boring, Isaac, and Freeland, “Rethinking Abiogenesis:
Part II, Life as a Simplification of the Nonliving Universe.”
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Perspectives on Science and Christian Faith