Bringing the Genome to Life: The Role of Computing

Author: Dr. Aristides Patrinos

Co-Author: Dr. Michael Colvin

**Introduction**

It has been widely observed that the life sciences are on the verge of a
great transformation from a qualitative, data-poor, experiment-driven
discipline into a quantitative data-rich science in which predictive
simulations guide and interpret experiments. Comparisons are often drawn
to similar transformations of physics and chemistry early in the last
century, fields in which comprehensive theoretical frameworks often
allow predictive simulations of experimental outcomes. Indeed, the last
few decades have seen tremendous progress in both the scope and depth of
biological understanding. New technologies are allowing the collection
of comprehensive biological data sets, including full genome sequences
and large scale protein structure collections. Moreover, many biological
processes are beginning to be understood at the molecular level, so that
first principles methods developed originally for chemical simulations
can increasingly be applied to biology.

Despite these stunning and rapid advancements, it is easy to
underestimate the challenges to achieving a truly predictive biology.
Although the complete genome sequence is now available for a growing
list of species, much about even the simplest organisms is terra
incognita. For example, at least a third of the proteins in *e. coli*
have no known function, and we have complete quantitative data about
virtually no cellular processes. Further, at the level of organisms,
cells or biochemical pathways there are no overarching theories to
provide a context for new experimental data.

Overcoming these barriers to achieve predictive understanding of living
systems will require the focused efforts of many different disciplines.
The new DOE Genomes to Life (GtL) program represents one such effort in
which a combination of large-scale biological facilities and focused
multidisciplinary research teams will be applied to achieve a
comprehensive knowledge of microbes and microbial communities relevant
to DOE missions in environmental cleanup, energy production, and global
climate change. In addition to achieving these goals, we hope that this
program will be a prototype for a "new biology" in which high-throughput
facilities provide comprehensive, quantitative datasets to enable
predictive models of biological function.

Computations and simulation are a central aspect of this "new biology".
Computers already have important roles in biology, primarily in two
distinct areas: bioinformatics and biomolecular simulations. Although
these two areas are often lumped together under the name "computational
biology", they are very distinct in both their goals and methods.
Bioinformatics involves the computations required to assemble, annotate,
and analyze DNA sequences, protein structures and other high-throughput
biological data. Biomolecular simulations apply chemical modeling
methods to biochemical phenomena. Additionally, several new areas of
computational biology are just emerging, such as chemical kinetics
models of metabolic and regulatory pathways, and reaction-diffusion
models of transport within and between cells. These newer areas are
primarily limited by the availability of accurate biophysical data, but
will rapidly grow in importance as more and better data become
available.

**Biological Data Management**

A characteristic feature of the new approaches to biology is that they
generate vast amounts of raw data that must be analyzed, annotated and
archived before it is of use to bioscience researchers. Originally,
these large data sets were primarily DNA sequence data and significant
progress has been made in methods to assemble, annotate and distribute
DNA sequence data. Nevertheless, there remain significant research
challenges in DNA sequence analysis, including whole genome comparisons
and higher-level genome annotation. For example, at this time many genes
found in new genomic sequences do not have assigned functions. Some
functions can be inferred by comparison to other genome sequences or
computational protein structure prediction, but a wide range of research
problems remain to be solved before reliable and comprehensive protein
function prediction from sequence is possible.

The size and complexity of the data produced in DNA sequencing is
dwarfed by that produced by the myriad of emerging high-throughput
biotechnologies. These new types of data include protein expression
levels, structures of proteins and protein complexes, and spatial and
morphological data at the cell level. Ideally, these diverse data types
would be analyzed to identify its salient features and then combined
with different types of biological data, including information on
structure, expression, and function. The resulting "integrative"
databases will allow researchers to view these data from many points of
reference and identify linkages between otherwise disparate biological
data. Ultimately, such databases could enable simulations at the pathway
or cellular level by transparently assembling and validated the
necessary modeling parameters.

Although many of the necessary computational tools and databases already
exist, considerable research remains to be done in order to develop
reliable, robust, efficient, and widely applicable versions of these
tools. This research will involve new developments in database
technology to manage and analyze the wide range of biological data
types; improved algorithms for genome reconstruction and gene finding;
methods to extend the capability of protein comparative modeling based
on more distant homologies (*e.g.*, sequences with less than 40%
sequence identity); and more accurate and computationally efficient
molecular modeling methods. The investments in these computational tools
are critical---the full potential of the new biology will not be
realized without the investments in information technology needed to
understand the information-rich description of life provided by the
genome programs.

**Biomolecular Simulations**

The ultimate biological models would be atomic level simulations of each
biochemical process. There are many challenges to molecular-level
simulations of biological processes, including the large size of
biomolecules and the long time scales of many biological processes, as
well as the subtle energetics and complex milieu of biochemical
reactions. Moreover, many biochemical reactions occur far from
equilibrium and are regulated by both transport of the reactants and
subsequent processing of the products. Finally, there remains a wide
gulf between the detailed chemical data needed for initiating and
validating biomolecular simulations and the data available on many
biological processes and environments. Despite these challenges, there
is a vast number of biochemical processes for which chemical simulations
will have a major impact on our understanding. These problems include
the elucidation of the energetic factors underlying protein-protein or
protein-DNA interactions and the dissection of the catalytic function of
certain enzymes.

A wide variety of chemical simulation methods are available that vary in
accuracy, scalability, and computational cost. At one extreme are
quantum mechanical methods that can in principle predict any biochemical
property to high accuracy but are in practice computationally limited to
relatively small molecular systems. At the other extreme is classical
molecular mechanics (MD) that simulates the motions of atoms using
simplified ball and spring" force fields with limited accuracy. These
different methods are complementary---for example classical molecular
dynamics can provide information on large-scale conformational changes
in enzymes, while quantum mechanical simulations can determine the
effect of these changes on the catalytic activity of the enzymes. Both
of these methods are extremely computationally costly when applied to
biological problems and therefore continued improvements in computer
speeds and algorithm efficiency are critical to sustained progress in
this area.

The current state-of-the-art in biophysical modeling are the so-called
First Principles Molecular Dynamics (FPMD) methods that simulate the
motions of atoms in biochemical systems using a quantum mechanical
description of the atomic interactions. The FPMD applications have been
highly optimized for DOE's current teraFLOP-speed computers on which
they are capable of simulating up to a few hundred atoms for a few
picoseconds. Because these methods constitute a nearly exact simulation
of nature, even with these computational limitations, it is becoming an
important tool for studying fundamental biochemical processes. The
results for small biochemical systems currently being simulated on
teraFLOP scale computers provide tantalizing glimpses of the value of
longer time and larger system size simulations that will be made
possible with faster computers. The promise of such modeling studies is
rapidly growing as a result of the development of linear-scaling
computational chemical methods and molecular modeling software for
massively parallel computers. One role of biomolecular simulations in
the Genomes to Life program will be to obtain mechanistic information on
specific enzymatic processes critical to understanding unique microbial
behaviors (*e.g.*, metal sequestration, toxin degradation, carbon
fixation, or photosynthesis.) Additionally, molecular modeling will be
used to determine the principles that underlie protein-protein
interactions, and ultimately to predict likely protein binding sites.

**The Path Forward**

In conclusion, there are truly vast prospects for a "new biology" in
which comprehensive, quantitative data sets are assembled into
predictive models that can be used to design solutions to many
environmental and health problems. In some sense this new biology will
be an inevitable outcome of improvements in the technology and methods
of biological research. Nevertheless, the present holds many
opportunities to catalyze the more rapid transformation. First, to more
efficiently develop and apply new experimental and simulation
technologies, large scale projects with visionary goals should be
created that will drive the integration of advanced technologies and
computations with the life sciences. Second, to increase the base of and
multi-agency partnerships should be formed to develop programs to
stimulate research in the mathematical and computational sciences that
enable progress in computational biology. Finally, to conclusively
demonstrate the value of this new biology to the wider bioscience
community, focused research efforts must be fostered that deliver
significant breakthroughs in long-standing research problems. By
applying these strategies in selected biological areas, we hope that the
DOE Genomes to Life program can help blaze the trail to this
tremendously exciting future for the life sciences.