The Covert Lab incorporated more than 1,900 experimentally observed parameters into their model of the tiny parasite Mycoplasma genitalium. Image: Erik Jacobsen/Covert Lab |
In a
breakthrough effort for computational biology, the world’s first complete
computer model of an organism has been completed, Stanford University researchers
reported in the journal Cell.
A team
led by Stanford bioengineering Professor Markus Covert used data from more than
900 scientific papers to account for every molecular interaction that takes
place in the life cycle of Mycoplasma genitalium—the world’s smallest
free-living bacterium.
By
encompassing the entirety of an organism in silicon, the paper fulfills a
longstanding goal for the field. Not only does the model allow researchers to
address questions that aren’t practical to examine otherwise, it represents a
stepping-stone toward the use of computer-aided design in bioengineering and
medicine.
“This
achievement demonstrates a transforming approach to answering questions about
fundamental biological processes,” said James M. Anderson, director of the
National Institutes of Health Division of Program Coordination, Planning and
Strategic Initiatives. “Comprehensive computer models of entire cells have
the potential to advance our understanding of cellular function and,
ultimately, to inform new approaches for the diagnosis and treatment of
disease.”
The
research was partially funded by an NIH Director’s Pioneer Award from the
National Institutes of Health Common Fund.
From
information to understanding
Biology over the past two decades has been marked by the rise of
high-throughput studies producing enormous troves of cellular information. A
lack of experimental data is no longer the primary limiting factor for
researchers. Instead, it’s how to make sense of what they already know.
Most
biological experiments, however, still take a reductionist approach to this
vast array of data: knocking out a single gene and seeing what happens.
“Many
of the issues we’re interested in aren’t single-gene problems,” said
Covert. “They’re the complex result of hundreds or thousands of genes
interacting.”
This
situation has resulted in a yawning gap between information and understanding
that can only be addressed by “bringing all of that data into one place
and seeing how it fits together,” according to Stanford bioengineering
graduate student and co-first author Jayodita Sanghvi.
Integrative
computational models clarify data sets whose sheer size would otherwise place
them outside human ken.
“You
don’t really understand how something works until you can reproduce it
yourself,” Sanghvi said.
Small
is beautiful
Mycoplasma genitalium is a humble parasitic bacterium, known mainly for
showing up uninvited in human urogenital and respiratory tracts. But the
pathogen also has the distinction of containing the smallest genome of any
free-living organism—only 525 genes, as opposed to the 4,288 of E. coli,
a more traditional laboratory bacterium.
Despite
the difficulty of working with this sexually transmitted parasite, the
minimalism of its genome has made it the focus of several recent bioengineering
efforts. Notably, these include the J. Craig Venter Institute’s 2009 synthesis
of the first artificial chromosome.
“The
goal hasn’t only been to understand M. genitalium better,” said
co-first author and Stanford biophysics graduate student Jonathan Karr.
“It’s to understand biology generally.”
Even at
this small scale, the quantity of data that the Stanford researchers
incorporated into the virtual cell’s code was enormous. The final model made
use of more than 1,900 experimentally determined parameters.
To
integrate these disparate data points into a unified machine, the researchers
modeled individual biological processes as 28 separate “modules,”
each governed by its own algorithm. These modules then communicated to each
other after every time step, making for a unified whole that closely matched M.
genitalium‘s real-world behavior.
Probing
the silicon cell
The purely computational cell opens up procedures that would be difficult to
perform in an actual organism, as well as opportunities to reexamine
experimental data.
In the
paper, the model is used to demonstrate a number of these approaches, including
detailed investigations of DNA-binding protein dynamics and the identification
of new gene functions.
The
program also allowed the researchers to address aspects of cell behavior that
emerge from vast numbers of interacting factors.
The
researchers had noticed, for instance, that the length of individual stages in
the cell cycle varied from cell to cell, while the length of the overall cycle
was much more consistent. Consulting the model, the researchers hypothesized
that the overall cell cycle’s lack of variation was the result of a built-in
negative feedback mechanism.
Cells
that took longer to begin DNA replication had time to amass a large pool of
free nucleotides. The actual replication step, which uses these nucleotides to
form new DNA strands, then passed relatively quickly. Cells that went through
the initial step quicker, on the other hand, had no nucleotide surplus.
Replication ended up slowing to the rate of nucleotide production.
These
kinds of findings remain hypotheses until they’re confirmed by real-world
experiments, but they promise to accelerate the process of scientific inquiry.
“If
you use a model to guide your experiments, you’re going to discover things
faster. We’ve shown that time and time again,” said Covert.
Bio-CAD
Much of the model’s future promise lies in more applied fields.
CAD—computer-aided
design—has revolutionized fields from aeronautics to civil engineering by
drastically reducing the trial-and-error involved in design. But our incomplete
understanding of even the simplest biological systems has meant that CAD hasn’t
yet found a place in bioengineering.
Computational
models like that of M. genitalium could bring rational design to biology—allowing
not only for computer-guided experimental regimes, but for the wholesale
creation of new microorganisms.
Once
similar models have been devised for more experimentally tractable organisms,
Karr envisions bacteria or yeast specifically designed to mass-produce
pharmaceuticals.
Bio-CAD
could also lead to enticing medical advances—especially in the field of
personalized medicine. But these applications are a long way off, the
researchers said.
“This
is potentially the new Human Genome Project,” Karr said. “It’s going
to take a really large community effort to get close to a human model.”
Source: Stanford University
