A team led by Markus Covert, assistant professor of bioengineering,
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 silico, 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 2008 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 also 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."
Stanford's Department of Bioengineering is jointly operated by the School of Engineering and the School of Medicine.
(Reuters)
