A mammoth effort has produced a complete computational model of the bacterium Mycoplasma genitalium, opening the door for biological computer-aided design.
In a breakthrough effort for computational biology, the world's first complete computer model of an organism has been completed, Stanford researchers reported last week in the journal Cell.
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.