It might shock you to hear that nobody has ever developed a complete computational model of a living cell. That’s because, despite their diminutive size, their internal processes are extremely complex — but now a team of Stanford engineers has succeeded where others have failed.
It’s no small undertaking. Combining data from over 900 scientific papers, a team led by Professor Markus Covert was able to account for every single molecular interaction which takes place in the world’s smallest free-living bacterium, Mycoplasma genitalium. Once they had that understanding, they were able write code describing those interactions, combining them into a piece of software which computationally replicates exactly how the cell behaves. The results are published in Cell.
While it might sound like an academic exercise, it’s been a long-standing goal of the biological community. Not only does it allow scientists to probe how the cell works in ways never before possible, it also opens up future avenues of research which could use computer-aided design in bioengineering and medicine. James M. Anderson, director of the National Institutes of Health Division of Program Coordination, Planning and Strategic Initiatives explains:
“This achievement demonstrates a transforming approach to answering questions about fundamental biological processes. 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.”
However, this is just the start. The reason it’s been possible to code up a model which describes Mycoplasma genitalium is that it contains the smallest genome of any free-living organism, containing just 525 genes. Compare that to, say, E. coli — a much more commonly studied lab bacterium which has 4288 genes — and the size of the task ahead is obvious.
But to an extent, that’s not the point. The understanding gained from being able to probe Mycoplasma genitalium will help scientists understand the true benefits of computational biology as a whole, accelerating future work. “The goal hasn’t only been to understand M. genitalium better,” explains Jonathan Karr, one of the researchers. “It’s to understand biology generally.” [Cell via Stanford]
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