Despite their small size and relative simplicity (and M. genitalium is the absolute smallest and simplest single organism there is), things like bacteria are still incredibly complex. Each one of M. genitalium’s 525 genes is responsible for the interactions of DNA, RNA, proteins, metabolites, and 24 other unique categories of molecules, and figuring out just how all of this stuff works together is a monumental task. The results (published last Friday) show that the researchers were able to simulate the complete life cycle of the pathogen from start to finish, including every last gene and every known function.
The reason to do this, beyond the general biological appeal of being able to understand how an organism operates on such a detailed level, is all about disease detection and treatment. With all of these cellular processes mapped out, it becomes possible to model and analyze organisms in the same way that architects and engineers model and analyze buildings and airplanes on a computer before building them in real life: this "bio-CAD" (computer-aided design) could lead to the development of new medicines and bioengineering techniques, and entire laboratories that perform experiments at high speed using only simulation, perhaps resulting in synthetic forms of life custom-made to inexpensively mass-produce complex pharmaceuticals.
To put the amount of data and computation required here into perspective, the simulation was run in a MATLAB environment on a 128 core Linux cluster and took about ten hours to simulate the M. genitalium organism dividing just once. Incidentally, this means that the simulation is operating approximate real-time (the bacteria takes 10 hours or so to naturally complete its life cycle), but remember that this is the most basic organism that there is. Stepping up to a simulation of a more complex (and more useful, medically speaking) organism like E. coli will involve modeling 4,288 genes dividing every 30 minutes, implying much more complex molecular interactions.
Fortunately, computing power is cheap and getting cheaper, and once the hard part (figuring out how all of those genes interact with each other in the first place) is taken care of, finding the hardware to run a simulation on in a reasonable amount of time is simply a matter of money. It should be an easy argument to make: simulating organisms has the potential to unlock the next generation of medicine. To bring the full power of this technique to bear on ourselves is a problem that’s exponentially more complex, but having this proof of concept brings it that much closer to reality.