Programming playground: A whole-cell computational model

Three days ago, Jonathan R. Karr, Jayodita C. Sanghvi and coauthors in Markus W. Covert’s lab published a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium. This is the first model of its kind: they track all biological processes such as DNA replication, RNA transcription and regulation, protein synthesis, metabolism and cell division at the molecular level. To achieve this, the authors integrate 28 different sub-models of the known cellular processes.

Figure 1A from Karr, Sanghvi et. al (2012): A diagram of the 28 sub-models, colored by category: RNA (green), protein (blue), metabolic (orange), DNA (red). The modules are connected by arrow representing common metabolites (orange), RNA (green), proteins (blue), and DNA (red).

The key technical accomplishment was integrating the 28 modulus into a single model. Each module is based on existing models, but different modules are expressed in different paradigms: ODE, Boolean, probabilistic, and constraint-based. For me, this is the most impressive aspect of the work. Usually, when I look at biology (or psychology), I see a mishmash of models with each expressed in its own language and seemingly incompatible with the others. The authors overcame this by assuming the modulus are independent on short timescales (under 1 second). This allows the software to keep track of 16 global cell variables which are used as inputs for the submodulus that are run to simulate 1 second and their results used to update the global variables and repeat the loop. The whole software is available online and the authors can use the data gathered to produce a video of a single cell’s life cycle:

The authors show that the model has a high level of agreement with existing data. They also use the predictions to run several novel real-biology experiments, and even partially overturn (or complete) a previous experimental observation based on hints from their model. In particular they show that disruption of the IpdA gene — which Glass et al. (2006) suggested as non-essential — has severe (but noncritical) impact on cell growth. I wish I could comment more on the validity of the model as judged by experiments, but molecular biology is magic to me.

The simulation results that were most exciting for me was looking at the effects of single-gene disruptions on phenotype. The bacterium Mycoplasma genitalium is a human urogenital parasite whose genome contains 525 genes (Fraser et al., 1995). It is not an easy model organism to work with, but it has the smallest known genome that can constitute a cell. Part of the team on this project, is from J. Craig Venter Institute and has extensive experience with the organism due to their effort to create the first self-replication synthetic life by implanting artificial DNA into Mycoplasma genitalium. I would not be surprised if this model plays a vital part in the institute’s engineering.

Karr, Sanghvi et al. (2012) ran simulations of each of the 525 possible single-gene disruption strains. They found that 284 genes were essential to sustain growth and division and 117 are non-essential — a 79% agreement with the experimental results of Glass et al. (2006). Of particular interest for me was that in some cases it took more than one generation for specific proteins to fall to lethal levels. As far as I understand this is because when a single-cell divides, daughters get both a copy of the mother DNA and have their initial levels of proteins and RNA set to within statistical fluctuations of those of their mother. Due to my complete lack of basic biological background, this seemed an interesting example of Lamarkian evolution. In particular, it raises questions on how to best combine single-cell learning and evolution. From a naive Bayesian model of learning, it would seem that this would allow cells to pass on their priors — a biological evolution counterpart to Beppu & Griffiths (2009) cultural ratchet.

The detail of the whole-cell model is impressive. I hope that the software becomes a tool for theorists without access to a wet-lab to play around with cells. The approach is an antithesis to the simple and completely unrealistic models I am accustomed to building. For me, it raises many thoughts on how to better think about the distinction between genotype and phenotype that is almost always ignored in evolutionary game theory. For now the whole-cell model is computationally too expensive for me to build evolutionary dynamics from it, but maybe parts of the code can be simplified or ignored or maybe we could use more course-grained models. Either way, I am excited for my new playground!

References

Beppu, A., & Griffiths, T. (2009). Iterated learning and the cultural ratchet. Proceedings of the 31st Annual Conference of the Cognitive Science Society, 2089-2094.

Fraser, C.M., Gocayne, J.D., White, O., Adams, M.D., Clayton, R.A., Fleischmann, R.D., Bult, C.J., Kerlavage, A.R., Sutton, G., Kelley, J.M., et al. (1995). The minimal gene complement of Mycoplasma genitalium. Science 270, 397–403.

Glass, J.I., Assad-Garcia, N., Alperovich, N., Yooseph, S., Lewis, M.R., Maruf, M., Hutchison, C.A., Smith, H.O., & Venter, J.C. (2006). Essential genes of a minimal bacterium. Proc. Natl. Acad. Sci. USA 103, 425–430.

Karr, J.R., Sanghvi, J.C., Macklin, D.N., Gutschow, M.V., Jacobs, J.M., Bolival, B., Assad-Garcia, N., Glass, J.I., & Covert, M.W. (2012). A whole-cell computational model predicts phenotype from genotype Cell, 150, 389-401 DOI: 10.1016/j.cell.2012.05.044