Bacterium, Simulated: Computer Program Predicts Phenotype from Genotype

A Whole-Cell Computational Model Predicts Phenotype from Genotype

Authors:

1. Jonathan R. Karr (a)
2. Jayodita C. Sanghvi (b)
3. Derek N. Macklin (b)
4. Miriam V. Gutschow (b)
5. Jared M. Jacobs (b)
6. Benjamin Bolival (b)
7. Nacyra Assad-Garcia (c)
8. John I. Glass (c)
9. Markus W. Covert (b, *)

Affiliations:

a. Graduate Program in Biophysics, Stanford University, Stanford, CA 94305, USA

b. Department of Bioengineering, Stanford University, Stanford, CA 94305, USA

c. J. Craig Venter Institute, Rockville, MD 20850, USA

*. Correspondence: mcovert@stanford.edu


Highlights

* An entire organism is modeled in terms of its molecular components

* Complex phenotypes can be modeled by integrating cell processes into a single model

* Unobserved cellular behaviors are predicted by model of M. genitalium

* New biological processes and parameters are predicted by model of M. genitalium

Summary:

Understanding how complex phenotypes arise from individual molecules and their interactions is a primary challenge in biology that computational approaches are poised to tackle. We report a whole-cell computational model of the life cycle of the human pathogen Mycoplasma genitalium that includes all of its molecular components and their interactions. An integrative approach to modeling that combines diverse mathematics enabled the simultaneous inclusion of fundamentally different cellular processes and experimental measurements. Our whole-cell model accounts for all annotated gene functions and was validated against a broad range of data. The model provides insights into many previously unobserved cellular behaviors, including in vivo rates of protein-DNA association and an inverse relationship between the durations of DNA replication initiation and replication. In addition, experimental analysis directed by model predictions identified previously undetected kinetic parameters and biological functions. We conclude that comprehensive whole-cell models can be used to facilitate biological discovery.

Link. Pop Sci write up at the NYT.

First off, this is a VERY first step.  Don't expect a human cell to be simulated very soon.  Ours are far, far more complicated.   Given a full run on our biggest current system using the same methods, you could "only" do something a thousand times more complicated, at best.

Even so, this is something impressive.