A bacterium in a chip

After the completion of the human genome project it was realized that listing a genome can only be a first step to understanding life. The living cell contains an extraordinary number of interlocking chemical processes that regulate genes and control their expression. In the post-genomics era biology has been trying to come to grips with this problem. In 2007 Craig Venter and Hamilton Smith made public their analysis that a complete understanding of the biochemistry of a cell would allow them to create an entirely artificial bacterium.

The model organism on which a lot of attention was focussed, because it contains the smallest number of genes, is the disease-causing bacterium Mycoplasma genitalium. Stepping stones along the path to understanding this cell were the artificial synthesis of its genome in 2008, and now the complete modeling of its biochemistry in a computer program, reported in the journal Cell:

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.

This paper was published on July 20, 2012 and is signed by a collaborative team of 9 authors affiliated to Stanford University and the J. Craig Venter Institute.