A Whole-Cell Computational Model Predicts Phenotype from Genotype

• Jonathan R. Karr^1, 4, 
• Jayodita C. Sanghvi^2, 4, 
• Derek N. Macklin², 
• Miriam V. Gutschow², 
• Jared M. Jacobs², 
• Benjamin Bolival Jr.², 
• Nacyra Assad-Garcia³, 
• John I. Glass³, 
• Markus W. Covert^2, , 

• ¹ Graduate Program in Biophysics, Stanford University, Stanford, CA 94305, USA
• ² Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
• ³ J. Craig Venter Institute, Rockville, MD 20850, USA

• Received 8 March 2012. Revised 20 April 2012. Accepted 14 May 2012. Available online 19 July 2012. Published: July 19

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.