Toward Whole-Cell Models for Bioengineering

When: Monday, April 22, 2013 at 3:30 pm
Where: DA 5th fl
Speaker: Jonathan Karr
Organization: Graduate Student, Biophysics and Translational Medicine, Stanford University
Sponsor: CCNR

A central challenge in biology is to understand how phenotype arises from genotype. Despite decades of research which have produced vast amounts of biological data, a complete, predictive understanding of biological behavior remains elusive. Computational techniques are needed to assemble the rapidly growing amount of biological data into a unified understanding.

Recently we developed the first comprehensive whole-cell computational model. The model predicts the life cycle dynamics of the Gram-positive bacterium Mycoplasma genitalium from the level of individual molecules and their interactions including its metabolism, transcription, translation, and replication.  We validated the model by broadly comparing its predictions to a wide range ofexperimental data across several biological processes and scales. We have demonstrated that the model can guide biological discovery. We have used the model to determine how the metabolic network controls the M. genitalium cell cycle in the absence of genetically encoded regulators, enumerate the modes and frequency of M. genitalium stochastic death, and determine the kinetic parameters of several M. genitalium metabolic enzymes.

We show that the M. genitalium whole-cell model can also be used to rationally guide biological design. Specifically, we use the model to identify the optimal distribution of gene expression that maximizes the M. genitalium growth rate. We are currently overexpressing several genes in vitro to validate these predictions. We believe this analysis provides valuable insight into biological design and genome optimization.

Furthermore, we believe that gene-complete models will accelerate bioengineering by enabling rapid, low cost in silico experimentation, facilitating experimental design and interpretation, and ultimately guiding rational bioengineering and medicine.