I will present a computational approach for predicting the metabolic response to genetic and environmental perturbations in a cell. The method is based on flux balance analysis (FBA), which computes whole-cell steady state metabolic fluxes associated with optimal performance (e.g. maximal growth, in bacteria) [Schilling]. I will show that the phenotype of a perturbed cell is better approximated by a minimization of metabolic adjustment (MOMA) with respect to the unperturbed state (wild type), rather than by maximization of growth rate [Segre]. Our method can be used to predict the effects of metabolic perturbations for environmental and biomedical applications. The integration of flux balance methods with stochastic models, originally developed for the study of prebiotic molecular self-organization, may help uncover general principles of biological systems.
Christophe Schilling et al, "Genome-Scale metabolic model of Helicobacter pylori 26695", J. Bacteriology, 184:4582-93, 2002. PubMed.
Daniel Segre', Dennis Vitkup and George Church, "Analysis of optimality in natural and perturbed networks", PNAS, 99:15112-7, 2002. PDF.