The Taub Faculty of Computer Science Events and Talks

Genome-scale metabolic models enable to successfully predict a variety of metabolic phenotype in microorganisms. Still, the integration of metabolic networks with various 'omics' data towards the prediction of metabolic flux remains an open challenge. Here, we show that enzyme kinetic parameters are significantly correlated with measured fluxes in E. coli under various conditions, providing a higher correlation than that achieved by measured gene expression data. Based on the latter, we developed a novel constraint-based modeling method, Enzyme Solvent-Capacity Flux Balance Analysis (ESC-FBA), which predicts cellular metabolic state by utilizing prior data on enzyme turn-over rates and molecular weights. In contrast to previous attempts to predict global metabolic flux by utilizing kinetic constants, our approach thoroughly accounts for enzymatic requirements for catalyzing metabolic flux, considering isozymes, protein complexes, and multi-functional enzymes. ESC-FBA is shown to significantly improve the prediction accuracy of various metabolic phenotypes in E. coli, including growth rate, flux rates, enzyme concentrations and gene expression levels, in comparison with state-of-the art computational approaches. A specifically interesting demonstration of ESC-FBA's applicability involves the prediction of lag-phase length when E. coli is switched between growth media, shedding light on this intriguing biological phenomenon.