Metabolic fluxes and ensemble modeling
Because cells exist far from chemical equilibrium, a kinetic description of metabolism is required to predict the effects of genetic interventions. Such a kinetic description must start with the fluxes through the major pathways of central carbon metabolism. Although these intracellular metabolic fluxes cannot be measured directly, they can be estimated by feeding carbon sources specifically labeled with 13-C. In these experiments, the labeling patterns of intracellular metabolites will be highly dependent upon the metabolic flux distribution. Labeling patterns may be measured through NMR or mass spectrometry, and these data can then be used with established methods for parameter estimation to approximate the fluxes through the metabolic network.
Although the estimation of metabolic fluxes is a powerful tool for understanding the results of metabolic engineering, the flux distributions themselves are not predictive. We are using Ensemble Modeling to develop predictive kinetic models of central carbon metabolism. In Ensemble Modeling, an initial flux distribution for the strain of interest is estimated using 13-C MFA, and an ensemble of many kinetic models (each with a different set of kinetic parameters) consistent with the measured flux distribution is constructed. The ensemble is then pruned by iterative rounds of genetic intervention followed by estimation of the new flux distribution resulting from the genetic change. After each genetic change, models that predict the new flux distribution are retained while those that do not are discarded. This process can be repeated until only one or a few closely related models remain in the ensemble. The kinetic models generated by this method have the potential to predict the most effective genetic manipulations for engineering strains to overproduce target molecules.
This project is a collaboration with the UCLA and Penn State groups of Drs. Liao and Maranas and aims first at the construction of a comprehensive kinetic model of the bacterium Escherichia coli. Other systems will follow in the near future.