(12a) Ensemble Modeling - a Novel Tool for Strain Design

Complete modeling of metabolic networks is desirable but difficult for the lack of kinetics. As a step towards this goal, we develop an approach, termed Ensemble Modeling (EM), to build an ensemble of dynamic models which reach the same steady state. The models in the ensemble are based on the same mechanistic framework at the elementary reaction level, including known regulations, and span the space of all kinetics allowable by thermodynamics. Instead of using dynamic metabolite data to fit the model, the EM approach uses phenotypic data (effects of enzyme overexpression or knockouts on the steady state production rate) to screen possible models. The size of the ensemble is reduced by acquiring data for such perturbation phenotypes. If the mechanistic framework is approximately accurate, the ensemble converges to a smaller set of models and becomes more predictive.

We demonstrate the Ensemble Modeling (EM) framework through application to the production of aromatic amino acids in Escherichia coli. By using existing data from the literature for the overexpression of genes coding for transketolase (Tkt), transaldolase (Tal), and phosphoenolpyruvate synthase (Pps) to screen the ensemble, we arrive at a set of models that accurately describes the known enzyme overexpression phenotypes. This subset of models becomes more predictive as additional data are used to refine the models. The final ensemble of models demonstrates the kinetic property that Tkt is the first limiting step, and correctly predicts that only after tkt is overexpressed does an increase in Pps increase the production rate of aromatics. This work demonstrates that ensemble modeling is able to capture the kinetic behavior of aromatic acid producing bacteria and describe resultant phenotypes after enzyme perturbations while bypassing the need for a detailed characterization of kinetic parameters.