Complete modeling of metabolic networks is desirable but difficult for the lack of kinetics. As a step towards this goal, we develop an approach 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. This ensemble allows for the examination of possible phenotypes of the network upon perturbations, such as changes in enzyme expression levels. 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. This approach bypasses the need for detailed characterization of kinetic parameters and arrives at a set of models that describes relevant phenotypes upon enzyme perturbations.

Further, we demonstrate that such an ensemble modeling approach can be used to identify enzyme targets for overexpression for metabolic engineering strain design. We demonstrate the utility of this approach on the pathway to aromatics biosynthesis, succinate production, and lysine biosynthesis, identifying the same enzyme targets to increase production that have been shown in the literature.