Gobal optimization of hybrid kinetic/FBA models via outer-approximation

Abstract

Flux balance analysis (FBA) is a linear programming-based framework widely used to predict the behavior, in terms of the resulting flux distribution, of cellular organisms in different media. FBA models are constructed using only stoichiometric information, and for this reason they sometimes fail in predicting fluxes precisely. In this work, we formally define the concept of hybrid FBA/kinetic models, in which kinetic information of key processes is used to tighten the search space of standalone FBA formulations, thereby enhancing their predictive capabilities. This approach leads to non-linear non-convex models that may exhibit multiple local optima. To solve them to global optimality, we use a customized outer-approximation algorithm that exploits the structure of the kinetic equations. Numerical results show that our method enhances the quality of standalone FBA models, providing more accurate predictions.