Outer approximation-based algorithm for biotechnology studies in systems biology

Optimization methods play a central role in systems biology studies as they can help in identifying key processes that can be experimentally changed so that specific biological goals can be attained. Standard optimization methods used in this field rely on simplified linear models that may fail in capturing the underlying complexity of the target metabolic network. Within this general context, we present a novel approach to globally optimize metabolic networks. The approach presented relies on (1) adopting a general modeling framework for metabolic networks: the Generalized Mass Action (GMA) representation; (2) posing the optimization task as a non-convex nonlinear programming (NLP) problem; and (3) devising an efficient solution method for globally optimizing the resulting NLP that embeds a GMA model of the metabolic network. The capabilities of our method are illustrated through two case studies: the anaerobic fermentation pathway in Saccharomyces cerevisiae and the citric acid production using Aspergillus niger. Numerical results show that the method presented provides near optimal solutions in low CPU times even in cases where the commercial global optimization package BARON fails to close the optimality gap.