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; lobal optimization eneralized Mass Action (GMA) etabolic engineering (2) posing the optimization task as a non-convexnonlinear 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 ourmethod 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 comm even in cases where the gap.