LAUSR

Abstract Genome-scale metabolic network models (GEMs) have been developed and applied successfully in many applications. Among different modeling approaches, flux balance analysis (FBA), a constraint-based method, has attracted significant interest due to its simplicity and effectiveness. Due to the inherent redundancies built into the metabolic network, alternative optimal solutions in general exist in the implementations of FBA. Although mathematical and geometrical meanings of the alternative optima are straightforward, their biological meaning has not been fully understood. In this work, with the E. coli core model as an example, we apply a system identification based framework that we previously developed for GEM analysis to reveal the biological meaning of the alternative optimal solutions to FBA. We show that for the E. coli core model, the alternative solutions are related to each other as different means to achieve the same redox balance, i.e., total NADPH.