Effective Dynamic Models of Metabolic Networks

Abstract

Mathematical models of biochemical networks are the useful tools to understand and ultimately predict how cells utilize nutrients to produce valuable products. Hybrid cybernetic models (HCMs) in combination with elementary modes (EMs) are a tool to model cellular metabolism. However, HCM is limited to reduced metabolic networks because of the computational burden of calculating EMs. In this letter, we develop the hybrid cybernetic modeling with flux balance analysis (HCM-FBA) technique, which uses flux balance solutions instead of EMs to dynamically model metabolism. We show that HCM-FBA has comparable performance to HCM for a proof of concept metabolic network and for a reduced anaerobic E. coli network. Next, HCM-FBA is applied to a larger metabolic network of aerobic E. coli metabolism, which was infeasible for HCM (29 FBA modes versus more than 153 000 EMs). The global sensitivity analysis further reduces the number of FBA modes required to describe the aerobic E. coli data, while maintaining model fit. Thus, HCM-FBA is a promising alternative to HCM for large networks, where the generation of EMs is infeasible.