Predicting metabolic engineering knockout strategies for chemical production: accounting for competing pathways.

Abstract

Computational modeling in metabolic engineering involves the prediction of genetic manipulations that would lead to optimized microbial strains, maximizing the production rate of chemicals of interest. Various computational methods are based on constraint-based modeling, which enables to anticipate the effect of genetic manipulations on cellular metabolism considering a genome-scale metabolic network. However, current methods do not account for the presence of competing pathways in a metabolic network that may diverge metabolic flux away from producing a required chemical, resulting in lower (or even zero) chemical production rates in reality-making these methods somewhat over optimistic. In this article, we describe a novel constraint-based method called RobustKnock that predicts gene deletion strategies that lead to the over-production of chemicals of interest, by accounting for the presence of competing pathways in the network. We describe results of applying RobustKnock to Escherichia coli's metabolic network towards the production of various chemicals, demonstrating its ability to provide more robust predictions than those obtained via current state-of-the-art methods.