IdealKnock: A framework for efficiently identifying knockout strategies leading to targeted overproduction

Abstract

In recent years, computer aided redesigning methods based on genome-scale metabolic network models (GEMs) have played important roles in metabolic engineering studies; however, most of these methods are hindered by intractable computing times. In particular, methods that predict knockout strategies leading to overproduction of desired biochemical are generally unable to do high level prediction because the computational time will increase exponentially. In this study, we propose a new framework named IdealKnock, which is able to efficiently evaluate potentials of the production for different biochemical in a system by merely knocking out pathways. In addition, it is also capable of searching knockout strategies when combined with the OptKnock or OptGene framework. Furthermore, unlike other methods, IdealKnock suggests a series of mutants with targeted overproduction, which enables researchers to select the one of greatest interest for experimental validation. By testing the overproduction of a large number of native metabolites, IdealKnock showed its advantage in successfully breaking through the limitation of maximum knockout number in reasonable time and suggesting knockout strategies with better performance than other methods. In addition, gene-reaction relationship is well considered in the proposed framework.

Keywords: Genome-scale metabolic network models; Knockout strategies; OptGene; OptKnock.