Identifying a Gene Knockout Strategy Using a Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis to Enhance the Production of Succinate and Lactate in Escherichia Coli

Abstract

In recent years, metabolic engineering hasgained central attention in numerousfields of science because of its capability to manipulate metabolic pathways in enhancing the expression of target phenotypes. Due to this, many computational approaches that perform genetic manipulation have been developed in the computational biology field. In metabolic engineering, conventional methods have been utilized to upgrade the generation of lactate and succinate in E. coli, although the yields produced are usually way below their theoretical maxima. Toovercome the drawbacks of such conventional methods, development of hybrid algorithm is introduced to obtain an optimal solution by proposing a gene knockout strategy in E. coli which is able to improve the production of lactate and succinate. The objective function of the hybrid algorithm is optimized using aswarm intelligence optimization algorithm and aSimple Constrained Artificial Bee Colony (SCABC) algorithm. The resultsmaximize the productionof lactate and succinate by resembling the gene knockout in E. coli. TheFlux Balance Analysis (FBA) is integrated in ahybrid algorithm to evaluate the growth rate of E. coli as well as theproductions of lactate and succinate. This results inthe identification of agene knockout list that contributes to maximizing theproduction of lactate and succinate in E. coli.