A gene knockout strategy for succinate production using a hybrid algorithm of bees algorithm and minimization of metabolic adjustment

Recently, metabolic engineering has gained popularity in the area of system biology due to its potential of capable to increase production. It works by manipulating the genes that can restructure the metabolic networks that have the potential to increase the yield of metabolite production. This metabolic network model has become the foundation for the development of computational procedures to suggest genetic manipulations that eventually leads to optimizing the metabolite production. This research focuses on optimizing the metabolite production of succinate in Escherichia coli. Previous works on rational modelling framework failed in optimizing the metabolite production and tended to suggest unrealistic flux distributions. Hence, in this paper, a hybrid algorithm of Bees Algorithm and Minimization of Metabolic Adjustment (BAMOMA) is proposed to overcome the problems found in the previous works. By developing this hybrid algorithm, it helps to identify a set of gene in Escherichia coli dataset that can be deleted and eventually leads to overproduction of succinate. Experimental results show that BAMOMA is better than previous works in term of production rates.