A hybrid of artificial bee colony and flux balance analysis for identifying optimum knockout strategies for producing high yields of lactate in echerichia Coli

The advent of genome-scale models of metabolism has laid the foundation for the development of computational procedures for suggesting genetic manipulations that lead to overproduction. Previously, for increasing the production of Lactate in E. coli, a traditional method of chemical synthesis was being used, this always lead the products are far below their theoretical maximums. This is not surprise as the cellular metabolism is always competing with the chemical overproduction. Besides, several optimization algorithms often get stuck at a local minimum in a multi-modal error. In this research, a hybrid of Artificial Bee Colony (ABC) and Flux Balance Analysis (FBA) is proposed for suggesting gene deletion strategies leading to the overproduction of Lactate in E. coli. In this work, the ABC is introduced as an optimization algorithm based on the intelligent behavior of honey bee swarm. As for the evaluation of fitness part, each mutant strain is evaluated by resorting to the simulation of its phenotype using the FBA, together with the premise that microorganisms have maximized their growth along natural evolution. This is the first research that successfully combined ABC and FBA for identifying optimum knockout strategies. The successfully created hybrid algorithm is applied to the E. coli model dataset.