Improved metaheuristic algorithms for metabolic network optimization

Abstract

Metaheuristic algorithms have been used in various domains to solve the optimization problem. In metabolic engineering, the problem of identifying near-optimal reactions knockout that can optimize the production rate of desired metabolites are hindered by the complexity of the metabolic networks. Through Flux Balance Analysis, different metaheuristics algorithms have been improved to optimize the desired phenotypes. In this paper, a comparative study of four metaheuristic algorithms have been proposed. Differential Search Algorithm (DSA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Genetic Algorithm (GA) are considered. These algorithms are tested on succinic acid production in Escherichia coli. The comparative performances are measured based on production rate, growth rate, and computational time. Hence, from the results, the best metaheuristic algorithms to solve the metabolic network optimization is identified.