A newton cooperative genetic algorithm method for in silico optimization of metabolic pathway production

• Published in: PloS one Vol. 10; no. 5; p. e0126199
• Main Authors: Ismail, Mohd Arfian, Deris, Safaai, Mohamad, Mohd Saberi, Abdullah, Afnizanfaizal
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 11-05-2015; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial intelligence; Bioinformatics; Biotechnology; Cell metabolism; Chemical reactions; Chromosomes; Computer Simulation; Evolutionary algorithms; Genetic algorithms; Genetic engineering; Linear programming; Mathematical models; Metabolic Networks and Pathways; Metabolic pathways; Metabolism; Methods; Models, Biological; Models, Theoretical; Molecular biology; Nonlinear equations; Nonlinear programming; Optimization; Physiological aspects; Researchers; Saccharomyces cerevisiae; Software engineering; System theory; Variables; Yeast; Malaysia
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0126199

This paper presents an in silico optimization method of metabolic pathway production. The metabolic pathway can be represented by a mathematical model known as the generalized mass action model, which leads to a complex nonlinear equations system. The optimization process becomes difficult when steady state and the constraints of the components in the metabolic pathway are involved. To deal with this situation, this paper presents an in silico optimization method, namely the Newton Cooperative Genetic Algorithm (NCGA). The NCGA used Newton method in dealing with the metabolic pathway, and then integrated genetic algorithm and cooperative co-evolutionary algorithm. The proposed method was experimentally applied on the benchmark metabolic pathways, and the results showed that the NCGA achieved better results compared to the existing methods.