title: Differential Bees Flux Balance Analysis with OptKnock for in silico microbial strains optimization.
abstract: Microbial strains optimization for the overproduction of desired phenotype has been a popular topic in recent years. The strains can be optimized through several techniques in the field of genetic engineering. Gene knockout is a genetic engineering technique that can engineer the metabolism of microbial cells with the objective to obtain desirable phenotypes. However, the complexities of the metabolic networks have made the process to identify the effects of genetic modification on the desirable phenotypes challenging. Furthermore, a vast number of reactions in cellular metabolism often lead to the combinatorial problem in obtaining optimal gene deletion strategy. Basically, the size of a genome-scale metabolic model is usually large. As the size of the problem increases, the computation time increases exponentially. In this paper, we propose Differential Bees Flux Balance Analysis (DBFBA) with OptKnock to identify optimal gene knockout strategies for maximizing the production yield of desired phenotypes while sustaining the growth rate. This proposed method functions by improving the performance of a hybrid of Bees Algorithm and Flux Balance Analysis (BAFBA) by hybridizing Differential Evolution (DE) algorithm into neighborhood searching strategy of BAFBA. In addition, DBFBA is integrated with OptKnock to validate the results for improving the reliability the work. Through several experiments conducted on Escherichia coli, Bacillus subtilis, and Clostridium thermocellum as the model organisms, DBFBA has shown a better performance in terms of computational time, stability, growth rate, and production yield of desired phenotypes compared to the methods used in previous works.
authors.affiliation: Artificial Intelligence and Bioinformatics Group, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia.
authors.forename: Yee Wen
authors.initials: YW
authors.lastname: Choon
authors.affiliation: Artificial Intelligence and Bioinformatics Group, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia.
authors.forename: Mohd Saberi
authors.initials: MS
authors.lastname: Mohamad
authors.affiliation: Artificial Intelligence and Bioinformatics Group, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia.
authors.forename: Safaai
authors.initials: S
authors.lastname: Deris
authors.affiliation: Department of Bioprocess Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, Skudai, Johor, Malaysia.
authors.forename: Rosli Md
authors.initials: RM
authors.lastname: Illias
authors.affiliation: Artificial Intelligence and Bioinformatics Group, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia.
authors.forename: Chuii Khim
authors.initials: CK
authors.lastname: Chong
authors.affiliation: Artificial Intelligence and Bioinformatics Group, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia.
authors.forename: Lian En
authors.initials: LE
authors.lastname: Chai
authors.affiliation: Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, Osaka, Japan.
authors.forename: Sigeru
authors.initials: S
authors.lastname: Omatu
authors.affiliation: Biomedical Research Institute of Salamanca/BISITE Research Group, University of Salamanca, Salamanca, Spain.
authors.forename: Juan Manuel
authors.initials: JM
authors.lastname: Corchado
mesh_terms: D000465:Algorithms; D001412:Bacillus subtilis / Q000235:genetics; D048013:Acetivibrio thermocellus / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D004926:Escherichia coli / Q000235:genetics; D055786:Gene Knockout Techniques / Q000379:methods*; D008954:Models, Biological; D010641:Phenotype; D015203:Reproducibility of Results