title: Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains.
abstract: Reconstructions of genome-scale metabolic networks from different organisms have become popular in recent years. Metabolic engineering can simulate the reconstruction process to obtain desirable phenotypes. In previous studies, optimization algorithms have been implemented to identify the near-optimal sets of knockout genes for improving metabolite production. However, previous works contained premature convergence and the stop criteria were not clear for each case. Therefore, this study proposes an algorithm that is a hybrid of the ant colony optimization algorithm and flux balance analysis (ACOFBA) to predict near optimal sets of gene knockouts in an effort to maximize growth rates and the production of certain metabolites. Here, we present a case study that uses Baker's yeast, also known as Saccharomyces cerevisiae, as the model organism and target the rate of vanillin production for optimization. The results of this study are the growth rate of the model organism after gene deletion and a list of knockout genes. The ACOFBA algorithm was found to improve the yield of vanillin in terms of growth rate and production compared with the previous algorithms.
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: lu_shi_jing@hotmail.com.
authors.forename: Shi Jing
authors.initials: SJ
authors.lastname: Lu
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: abdhakim.utm@gmail.com.
authors.forename: Abdul Hakim Mohamed
authors.initials: AH
authors.lastname: Salleh
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: saberi@utm.my.
authors.forename: Mohd Saberi
authors.initials: MS
authors.lastname: Mohamad
authors.affiliation: Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: safaai@utm.my.
authors.forename: Safaai
authors.initials: S
authors.lastname: Deris
authors.affiliation: Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan. Electronic address: omatu@rsh.oit.ac.jp.
authors.forename: Sigeru
authors.initials: S
authors.lastname: Omatu
authors.affiliation: Faculty of Engineering, Osaka Prefecture University, 1-1 Gakuencho Nakaku, Sakai, 599-8531,Japan. Electronic address: yoshioka@cs.osakafu-u.ac.jp.
authors.forename: Michifumi
authors.initials: M
authors.lastname: Yoshioka
keywords: Ant colony optimization algorithm; Flux balance analysis; Gene knockout strategy; Metabolic engineering; Microbial strains; Optimization algorithm