Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains

Shi Jing Lu; Abdul Hakim Mohamed Salleh; Mohd Saberi Mohamad; Safaai Deris; Sigeru Omatu; Michifumi Yoshioka

doi:10.1016/j.compbiolchem.2014.09.008

Identification of gene knockout strategies using a hybrid of an ant colony optimization algorithm and flux balance analysis to optimize microbial strains

Comput Biol Chem. 2014 Dec:53PB:175-183. doi: 10.1016/j.compbiolchem.2014.09.008. Epub 2014 Sep 28.

Authors

Shi Jing Lu¹, Abdul Hakim Mohamed Salleh², Mohd Saberi Mohamad³, Safaai Deris⁴, Sigeru Omatu⁵, Michifumi Yoshioka⁶

Affiliations

¹ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: lu_shi_jing@hotmail.com.
² Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: abdhakim.utm@gmail.com.
³ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: saberi@utm.my.
⁴ Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: safaai@utm.my.
⁵ Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan. Electronic address: omatu@rsh.oit.ac.jp.
⁶ Faculty of Engineering, Osaka Prefecture University, 1-1 Gakuencho Nakaku, Sakai, 599-8531,Japan. Electronic address: yoshioka@cs.osakafu-u.ac.jp.

PMID: 25462325
DOI: 10.1016/j.compbiolchem.2014.09.008

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.

Keywords: Ant colony optimization algorithm; Flux balance analysis; Gene knockout strategy; Metabolic engineering; Microbial strains; Optimization algorithm.