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

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

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Lu, Shi Jing Salleh, Abdul Hakim Mohamed Mohamad, Mohd Saberi Deris, Safaai Omatu, Sigeru Yoshioka, Michifumi
Description	Author Affiliation: Lu SJ ( Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: lu_shi_jing@hotmail.com.); Salleh AH ( Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: abdhakim.utm@gmail.com.); Mohamad MS ( Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: saberi@utm.my.); Deris S ( Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia. Electronic address: safaai@utm.my.); Omatu S ( Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, Osaka 535-8585, Japan. Electronic address: omatu@rsh.oit.ac.jp.); Yoshioka M ( Faculty of Engineering, Osaka Prefecture University, 1-1 Gakuencho Nakaku, Sakai, 599-8531,Japan. Electronic address: yoshioka@cs.osakafu-u.ac.jp.)
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.
File Format	HTM / HTML
ISSN	14769271
Journal	Computational Biology and Chemistry
Volume Number	53PB
e-ISSN	1476928X
Language	English
Publisher	Elsevier
Publisher Date	2014-09-28
Publisher Place	Great Britain (UK)
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Medical Informatics Discipline Chemistry Discipline Computational Biology
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Structural Biology Biochemistry Computational Mathematics

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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