A newton cooperative genetic algorithm method for in silico optimization of metabolic pathway production
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 stea...
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| Published in: | PloS one Vol. 10; no. 5; p. e0126199 |
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11-05-2015
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Audience | Academic |
| Author | Ismail, Mohd Arfian Deris, Safaai Mohamad, Mohd Saberi Abdullah, Afnizanfaizal |
| AuthorAffiliation | 1 Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, Gambang, Kuantan, Malaysia 2 Artificial Intelligence and Bioinformatics Group, Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia Tel Aviv University, ISRAEL |
| AuthorAffiliation_xml | – name: 1 Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, Gambang, Kuantan, Malaysia – name: Tel Aviv University, ISRAEL – name: 2 Artificial Intelligence and Bioinformatics Group, Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia |
| Author_xml | – sequence: 1 givenname: Mohd Arfian surname: Ismail fullname: Ismail, Mohd Arfian organization: Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, Lebuhraya Tun Razak, Gambang, Kuantan, Malaysia; Artificial Intelligence and Bioinformatics Group, Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia – sequence: 2 givenname: Safaai surname: Deris fullname: Deris, Safaai organization: Artificial Intelligence and Bioinformatics Group, Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia – sequence: 3 givenname: Mohd Saberi surname: Mohamad fullname: Mohamad, Mohd Saberi organization: Artificial Intelligence and Bioinformatics Group, Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia – sequence: 4 givenname: Afnizanfaizal surname: Abdullah fullname: Abdullah, Afnizanfaizal organization: Artificial Intelligence and Bioinformatics Group, Department of Software Engineering, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Malaysia |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25961295$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1007_s43393_022_00115_6 crossref_primary_10_1016_j_tibtech_2015_11_006 crossref_primary_10_1371_journal_pone_0168725 crossref_primary_10_1016_j_biosystems_2017_09_013 |
| Cites_doi | 10.1016/j.advengsoft.2011.05.014 10.1016/j.ejor.2012.07.026 10.1109/TIE.2007.893063 10.1016/j.asoc.2013.02.005 10.1016/0141-0229(90)90033-M 10.1109/TSMCA.2008.918599 10.1007/s12010-008-8450-6 10.1016/j.compchemeng.2007.08.009 10.1016/j.cor.2009.02.021 10.1016/j.asoc.2007.05.013 10.1016/j.amc.2012.01.023 10.1016/j.cor.2009.07.002 10.1021/bp990144l 10.1016/S0168-1656(98)00178-3 10.1016/S0168-1656(97)00143-0 10.1016/S0025-5564(03)00046-4 10.1186/1742-4682-4-38 |
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| Copyright | COPYRIGHT 2015 Public Library of Science 2015 Ismail et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2015 Ismail et al 2015 Ismail et al |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Conceived and designed the experiments: MAI. Performed the experiments: MAI. Analyzed the data: MAI. Contributed reagents/materials/analysis tools: MAI. Wrote the paper: MAI SD MSM AA. Developed the Newton Cooperative Genetic Algorithm (NCGA): MAI SD MSM AA. Competing Interests: The authors have declared that no competing interests exist. These authors also contributed equally to this work. |
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| SubjectTerms | 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 |
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| Title | A newton cooperative genetic algorithm method for in silico optimization of metabolic pathway production |
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