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Comparison of Optimization-Modelling Methods for Metabolites Production in Escherichia coli

Lee MK 1 , Mohamad MS 2 , Choon YW 1 , Mohd Daud K 1 , Nasarudin NA 1 , Ismail MA 3 Show all authors , Ibrahim Z 4 , Napis S 5 , Sinnott RO 6

Affiliations 

  • 1 Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, 81310 Skudai Johor, Malaysia
  • 2 Institute for Artificial Intelligence and Big Data, Universiti Malaysia Kelantan, City Campus, Pengkalan Chepa, 16100 Kota Bharu Kelantan, Malaysia
  • 3 Soft Computing and Intelligent System Research Group, Faculty of Computer Systems and Software Engineering, Universiti Malaysia Pahang, 26300 Kuantan, Pahang, Malaysia
  • 4 Faculty of Manufacturing Engineering, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
  • 5 Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia
  • 6 School of Computing and Information Systems, The University of Melbourne, Melbourne, VIC 3052 Australia
J Integr Bioinform, 2020 May 06;17(1).
PMID: 32374287 DOI: 10.1515/jib-2019-0073

Abstract

The metabolic network is the reconstruction of the metabolic pathway of an organism that is used to represent the interaction between enzymes and metabolites in genome level. Meanwhile, metabolic engineering is a process that modifies the metabolic network of a cell to increase the production of metabolites. However, the metabolic networks are too complex that cause problem in identifying near-optimal knockout genes/reactions for maximizing the metabolite's production. Therefore, through constraint-based modelling, various metaheuristic algorithms have been improvised to optimize the desired phenotypes. In this paper, PSOMOMA was compared with CSMOMA and ABCMOMA for maximizing the production of succinic acid in E. coli. Furthermore, the results obtained from PSOMOMA were validated with results from the wet lab experiment.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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