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Gene knockout identification for metabolite production improvement using a hybrid of genetic ant colony optimization and flux balance analysis

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Abstract

The increasing demand of biochemical supply for various industries has spurred the development of metabolic engineering to find the optimal design of the microbial cell factories. Traditional method of chemical synthesis using the natural producer leads to the production far below their theoretical maximums. Gene knockout strategy is then introduced to improve the metabolite production. To aid the process, many computational algorithms have been developed to design the optimal microbial strain as cell factories to increase the production of the desired metabolite. However, due to the size of the genome scale model of the microbial strain, finding the optimal combination of genes to be knocked out is not an easy task. In this paper, we propose a hybrid of Genetic Ant Colony Optimization (GACO) and Flux Balance Analysis (FBA) namely GACOFBA to find the optimal gene knockout that increase the production of the target metabolite. Using E. coli and S. cerevisiae genome scale model, we test our proposed hybrid algorithm to increase the production of four different metabolites. By comparing with the results from existing method OptKnock as well as the conventional Ant Colony Optimization (ACO), the results show that our proposed hybrid algorithm able to identify the best set of genes and increase the production while maintaining the optimal growth rate.

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Authors and Affiliations

  1. Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310, UTM Skudai, Johor, Malaysia

    Abdul Hakim Mohamed Salleh, Mohd Saberi Mohamad & Sigeru Omatu

  2. Department of Electronics, Information and Communication Engineering, Osaka Institute of Technology, Osaka, 535-8585, Japan

    Safaai Deris

  3. Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/n, University of Vigo, 32004, Ourense, Spain

    Florentino Fdez-Riverola

  4. Biomedical Research Institute of Salamanca/BISITE Research Group, University of Salamanca, 37008, Salamanca, Spain

    Juan Manuel Corchado

  5. Osaka Institute of Technology, Osaka, 535-8585, Japan

    Juan Manuel Corchado

Authors
  1. Abdul Hakim Mohamed Salleh
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  2. Mohd Saberi Mohamad
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  3. Safaai Deris
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  4. Sigeru Omatu
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  5. Florentino Fdez-Riverola
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  6. Juan Manuel Corchado
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Correspondence to Mohd Saberi Mohamad.

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Salleh, A.H.M., Mohamad, M.S., Deris, S. et al. Gene knockout identification for metabolite production improvement using a hybrid of genetic ant colony optimization and flux balance analysis. Biotechnol Bioproc E 20, 685–693 (2015). https://doi.org/10.1007/s12257-015-0276-9

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  • DOI: https://doi.org/10.1007/s12257-015-0276-9

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