Biotechnology and Bioprocess Engineering

, Volume 20, Issue 4, pp 685–693 | Cite as

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

  • Abdul Hakim Mohamed Salleh
  • Mohd Saberi MohamadEmail author
  • Safaai Deris
  • Sigeru Omatu
  • Florentino Fdez-Riverola
  • Juan Manuel Corchado
Research Paper


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.


gene knockout metabolites production flux balance analysis genetic ant colony optimization hybrid algorithm 


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Copyright information

© The Korean Society for Biotechnology and Bioengineering and Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Abdul Hakim Mohamed Salleh
    • 1
  • Mohd Saberi Mohamad
    • 1
    Email author
  • Safaai Deris
    • 2
  • Sigeru Omatu
    • 1
  • Florentino Fdez-Riverola
    • 3
  • Juan Manuel Corchado
    • 4
    • 5
  1. 1.Artificial Intelligence and Bioinformatics Research Group, Faculty of ComputingUniversiti Teknologi MalaysiaUTM Skudai, JohorMalaysia
  2. 2.Department of Electronics, Information and Communication EngineeringOsaka Institute of Technology535-8585Japan
  3. 3.Escuela Superior de Ingeniería Informática, Edificio Politécnico, Campus Universitario As Lagoas s/nUniversity of VigoOurenseSpain
  4. 4.Biomedical Research Institute of Salamanca/BISITE Research GroupUniversity of SalamancaSalamancaSpain
  5. 5.Osaka Institute of TechnologyOsakaJapan

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