Enhancement of Ethanol Production Using a Hybrid of Firefly Algorithm and Dynamic Flux Balance Analysis

Wan Ting Leong, Mohd Saberi Mohamad, Kohbalan Moorthy, Yee Wen Choon, Hasyiya Karimah Adli, W. S.W. Khairul Nizar Syazwan, Loo Keat Wei, Nazar Zaki

Research output: Contribution to journalArticlepeer-review


Many high-demand industrial products are generated by microorganisms, including fuels, food, vitamins, and other chemicals. Metabolic engineering is the method of circumventing cellular control to manufacture a desirable product or to create a new product that the host cells do not normally need to produce. One of the objectives of microorganism metabolic engineering is to maximise the production of a desired product. However, owing to the structure of the regulatory cellular and metabolic network, identifying specific genes to be knocked out is difficult. The development of optimization algorithms often confronts issues such as easily trapping in local maxima and handling multivariate and multimodal functions inefficiently. To predict the gene knockout list that can generate high yields of desired product, a hybrid of firefly algorithm and dynamic flux balance analysis (FADFBA) is proposed. This paper focuses on the ethanol production of Escherichia coli (E. coli). The findings of the experiments include gene lists, ethanol production, growth rate, and the performance of FADFBA.

Original languageEnglish
JournalInternational Journal of Swarm Intelligence Research
Issue number1
Publication statusPublished - 2022


  • Artificial Intelligence
  • Bioinformatics
  • Dynamic Flux Balance Analysis
  • Escherichia coli
  • Firefly Algorithm
  • Gene Knockout
  • Metabolic Engineering

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Artificial Intelligence


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