A Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis for Enhancing Lactate and Succinate in Escherichia Coli

  • Mei Kie Hon
  • Mohd Saberi Mohamad
  • Abdul Hakim Mohamed Salleh
  • Yee Wen Choon
  • Muhammad Akmal Remli
  • Mohd Arfian Ismail
  • Sigeru Omatu
  • Juan Manuel Corchado
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 803)


In the past decades, metabolic engineering has received great attention from different sectors of science due to its important role in enhancing the over expression of the target phenotype by manipulating the metabolic pathway. The advent of metabolic engineering has further laid the foundation for computational biology, leading to the development of computational approaches for suggesting genetic manipulation. Previously, conventional methods have been used to enhance the production of lactate and succinate in E. coli. However, these products are always far below their theoretical maxima. In this research, a hybrid algorithm is developed to seek optimal solutions in order to increase the overproduction of lactate and succinate by gene knockout in E. coli. The hybrid algorithm employed the Simple Constrained Artificial Bee Colony (SCABC) algorithm, using swarm intelligence as an optimization algorithm to optimize the objective function, where lactate and succinate productions are maximized by simulating gene knockout in E. coli. In addition, Flux Balance Analysis (FBA) is used as a fitness function in the SCABC algorithm to assess the growth rate of E. coli and the productivity of lactate and succinate. As a result of the research, the gene knockout list which induced the highest production of lactate and succinate is obtained.


Gene knockout strategies Escherichia coli Lactate Succinate Simple Constrained Artificial Bee Colony Flux Balance Analysis Computational intelligence 



We would like to thank Malaysian Ministry of Higher Education and Universiti Teknologi Malaysia for supporting this research by the Fundamental Research Grant Schemes (grant number: R.J130000.7828.4F886 and R.J130000.7828.4F720). We would also like to thank Universiti Malaysia Pahang for sponsoring this research via the RDU Grant (Grant Number: RDU180307).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mei Kie Hon
    • 1
  • Mohd Saberi Mohamad
    • 2
    • 3
  • Abdul Hakim Mohamed Salleh
    • 1
  • Yee Wen Choon
    • 1
  • Muhammad Akmal Remli
    • 1
  • Mohd Arfian Ismail
    • 4
  • Sigeru Omatu
    • 5
  • Juan Manuel Corchado
    • 6
  1. 1.Artificial Intelligence and Bioinformatics Research Group, Faculty of ComputingUniversiti Teknologi MalaysiaSkudaiMalaysia
  2. 2.Institute for Artificial Intelligence and Big DataUniversiti Malaysia KelantanKota BharuMalaysia
  3. 3.Faculty of Bioengineering and TechnologyUniversiti Malaysia KelantanJeliMalaysia
  4. 4.Soft Computing and Intelligent System Research Group, Faculty of Computer Systems and Software EngineeringUniversiti Malaysia PahangKuantanMalaysia
  5. 5.Department of Electronics, Information and Communication EngineeringOsaka Institute of TechnologyOsakaJapan
  6. 6.Biomedical Research Institute of Salamanca/BISITE Research GroupUniversity of SalamancaSalamancaSpain

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