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A Hybrid of Particle Swarm Optimization and Minimization of Metabolic Adjustment for Ethanol Production of Escherichia Coli

  • Mee K. Lee
  • Mohd Saberi MohamadEmail author
  • Yee Wen Choon
  • Kauthar Mohd Daud
  • Nurul Athirah Nasarudin
  • Mohd Arfian Ismail
  • Zuwairie Ibrahim
  • Suhaimi Napis
  • Richard O. Sinnott
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1005)

Abstract

Ethanol is a chemical-colourless compound that widely used in pharmaceutical, medicines, food products, and industrial applications. As the demand for ethanol is rising recently, attention has been given on metabolic engineering of Escherichia coli (E.coli) to enhance its production through alteration of its genetic content. This research mainly aimed to optimize ethanol production in E.coli using a gene knockout strategy. Several gene knockout strategies like OptKnock and OptGene have been proposed previously. However, most of them suffer from premature convergence. Hence, a hybrid of Particle Swarm Optimization (PSO) and Minimization of Metabolic Adjustment (MOMA) algorithm is proposed to identify the list of gene knockouts in maximizing the ethanol production and growth rate of E.coli. Experiment results show that the hybrid method is comparable with two state-of-the-art methods in term of growth rate and production.

Keywords

Particle swarm optimization Minimization of metabolic adjustment Metabolic engineering Bioinformatics Artificial intelligence 

Notes

Acknowledgement

We would like to thank the Ministry of Education Malaysia for supporting this research by the Fundamental Research Grant Schemes (grant number: RDU190113 and R.J130000.7828.4F720).

References

  1. 1.
    Tang, P., Choon, Y.W., Mohamad, M.S., Deris, S., Napis, S.: Optimising the production of succinate and lactate in Escherichia coli using a hybrid of artificial bee colony algorithm and minimisation of metabolic adjustment. J. Biosci. Bioeng. 119(3), 363–368 (2015)CrossRefGoogle Scholar
  2. 2.
    Burgard, A.P., Pharkya, P., Maranas, C.D.: OptKnock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol. Bioeng. 84(6), 647–657 (2003)CrossRefGoogle Scholar
  3. 3.
    Arif, M.A., Mohamad, M.S., Abd Latif, M.S., Deris, S., Remli, M.A., Daud, M.K., Ibrahim, Z., Omatu, S., Corchado, J.M.: A hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to optimize metabolites production in genome-scale models. Comput. Biol. Med. 102, 112–119 (2018)CrossRefGoogle Scholar
  4. 4.
    Orth, J.D., Conrad, T.M., Na, J., Lerman, J.A., Nam, H., Feist, A.M., Palsson, B.Ø.: A comprehensive genome-scale reconstruction of Escherichia coli metabolism. Mol. Syst. Biol. 7(1), 535 (2011)CrossRefGoogle Scholar
  5. 5.
    Klein, H.A., Shulla, A., Reimann, A.S., Keating, H.D., Wolfe, J.A.: The intracellular concentration of acetyl phosphate in escherichia coli is sufficient for direct phosphorylation of two-component response regulators. J. Bacteriol. 189(15), 5574–5581 (2007)CrossRefGoogle Scholar
  6. 6.
    Zhou, L., Zuo, R.Z., Chen, Z.X., Niu, D.D., Tian, M.K.: Evaluation of genetic manipulation strategies on D-lactate production by Escherichia coli. Curr. Microbiol. 62(3), 981–989 (2011)CrossRefGoogle Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceeding of the 1995 IEEE on Neural Networks, pp. 1942–1948 (1995)Google Scholar
  8. 8.
    Segre, D., Vltkup, D., Church, M.G.: Analysis of optimality in natural and perturbed metabolic networks. Proc. Natl. Acad. Sci. 99(23), 15112–15117 (2002)CrossRefGoogle Scholar
  9. 9.
    Mienda, S.B., Shamsir, S.M., Shehu, I., Deba, A.A., Galadima, A.I.: In silico metabolic engineering interventions of Escherichia coli for enhanced ethanol production, based on gene knockout simulation. J. Multi. Sci. Technol. 5(2), 16–23 (2014)Google Scholar
  10. 10.
    Dien, S.B., Cotta, A.M., Jeffries, W.T.: Bacteria engineered for fuel ethanol production: current status. Appl. Microbiol. Biotechnol. 63(3), 258–266 (2003)CrossRefGoogle Scholar
  11. 11.
    Pharkya, P., Maranas, C.D.: An optimization framework for identifying reaction activation/inhibition or elimination candidates for overproduction in microbial systems. Metab. Eng. 8(1), 1–13 (2006)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Mee K. Lee
    • 1
  • Mohd Saberi Mohamad
    • 2
    • 3
    Email author
  • Yee Wen Choon
    • 1
  • Kauthar Mohd Daud
    • 1
  • Nurul Athirah Nasarudin
    • 1
  • Mohd Arfian Ismail
    • 4
  • Zuwairie Ibrahim
    • 5
  • Suhaimi Napis
    • 6
  • Richard O. Sinnott
    • 7
  1. 1.Artificial Intelligence and Bioinformatics Research Group, School of Computing, Faculty of EngineeringUniversiti 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.Faculty of Manufacturing EngineeringUniversiti Malaysia PahangPekanMalaysia
  6. 6.Faculty of Biotechnology and Biomolecular SciencesUniversiti Putra MalaysiaUPM SerdangMalaysia
  7. 7.School of Computing and Information SystemsThe University of MelbourneMelbourneAustralia

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