Advertisement

Identifying a Gene Knockout Strategy Using a Hybrid of Simple Constrained Artificial Bee Colony Algorithm and Flux Balance Analysis to Enhance the Production of Succinate and Lactate in Escherichia Coli

  • Mei Kie Hon
  • Mohd Saberi MohamadEmail author
  • Abdul Hakim Mohamed Salleh
  • Yee Wen Choon
  • Kauthar Mohd Daud
  • Muhammad Akmal Remli
  • Mohd Arfian Ismail
  • Sigeru Omatu
  • Richard O. Sinnott
  • Juan Manuel Corchado
Original research article

Abstract

In recent years, metabolic engineering has gained central attention in numerous fields of science because of its capability to manipulate metabolic pathways in enhancing the expression of target phenotypes. Due to this, many computational approaches that perform genetic manipulation have been developed in the computational biology field. In metabolic engineering, conventional methods have been utilized to upgrade the generation of lactate and succinate in E. coli, although the yields produced are usually way below their theoretical maxima. To overcome the drawbacks  of such conventional methods, development of hybrid algorithm is introduced to obtain an optimal solution by proposing a gene knockout strategy in E. coli which is able to improve the production of lactate and succinate. The objective function of the hybrid algorithm is optimized using a swarm intelligence optimization algorithm and a Simple Constrained Artificial Bee Colony (SCABC) algorithm. The results maximize the production of lactate and succinate by resembling the gene knockout in E. coli. The Flux Balance Analysis (FBA) is integrated in a hybrid algorithm to evaluate the growth rate of E. coli as well as the productions of lactate and succinate. This results in the identification of a gene knockout list that contributes to maximizing the production of lactate and succinate in E. coli.

Keywords

Gene Knockout Strategies Escherichia Coli Lactate Succinate Simple Constrained Artificial Bee Colony Flux Balance Analysis 

Notes

Acknowledgements

We would like to thank Malaysian Ministry of Higher Education and Universiti Teknologi Malaysia for supporting this research as part of the Fundamental Research Grant Scheme (Grant number: R.J130000.7828.4F720) and the Flagship Grant Scheme (Grant number: Q.J130000.2428.03G57). We would also like to thank Universiti Malaysia Pahang for sponsoring this research via the RDU Grant (Grant number: RDU180307).

References

  1. 1.
    Lee SJ, Lee DY, Kim TY, Kim BH, Lee J, Lee SY (2005) Metabolic engineering of escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl Environ Microbiol 71:7880.  https://doi.org/10.1128/AEM.71.12.7880-7887.2005 Google Scholar
  2. 2.
    Burgard AP, Pharkya P, Maranas CD (2003) Optknock: a bilevel programming framework for identifying gene knockout strategies for microbial strain optimization. Biotechnol Bioeng 84(6):647–657.  https://doi.org/10.1002/bit.10803 Google Scholar
  3. 3.
    Patil KR, Rocha I, Forster J, Nielsen J (2005) Evolutionary programming as a platform for in silico metabolic engineering. BMC Bioinform 6(1):1–12.  https://doi.org/10.1186/14702105-6-308 Google Scholar
  4. 4.
    Rocha M, Maia P, Mendes R, Pinto JP, Ferreira EC, Nielsen J, Patil KR, Rocha I (2008) Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinform 9:499.  https://doi.org/10.1186/1471-2105-9-499 Google Scholar
  5. 5.
    Martino GD, Cardillo FA, Starita A (2006) A new swarm intelligence coordination model inspired by collective prey retrieval and its application to image alignment. In: Runarsson T, Beyer HG, Burke E, Merelo-Guervós J, Whitley LD, Yao X (eds) Parallel problem solving from Nature—PPSN IX Springer, Berlin, pp 691–700Google Scholar
  6. 6.
    Brajevic I, Tuba M, Subotic M (2011) Performance of the improved artificial bee colony algorithm on standard engineering constrained problems. Int J Math Comput Simul 5(2):135–143Google Scholar
  7. 7.
    Karaboga D, Basturk B (2007) A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J Glob Optim 39(3):459–471.  https://doi.org/10.1007/s10898-007-9149-x Google Scholar
  8. 8.
    Raman K, Chandra N (2009) Flux balance analysis of biological systems: applications and challenges. Brief Bioinform 10(4):435–449.  https://doi.org/10.1093/bib/bbp011 Google Scholar
  9. 9.
    Rocha M, Maia P, Mendes R, Pinto JP, Ferreira EC, Nielsen J, Patil KR, Rocha I (2008) Natural computation meta-heuristics for the in silico optimization of microbial strains. BMC Bioinform 9(1):499.  https://doi.org/10.1186/1471-215-9-499 Google Scholar
  10. 10.
    Hua Q, Joyce AR, Fong SS, Palsson BO (2006) Metabolic analysis of adaptive evolution for in silico-designed lactate-producing strains. Biotechnol Bioeng 95(5):992–1002.  https://doi.org/10.1002/bit.21073 Google Scholar
  11. 11.
    Sauer U, Canonaco F, Heri S, Perrenoud A, Fischer E (2004) The soluble and membrane-bound transhydrogenases UdhA and PntAB have divergent functions in NADPH metabolism of Escherichia coli. J Biol Chem 279(8):6613–6619.  https://doi.org/10.1074/jbc.M311657200 Google Scholar
  12. 12.
    Jung YK, Kim TY, Park SJ, Lee SY (2010) Metabolic engineering of Escherichia coli for the production of polylactic acid and its copolymers. Biotechnol Bioeng 105(1):161–171.  https://doi.org/10.1002/bit.22548 Google Scholar
  13. 13.
    Smith EL, Austen BM, Blumenthal KM, Nyc JF (1975) Glutamate dehydrogenases. In: The enzymes, New York, Academic Press, pp 293–367Google Scholar
  14. 14.
    Zhou L, Zuo ZR, Chen XZ, Niu DD, Tian KM, Prior BA, Shen W, Shi GY, Singh S, Wang ZX (2011) Evaluation of genetic manipulation strategies on D-lactate production by Escherichia coli. Curr Microbiol 62(3):981–989.  https://doi.org/10.1007/s00284-010-9817-9 Google Scholar
  15. 15.
    Niersbach M, Kreuzaler F, Geerse RH, Postma PW, Hirsch HJ (1992) Cloning and nucleotide-sequence of the Escherichia-Coli K-12 ppsA gene, encoding PEP synthase. Mol Gen Genet 231(2):332–336Google Scholar
  16. 16.
    Gamo FJ, Portillo F, Gancedo C (1993) Characterization of mutations that overcome the toxic effect of glucose on phosphoglucose isomerase less strains of Saccharomyces cerevisiae. FEMS Microbiol Lett 106(3):233–238.  https://doi.org/10.1111/j.1574-6968.1993.tb05969.x Google Scholar
  17. 17.
    Vemuri GN, Eiteman MA, Altman E (2002) Effects of growth mode and pyruvate carboxylase on succinic acid production by metabolically engineered strains of Escherichia coli. Appl Environ Microbiol 68(4):1715–1727.  https://doi.org/10.1128/AEM.68.4.1715-1721-2002 Google Scholar
  18. 18.
    Cox SJ, Levanon SS, Sanchez A, Lin H, Peercy B, Bennett GN, San KY (2006) Development of a metabolic network design and optimization framework incorporating implementation constraints: A succinate production case study. Metab Eng 8(1):46–57.  https://doi.org/10.1016/j.ymben.2005.09.006 Google Scholar
  19. 19.
    Nor’Aini AR, Shirai Y, Hassan MA, Shimizu K (2006) Investigation on the metabolic regulation of pgi gene knockout Escherichia coli by enzyme activities and intracellular metabolite concentrations. Malaysian J Microbiol 2:24–31Google Scholar
  20. 20.
    Bautista J, Satrustegui J, Machado A (1979) Evidence suggesting that the NADPH/NADP ratio modulates the splitting of the isocitrate flux between the glyoxylic and tricarboxylic acid cycles, in Escherichia coli. FEBS Lett 105(2):333–336.  https://doi.org/10.1016/0014-5793(79)80642-0 Google Scholar
  21. 21.
    Jantama K, Zhang X, Moore JC, Shanmugam KT, Svoronos SA, Ingram LO (2008) Eliminating side products and increasing succinate yields in engineered strains of Escherichia coli C. Biotechnol Bioeng 101(5):881–893.  https://doi.org/10.1002/bit.22005 Google Scholar
  22. 22.
    Lee SJ, Lee DY, Kim TY, Kim BH, Lee J, Lee SY (2005) Metabolic engineering of Escherichia coli for enhanced production of succinic acid, based on genome comparison and in silico gene knockout simulation. Appl Environ Microbiol 71(12):7880–7887.  https://doi.org/10.1128/AEM.71.12.7880-7887.2005 Google Scholar
  23. 23.
    Lin H, Bennett GN, San KY (2005) Genetic reconstruction of the aerobic central metabolism in Escherichia coli for the absolute aerobic production of succinate. Biotechnol Bioeng 89(2):148–156.  https://doi.org/10.1002/bit.20298 Google Scholar

Copyright information

© International Association of Scientists in the Interdisciplinary Areas 2019

Authors and Affiliations

  • Mei Kie Hon
    • 1
  • Mohd Saberi Mohamad
    • 2
    • 3
    Email author
  • Abdul Hakim Mohamed Salleh
    • 1
  • Yee Wen Choon
    • 1
  • Kauthar Mohd Daud
    • 1
  • Muhammad Akmal Remli
    • 4
  • Mohd Arfian Ismail
    • 4
  • Sigeru Omatu
    • 5
  • Richard O. Sinnott
    • 6
  • Juan Manuel Corchado
    • 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.Department of System Design Engineering, Faculty of Robotics & Design EngineeringOsaka Institute of TechnologyOsakaJapan
  6. 6.School of Computing and Information SystemsUniversity of MelbourneVictoriaAustralia
  7. 7.Biomedical Research Institute of Salamanca/BISITE Research GroupUniversity of SalamancaSalamancaSpain

Personalised recommendations