IJMLC 2019 Vol.9(2): 160-167 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2019.9.2.781

Large-Scale Kinetic Parameters Estimation of Metabolic Model of Escherichia Coli

Mohammed Adam Kunna Azrag, Tuty Asmawaty Abdul Kadir, Muhammad Nomani Kabir, and Aqeel S. Jaber

Abstract—In the last few decades, the metabolic model of E.coli has attracted the attention of many researchers in the area of biological system modeling. Metabolic models are constructed using mass-balance equations with kinetic-rate computation to simulate the behavior of the metabolic system over time. However, in the development of the metabolic model, large-scale kinetic parameters affect the model response if the parameter values are not assigned accurately, which, in turn, propagates the errors in the ordinary differential equations (ODEs) – the mass balance equations associated with the model. This situation emphasizes the need to adopt a global optimization technique to compute the kinetic parameters such that the errors – the discrepancy between actual biological data and the model response - are minimized. In this work, the PSO algorithm has been adopted to estimate the kinetic parameters by minimizing the errors of the large-scale of metabolic model response of E. coli with reference to real experimental data. Seven highly sensitive kinetic parameters in the model response were considered in the optimization problem. Estimation of the 7th kinetic parameters by the PSO method provides a good performance of the model in terms of accuracy.

Index Terms—Kinetic parameters, dynamic metabolic model, escherichia coli, PSO algorithm.

Mohammed Adam Kunna Azrag, Tuty Asmawaty Abdul Kadir, Muhammad Nomani Kabir, and Aqeel S. Jaber are with Universiti Malaysia Pahang, Malaysia (e-mail: mohammed87kunna@gmail.com).

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Cite: Mohammed Adam Kunna Azrag, Tuty Asmawaty Abdul Kadir, Muhammad Nomani Kabir, and Aqeel S. Jaber, "Large-Scale Kinetic Parameters Estimation of Metabolic Model of Escherichia Coli," International Journal of Machine Learning and Computing vol. 9, no. 2, pp. 160-167, 2019.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net