Estimating Kinetic Parameters for Essential Amino Acid Production in Arabidopsis Thaliana by Using Particle Swarm Optimization

Authors

  • Siew Teng Ng Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
  • Chuii Khim Chong Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
  • Yee Wen Choon Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
  • Lian En Chai Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
  • Safaai Deris Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
  • Rosli Md Illias Department of Bioprocess Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia
  • Mohd Shahir Shamsir Bioinformatics Research Group, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia
  • Mohd Saberi Mohamad Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

DOI:

https://doi.org/10.11113/jt.v64.1737

Keywords:

Parameter estimation, PSO, SBToolbox, Arabidopsis Thaliana

Abstract

Parameter estimation is one of nine phases in modelling, which is the most challenging task that is used to estimate the parameter values for biological system that is non-linear. There is no general solution for determining the nonlinearity of the dynamic model. Experimental measurement is expensive, hard and time consuming. Hence, the aim for this research is to implement Particle Swarm Optimization (PSO) intoSBToolbox to solve the mentioned problems. As a result, the optimum kinetic parameters for simulating essential amino acid metabolism in plant model Arabidopsis Thaliana are obtained. There are four performance measurements used, namely computational time, average of error rate, standard deviation and production of graph. As a finding of this research, PSO has the smallest standard deviation and average of error rate.  The computational time in parameter estimation is smaller in comparison with others, indicating that PSO is a consistent method to estimate parameter values compared to the performance of Simulated Annealing (SA) and downhill simplex method after the implementation into SBToolbox.

 

Author Biographies

  • Chuii Khim Chong, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
    PhD Student
  • Yee Wen Choon, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
    PhD Student
  • Lian En Chai, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
    PhD Student
  • Safaai Deris, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computer Science and Information Systems, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
    Professor, Department of Engineering
  • Rosli Md Illias, Department of Bioprocess Engineering, Faculty of Chemical Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia
    Department of Bioprocess Engineering, Professor
  • Mohd Shahir Shamsir, Bioinformatics Research Group, Faculty of Biosciences and Medical Engineering, Universiti Teknologi Malaysia, UTM Johor Bahru, Johor, Malaysia
    Department of Biological Sciences, Dr
  • Mohd Saberi Mohamad, Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia

    Associate Professor,

    Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, UniversitiTeknologi Malaysia, Skudai, 81310 Johor, Malaysia

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Published

2013-09-10

Issue

Vol. 64 No. 1: September 2013

Section

Science and Engineering

License

Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

Estimating Kinetic Parameters for Essential Amino Acid Production in Arabidopsis Thaliana by Using Particle Swarm Optimization. (2013). Jurnal Teknologi (Sciences & Engineering), 64(1). https://doi.org/10.11113/jt.v64.1737