Using particle swarm optimization for estimating kinetics parameters on essential amino acid production of Arabidopsis Thaliana

Siew Teng Ng; Chuii Khim Chong; Yee Wen Choon; Lian En Chai; Safaai Deris; Rosli M. Illias; Mohd Shahir Shamsir; Mohd Saberi Mohamad

doi:10.1007/978-3-642-37137-0_7

Using particle swarm optimization for estimating kinetics parameters on essential amino acid production of Arabidopsis Thaliana

Siew Teng Ng
, Chuii Khim Chong
, Yee Wen Choon
, Lian En Chai
, Safaai Deris
, Rosli M. Illias
, Mohd Shahir Shamsir
, Mohd Saberi Mohamad

Research output: Chapter in Book/Report/Conference proceeding › Chapter

2 Citations (Scopus)

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 PSO into SBToolbox to obtain optimum kinetic parameters for simulating essential amino acid metabolism in plant model Arabidopsis Thaliana. There are four performance measurements, namely computational time, average of error rate, standard deviation and production of graph. 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 SA and downhill simplex method after the implementation into SBToolbox.

Original language	English
Title of host publication	Advances in Biomedical Infrastructure 2013
Subtitle of host publication	Proceedings of International Symposium on Biomedical Data Infrastructure (BDI 2013)
Publisher	Springer Verlag
Pages	51-61
Number of pages	11
ISBN (Print)	9783642371363
DOIs	https://doi.org/10.1007/978-3-642-37137-0_7
Publication status	Published - 2013
Externally published	Yes

Publication series

Name	Studies in Computational Intelligence
Volume	477
ISSN (Print)	1860-949X

Keywords

Arabidopsis thaliana
PSO
Parameter estimation
SBToolbox

ASJC Scopus subject areas

Artificial Intelligence

Access to Document

10.1007/978-3-642-37137-0_7

Cite this

Ng, S. T., Chong, C. K., Choon, Y. W., En Chai, L., Deris, S., Illias, R. M., Shamsir, M. S., & Mohamad, M. S. (2013). Using particle swarm optimization for estimating kinetics parameters on essential amino acid production of Arabidopsis Thaliana. In Advances in Biomedical Infrastructure 2013: Proceedings of International Symposium on Biomedical Data Infrastructure (BDI 2013) (pp. 51-61). (Studies in Computational Intelligence; Vol. 477). Springer Verlag. https://doi.org/10.1007/978-3-642-37137-0_7

Using particle swarm optimization for estimating kinetics parameters on essential amino acid production of Arabidopsis Thaliana. / Ng, Siew Teng; Chong, Chuii Khim; Choon, Yee Wen et al.
Advances in Biomedical Infrastructure 2013: Proceedings of International Symposium on Biomedical Data Infrastructure (BDI 2013). Springer Verlag, 2013. p. 51-61 (Studies in Computational Intelligence; Vol. 477).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Ng, ST, Chong, CK, Choon, YW, En Chai, L, Deris, S, Illias, RM, Shamsir, MS & Mohamad, MS 2013, Using particle swarm optimization for estimating kinetics parameters on essential amino acid production of Arabidopsis Thaliana. in Advances in Biomedical Infrastructure 2013: Proceedings of International Symposium on Biomedical Data Infrastructure (BDI 2013). Studies in Computational Intelligence, vol. 477, Springer Verlag, pp. 51-61. https://doi.org/10.1007/978-3-642-37137-0_7

Ng ST, Chong CK, Choon YW, En Chai L, Deris S, Illias RM et al. Using particle swarm optimization for estimating kinetics parameters on essential amino acid production of Arabidopsis Thaliana. In Advances in Biomedical Infrastructure 2013: Proceedings of International Symposium on Biomedical Data Infrastructure (BDI 2013). Springer Verlag. 2013. p. 51-61. (Studies in Computational Intelligence). doi: 10.1007/978-3-642-37137-0_7

Ng, Siew Teng ; Chong, Chuii Khim ; Choon, Yee Wen et al. / Using particle swarm optimization for estimating kinetics parameters on essential amino acid production of Arabidopsis Thaliana. Advances in Biomedical Infrastructure 2013: Proceedings of International Symposium on Biomedical Data Infrastructure (BDI 2013). Springer Verlag, 2013. pp. 51-61 (Studies in Computational Intelligence).

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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 PSO into SBToolbox to obtain optimum kinetic parameters for simulating essential amino acid metabolism in plant model Arabidopsis Thaliana. There are four performance measurements, namely computational time, average of error rate, standard deviation and production of graph. 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 SA and downhill simplex method after the implementation into SBToolbox.",

keywords = "Arabidopsis thaliana, PSO, Parameter estimation, SBToolbox",

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AU - Deris, Safaai

AU - Illias, Rosli M.

AU - Shamsir, Mohd Shahir

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AB - 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 PSO into SBToolbox to obtain optimum kinetic parameters for simulating essential amino acid metabolism in plant model Arabidopsis Thaliana. There are four performance measurements, namely computational time, average of error rate, standard deviation and production of graph. 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 SA and downhill simplex method after the implementation into SBToolbox.

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Using particle swarm optimization for estimating kinetics parameters on essential amino acid production of Arabidopsis Thaliana

Abstract

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Access to Document

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