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.
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Ng, S.T. et al. (2013). Using Particle Swarm Optimization for Estimating Kinetics Parameters on Essential Amino Acid Production of Arabidopsis Thaliana . In: Sidhu, A., Dhillon, S. (eds) Advances in Biomedical Infrastructure 2013. Studies in Computational Intelligence, vol 477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37137-0_7
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DOI: https://doi.org/10.1007/978-3-642-37137-0_7
Publisher Name: Springer, Berlin, Heidelberg
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