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A Parallel-and-Interactive Scheme for the Parameter Estimation of Michaelis-Menten Biological Systems

Shinq-Jen Wu and Anh-Tuan Ngo
Department of Electrical Engineering, Da-Yeh University, Chang-Hwa, Taiwan, ROC
Abstract—The well-known Michaelis-Menten (MM) models give locally rich-kinetic information of proteins or metabolites. However, its parameter estimation requires increasingly large amount of experimental data and repeated modifications. In this study, we proposed a parallel-and-interactive optimization scheme for computationally MM modeling. The scheme integrates the most stochastic physiology evolution (genetic algorithm) and the less-stochastic swarm intelligence (particle swarm optimization) to ensure a flexible search. The scheme was tested with artificially time series data. Simulation results show the proposed scheme possesses good ability in global search even searching in a rather wide space (a range between 0 and 50000). 
 
Index Terms—computational intelligence, data mining, computational biology

Cite: Shinq-Jen Wu and Anh-Tuan Ngo, "A Parallel-and-Interactive Scheme for the Parameter Estimation of Michaelis-Menten Biological Systems," Lecture Notes on Information Theory, Vol. 5, No. 1, pp. 39-43, June 2017. doi: 10.18178/lnit.5.1.39-43
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