A Comparison of Genetic Algorithms and Particle Swarm Optimization for Parameter Estimation in Stochastic Biochemical Systems

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Abstract

The modelling of biochemical systems requires the knowledge of several quantitative parameters (e.g. reaction rates) which are often hard to measure in laboratory experiments. Furthermore, when the system involves small numbers of molecules, the modelling approach should also take into account the effects of randomness on the system dynamics. In this paper, we tackle the problem of estimating the unknown parameters of stochastic biochemical systems by means of two optimization heuristics, genetic algorithms and particle swarm optimization. Their performances are tested and compared on two basic kinetics schemes: the Michaelis-Menten equation and the Brussellator. The experimental results suggest that particle swarm optimization is a suitable method for this problem. The set of parameters estimated by particle swarm optimization allows us to reliably reconstruct the dynamics of the Michaelis-Menten system and of the Brussellator in the oscillating regime.
Original languageUnknown
Title of host publicationEvolutionary Computation, Machine Learning and Data Mining in Bioinformatics, Proceedings
EditorsCRMDGM Pizzuti
Place of PublicationBerlin
PublisherSpringer
Pages116-127
Volume5483
ISBN (Print)0302-9743 978-3-642-01183-2
DOIs
Publication statusPublished - 1 Jan 2009

Publication series

NameLecture Notes in Computer Science
PublisherSpringer

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