Paper Info
Title
A Comparison of Genetic Algorithms and Particle Swarm Optimization for Parameter Estimation in Stochastic Biochemical Systems
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.
Year
DOI
Venue
2009
10.1007/978-3-642-01184-9_11
Lecture Notes in Computer Science
Keywords
Field
DocType
genetic algorithms,particle swarm optimization,modelling approach,system dynamic,michaelis-menten system,biochemical system,parameter estimation,stochastic biochemical systems,michaelis-menten equation,optimization heuristics,stochastic biochemical system,basic kinetics scheme,kinetic scheme,oscillations,genetic algorithm,system dynamics,reaction rate
Particle swarm optimization,Mathematical optimization,Computer science,Meta-optimization,Multi-swarm optimization,System dynamics,Estimation theory,Genetic algorithm,Randomness,Metaheuristic
Conference
Volume
ISSN
Citations 
5483
0302-9743
14
PageRank 
References 
Authors
0.92
8
5
Name
Order
Citations
PageRank
Daniela Besozzi139139.10
Paolo Cazzaniga223527.16
Giancarlo Mauri32106297.38
Dario Pescini427425.92
Leonardo Vanneschi51440116.04