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

  • Daniela Besozzi
  • Paolo Cazzaniga
  • Giancarlo Mauri
  • Dario Pescini
  • Leonardo Vanneschi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5483)


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.


Genetic Algorithm Particle Swarm Optimization Stochastic Simulation Algorithm Target Dynamic Gaussian Mutation 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Daniela Besozzi
    • 1
  • Paolo Cazzaniga
    • 2
  • Giancarlo Mauri
    • 2
  • Dario Pescini
    • 2
  • Leonardo Vanneschi
    • 2
  1. 1.Dipartimento di Informatica e ComunicazioneUniversità di MilanoMilanoItaly
  2. 2.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversità di Milano-BicoccaMilanoItaly

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