Advertisement

Combining Probabilistic Dependency Models and Particle Swarm Optimization for Parameter Inference in Stochastic Biological Systems

  • Michele Forlin
  • Debora Slanzi
  • Irene Poli
Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 145)

Abstract

In this work we present an efficient method to tackle the problem of parameter inference for stochastic biological models. We develop a variant of the Particle Swarm Optimization algorithm by including Probabilistic Dependency statistical models to detect the parameter dependencies. This results in a more efficient parameter inference of the biological model.We test the Probabilistic Dependency- PSO on a well-known benchmark problem: the thermal isomerization of α-pinene

Keywords

Particle Swarm Optimization Particle Swarm Optimization Algorithm Probabilistic Dependency Standard Particle Swarm Optimization Parameter Inference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Balsa-Canto, E., Peifer, M., Banga, J.R., Timmer, J., Fleck, C.: Hybrid optimization method with general switching strategy for parameter estimation. BMC Systems Biology 2(1), 26 (2008)CrossRefGoogle Scholar
  2. 2.
    Boys, R.J., Wilkinson, D.J., Kirkwood, T.B.L.: Bayesian inference for a discretely observed stochastic kinetic model. Statistics and Computing 18(2), 125–135 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Box, G.E.P., Hunter, W.G., MacGregor, J.F., Erjavec, J.: Some problems associated with the analysis of multiresponse data. Technometrics 15(1), 33–51 (1973)zbMATHCrossRefGoogle Scholar
  4. 4.
    Darwiche, A.: Modeling and reasoning with Bayesian networks. Ebooks Corporation (2009)Google Scholar
  5. 5.
    Dematté, L., Priami, C., Romanel, A.: Modelling and simulation of biological processes in BlenX. ACM SIGMETRICS Performance Evaluation Review 35(4), 32–39 (2008)CrossRefGoogle Scholar
  6. 6.
    Dolan, E.D., Moré, J.J., Munson, T.S.: Benchmarking optimization software with COPS 3.0. Argonne National Laboratory Research Report (2004)Google Scholar
  7. 7.
    Forlin, M.: Knowledge discovery for stochastic models of biological systems. University of Trento, PhD Thesis (2010)Google Scholar
  8. 8.
    Geiger, D., Heckerman, D.: Learning gaussian networks (1994)Google Scholar
  9. 9.
    Hunt, H.G., Hawkins, J.E.: The rate of thermal isomerization of α-pinene and βpinene in the liquid phase. Journal of the American Chemical Society 72, 5618–5620 (1950)CrossRefGoogle Scholar
  10. 10.
    Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (2005)Google Scholar
  11. 11.
    Koller, D., Friedman, N.: Probabilistic graphical models: Principles and techniques. MIT Press (2009)Google Scholar
  12. 12.
    Liu, B., Wang, L., Jin, Y.H., Tang, F., Huang, D.X.: Improved particle swarm optimization combined with chaos. Chaos, Solitons & Fractals 25(5), 1261–1271 (2005)zbMATHCrossRefGoogle Scholar
  13. 13.
    Neapolitan, R.E.: Learning bayesian networks. Pearson Prentice Hall, Upper Saddle River (2004)Google Scholar
  14. 14.
    Reinker, S., Altman, R.M., Timmer, J.: Parameter estimation in stochastic biochemical reactions. IEE Proc. -Syst. Biol. 153(4), 168 (2006)CrossRefGoogle Scholar
  15. 15.
    Rodriguez-Fernandez, M., Egea, J.A., Banga, J.R.: Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems. BMC Bioinformatics 7(1), 483 (2006)CrossRefGoogle Scholar
  16. 16.
    Tsamardinos, I., Brown, L.E., Aliferis, C.F.: The max-min hill-climbing Bayesian network structure learning algorithm. Machine learning 65(1), 31–78 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.European Centre for Living TechnologyS. MarcoIT
  2. 2.Department of Environmental Sciences, Informatics and StatisticsUniversity Ca’Foscari of VeniceCannaregioIT

Personalised recommendations