Grid Computing for Sensitivity Analysis of Stochastic Biological Models

  • Ivan Merelli
  • Dario Pescini
  • Ettore Mosca
  • Paolo Cazzaniga
  • Carlo Maj
  • Giancarlo Mauri
  • Luciano Milanesi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6873)


Systems biology is a multidisciplinary research area aimed at investigating biological systems by developing mathematical models that approach the study and the analysis of both the structure and behaviour of a biological phenomenon from a system perspective. The dynamics described by such mathematical models can be deeply affected by many parameters, and an extensive exploration of the parameters space in order to find crucial factors is most of the time prohibitive since it requires the execution of a huge number of computer simulations. Sensitivity analysis techniques can help in understanding how much the uncertainty in the model outcome is determined by the uncertainties, or by the variations, of the model input factors (components, reactions and respective parameters). In this work we exploit the European Grid Infrastructure to manage the calculations required to perform the SA on a stochastic model of bacterial chemotaxis, using an improved version of the first order screening method of Morris. According to the results achieved in our exploratory analysis, the European Grid Infrastructure is a useful solution for distributing the stochastic simulations required to carry out the SA of a stochastic model. Considering that the more intensive the computation the more scalable the infrastructure, grid computing can be a suitable technology for large scale biological models analysis.


Grid Computing Stochastic Simulation Input Factor Biological Model Elementary Effect 
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.


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  1. 1.
    Elowitz, M.B., Levine, A.J., Siggia, E.D., Swain, P.S.: Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002)CrossRefGoogle Scholar
  2. 2.
    Arkin, A., Ross, J., McAdams, H.H.: Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected Escherichia coli cells. Genetics 149, 1633–1648 (1998)Google Scholar
  3. 3.
    Turner, T.E., Schnell, S., Burrage, K.: Stochastic approaches for modelling in vivo reactions. Comput. Biol. Chem. 28, 165–178 (2004)CrossRefzbMATHGoogle Scholar
  4. 4.
    Mosca, E., Merelli, I., Milanesi, L., Cazzaniga, P., Pescini, D., Mauri, G.: Stochastic simulations on a grid framework for parameter sweep applications in biological models. In: International Workshop on High Performance Computational Systems Biology, HIBI 2009, pp. 33–42 (2009)Google Scholar
  5. 5.
    Cazzaniga, P., Pescini, D., Besozzi, D., Mauri, G.: Tau leaping stochastic simulation method in P systems. In: Hoogeboom, H.J., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2006. LNCS, vol. 4361, pp. 298–313. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Saltelli, A., Ratto, M., Andres, T.: Global sensitivity analysis: the primer. Wiley Online Library (2008)Google Scholar
  7. 7.
    Gunawan, R., Cao, Y., Petzold, L., Doyle, F.J.: Sensitivity analysis of discrete stochastic systems. Biophys. J. 88, 2530–2540 (2005)CrossRefGoogle Scholar
  8. 8.
    Plyasunov, S., Arkin, A.P.: Efficient stochastic sensitivity analysis of discrete event systems. J. Comp. Phys. 221, 724–738 (2007)CrossRefzbMATHGoogle Scholar
  9. 9.
    Morris, M.: Factorial sampling plans for preliminary computational experiments. Technometrics 33, 161–174 (1991)CrossRefGoogle Scholar
  10. 10.
    Campolongo, F., Cariboni, J., Saltelli, A.: An effective screening design for sensitivity analysis of large models. Environmental modelling & software 22, 1509–1518 (2007)CrossRefGoogle Scholar
  11. 11.
    Degasperi, A., Gilmore, S.: Sensitivity analysis of stochastic models of bistable biochemical reactions. In: Bernardo, M., Degano, P., Tennenholtz, M. (eds.) SFM 2008. LNCS, vol. 5016, pp. 1–20. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  12. 12.
    Jurica, M.S., Stoddard, B.L.: Mind your b’s and r’s: bacterial chemotaxis, signal transduction and protein recognition. Structure 6, 809–813 (1998)CrossRefGoogle Scholar
  13. 13.
    Wadhams, G.H., Armitage, J.P.: Making sense of it all: bacterial chemotaxis. Nat. Rev. Mol. Cell Biol. 5, 1024–1037 (2004)CrossRefGoogle Scholar
  14. 14.
    Besozzi, D., Cazzaniga, P., Dugo, M., Pescini, D., Mauri, G.: A study on the combined interplay between stochastic fluctuations and the number of flagella in bacterial chemotaxis. EPTCS 6, 47–62 (2009)CrossRefGoogle Scholar
  15. 15.
    Foster, I., Kesselman, C., Tuecke, S.: The anatomy of the grid: enabling scalable virtual organizations. Int. J. High Perform. Comput. Appl. 15, 200–222 (2001)CrossRefGoogle Scholar
  16. 16.
    Laure, E., Fisher, S., Frohner, A., Grandi, C., Kunszt, P., Krenek, A., Mulmo, O., Pacini, F., Prelz, F., White, J., Barroso, M., Bunic, P., Hemmer, F., Meglio, A.D., Edlund, A.: Programming the grid with glite. Comp. Meth. Sci. Tech. 12(1), 33–45 (2006)CrossRefGoogle Scholar
  17. 17.
    Campana, S., Rebatto, D., Sciabá, A.: Experience with the glite workload management system in atlas monte carlo production on lcg. J. Phys. Conf. Ser. 119 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ivan Merelli
    • 1
  • Dario Pescini
    • 2
  • Ettore Mosca
    • 1
  • Paolo Cazzaniga
    • 3
  • Carlo Maj
    • 4
  • Giancarlo Mauri
    • 4
  • Luciano Milanesi
    • 1
  1. 1.Istituto Tecnologie BiomedicheConsiglio Nazionale RicercheSegrateItaly
  2. 2.Dipartimento di StatisticaUniversità degli Studi di Milano-BicoccaMilanoItaly
  3. 3.Dipartimento di Scienze della PersonaUniversità degli Studi di BergamoBergamoItaly
  4. 4.Dipartimento di Informatica, Sistemistica e ComunicazioneUniversità degli Studi di Milano-BicoccaMilanoItaly

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