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On P Systems as a Modelling Tool for Biological Systems

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Membrane Computing (WMC 2005)

Abstract

We introduce a variant of P systems where rules have associated a real number providing a measure for the “intrinsic reactivity”of the rule and roughly corresponding to the kinetic coefficient which, in bio-chemistry, is usually associated to each molecular reaction. The behaviour of these P systems is then defined according to a strategy which, in each step, randomly selects the next rule to be applied depending upon a certain distribution of probabilities. As an application, we present a P system model of the quorum sensing regulatory networks of the bacterium Vibrio Fischeri. In this respect, a formalisation of the network in terms of P systems is provided and some simulation results concerning the behaviour of a colony of such bacteria are reported. We also briefly describe the implementation techniques adopted by pointing out the generality of our approach which appears to be fairly independent from the particular choice of P system variant and the language used to implement it.

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References

  1. Alberts, B., Johnson, A., Lewis, J., Raff, M., Roberts, K., Walter, P.: The Molecular Biology of The Cell, 4th edn. Garland Publ. Inc., London (2002)

    Google Scholar 

  2. Andrei, O., Ciobanu, G., Lucanu, D.: Executable Specifications of P Systems. In: [10], pp. 126–145 (2005)

    Google Scholar 

  3. Bernardini, F., Gheorghe, M.: Population P systems. Journal of Universal Computer Science 10, 509–539 (2004)

    MathSciNet  Google Scholar 

  4. Besozzi, D.: Computational and Modelling Power of P systems. PhD Thesis, Università degli Studi di Milano, Milan, Italy (2004)

    Google Scholar 

  5. Bianco, L., Fontana, F., Franco, G., Manca, V.: P Systems for Biological Dynamics. In: Ciobanu, G., Păun, G., Pérez-Jiménez, M.J. (eds.) Applications of Membrane Computing, pp. 81–126. Springer, Heidelberg (2005)

    Google Scholar 

  6. Fargerströn, T., James, G., James, S., Kjelleberg, S., Nilsson, P.: Luminescence Control in the Marine Bacterium Vibrio Fischeri: An Analysis of the Dynamics of lux Regulation. Journal of Molecular Biology 296, 1127–1137 (2000)

    Article  Google Scholar 

  7. Gibson, M.A., Bruck, J.: Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. Journal of Physical Chemistry 104(25), 1876–1889 (2000)

    Google Scholar 

  8. Gillespie, D.T.: Exact Stochastic Simulation of Coupled Chemical Reactions. The Journal of Physical Chemistry 81(25), 2340–2361 (1977)

    Article  Google Scholar 

  9. Martin-Vide, C., Mauri, G., Păun, G., Rozenberg, G., Salomaa, A. (eds.) WMC 2003. LNCS, vol. 2933. Springer, Heidelberg (2003)

    Google Scholar 

  10. Mauri, G., Păun, G., Pérez-Jiménez, M.J., Rozenberg, G., Salomaa, A. (eds.): WMC 2004. LNCS, vol. 3365. Springer, Heidelberg (2005)

    Google Scholar 

  11. Meng, T.C., Somani, S., Dhar, P.: Modelling and Simulation of Biological Systems with Stochasticity. In Silico Biology 4 (2004)

    Google Scholar 

  12. Nepomuceno, I., Nepomuceno, J.: A Tool for Using the SBML Format to Represent P System which Model Biological Reaction Networks. In: Proceeding of the Third Brainstorming Week in Membrane Computing, University of Seville, Seville, Spain, January 31st-February 4th (2005)

    Google Scholar 

  13. Păun, G.: Computing with Membranes. Journal of Computer and System Sciences 61(1), 108–143 (2000)

    Article  MATH  MathSciNet  Google Scholar 

  14. Păun, G.: Membrane Computing. An Introduction. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  15. Păun, G., Rozenberg, G., Salomaa, A., Zandron, C. (eds.) WMC 2002. LNCS, vol. 2597. Springer, Heidelberg (2003)

    Google Scholar 

  16. Pérez-Jiménez, M.J., Romero-Campero, F.J.: Modelling EGFR Signalling Cascade Using Continuous Membrane Systems. In: Plotkin, G. (ed.) Proceedings of the Third International Workshop on Computational Methods in Systems Biology 2005 (CMSB 2005), University of Edinburgh, Edinburgh, United Kingdom (2005)

    Google Scholar 

  17. Philips, A., Cardelli, L.: A Correct Abstract Machine for the Stochastic Pi-calculus. Electronical Notes in Theoretical Computer Science (2004) (to appear)

    Google Scholar 

  18. Priami, C., Regev, A., Shapiro, E., Silverman, W.: Application of a Stochastic Name-Passing Calculus to Representation and Simulation of Molecular Processes. Information Processing Letters 80, 25–31 (2001)

    Article  MATH  MathSciNet  Google Scholar 

  19. Taga, M.E., Bassler, B.L.: Chemical Communication among Bacteria. Proceedings of the National Academy of Sciences of the United States of America PNAS 100(2), 14549–14554 (2003)

    Article  Google Scholar 

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Bernardini, F., Gheorghe, M., Krasnogor, N., Muniyandi, R.C., Pérez-Jímenez, M.J., Romero-Campero, F.J. (2006). On P Systems as a Modelling Tool for Biological Systems. In: Freund, R., Păun, G., Rozenberg, G., Salomaa, A. (eds) Membrane Computing. WMC 2005. Lecture Notes in Computer Science, vol 3850. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11603047_8

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  • DOI: https://doi.org/10.1007/11603047_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30948-2

  • Online ISBN: 978-3-540-32340-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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