Modeling Signal Transduction Using P Systems

  • Andrei Păun
  • Mario J. Pérez-Jiménez
  • Francisco J. Romero-Campero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4361)


Cellular signalling pathways are fundamental to the control and regulation of cell behavior. Understanding of biosignalling network functions is crucial to the study of different diseases and to the design of effective therapies. In this paper we present P systems as a feasible computational modeling tool for cellular signalling pathways that takes into consideration the discrete character of the components of the system and the key role played by membranes in their functioning. We illustrate these cellular models simulating the epidermal growth factor receptor (EGFR) signalling cascade and the FAS–induced apoptosis using a deterministic strategy for the evolution of P systems.


Epidermal Growth Factor Receptor Epidermal Growth Factor Epidermal Growth Factor Receptor Signalling Cellular Signalling Pathway Membrane Computing 
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.
    Alimonti, J.B., Ball, T.B., Fowke, K.R.: Mechanisms of CD4+ T lymphocyte cell death in human immunodeficiency virus infection and AIDS. Journal of General Virology 84, 1649–1661 (2003)CrossRefGoogle Scholar
  2. 2.
    Bhalla, U.S., Iyengar, R.: Emergent properties of networks of biological signaling pathways. Science 283, 381–387 (1999)CrossRefGoogle Scholar
  3. 3.
    Blossey, R., Cardelli, L., Phillips, A.: A compositional approach to the stochastic dynamics of gene networks. In: Priami, C., Cardelli, L., Emmott, S. (eds.) Transactions on Computational Systems Biology IV. LNCS (LNBI), vol. 3939, pp. 99–122. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  4. 4.
    Cheng, E.H., Wei, M.C., Weiler, S., Flavell, R.A., Mak, T.W., Lindsten, T., Korsmeyer, S.J.: BCL-2, BCL-XL sequester BH3 domain-only molecules preventing BAX- and BAK-mediated mitochondrial apoptosis. Molecular Cell 8, 705–711 (2001)CrossRefGoogle Scholar
  5. 5.
    Cheruku, S., Păun, A., Romero, F.J., Pérez–Jiménez, M.J., Ibarra, O.H.: Simulating FAS–induced apoptosis by using P systems. In: Proceedings of the First International Conference on Bio–Inspired Computing: Theory and Applications, Wuhan, China, September 18-22 (2006)Google Scholar
  6. 6.
    Hua, F., Cornejo, M., Cardone, M., Stokes, C., Lauffenburger, D.: Effects of Bcl-2 levels on FAS signaling-induced caspase-3 activation: Molecular genetic tests of computational model predictions. The Journal of Immunology 175(2), 985–995 (2005); correction 175(9), 6235–6237 (2005)Google Scholar
  7. 7.
    Ibarra, O.H., Păun, A.: Counting time in computing with cells. In: Proceedings of DNA Based Computing, DNA11, London, Ontario, pp. 25–36 (2005)Google Scholar
  8. 8.
    Kerr, J.F., Wyllie, A.H., Currie, A.R.: Apoptosis: a basic biological phenomenon with wide-ranging implications in tissue kinetics. British Journal Cancer 26, 239 (1972)CrossRefGoogle Scholar
  9. 9.
    Pogson, M., Smallwood, R., Qwarnstrom, E.: Holcombe, Formal agent–based of intracellular chemical interactions. BioSystems 85(1), 37–45 (2006)CrossRefGoogle Scholar
  10. 10.
    Holcombe, M., Gheorghe, M., Talbot, N.: A hybrid machine model of rice blast fungus, Magnaphorte Grisea. BioSystems 68(2-3), 223–228 (2003)CrossRefGoogle Scholar
  11. 11.
    Jaatela, M.: Multiple cell death pathways as regulators of tumour initiation and progression. Oncogene 23, 2746–2756 (2004)CrossRefGoogle Scholar
  12. 12.
    Jackson, D., Holcombe, M., Ratnieks, F.: Trail geometry gives polarity to ant foraging networks. Nature 432, 907–909 (2004)CrossRefGoogle Scholar
  13. 13.
    Moehren, G., Markevich, N., Demin, O., Kiyatkin, A., Goryanin, I., Hoek, J.B., Kholodenko, B.N.: Temperature dependence of the epidermal growth factor receptor signaling network can be accounted for by a kinetic model. Biochemistry 41, 306–320 (2002)CrossRefGoogle Scholar
  14. 14.
    Moghal, N., Sternberg, P.W.: Multiple positive and negative regulators of signaling by the EGFR receptor. Curr. Opin. Cell Biology 11, 190–196 (1999)CrossRefGoogle Scholar
  15. 15.
    Nijhawan, D., Honarpour, N., Wang, X.: Apotosis in neural development and disease. Annual Reviews Neuroscience 23, 73–87 (2000)CrossRefGoogle Scholar
  16. 16.
    Oltavi, Z.N., Milliman, C.L., Korsmeyer, S.J.: Bcl-2 heterodimerizes in vivo with a conserved homolog, Bax, that accelerates programmed cell death. Cell 74(4), 609–619 (1993)CrossRefGoogle Scholar
  17. 17.
    Păun, G.: Computing with membranes. Journal of Computer and System Sciences 61(1), 108–143 (2000); Turku Center for Computer Science-TUCS Report Nr. 208 (1998)Google Scholar
  18. 18.
    Păun, G.: Membrane Computing. An Introduction. Springer, Berlin (2002)zbMATHGoogle Scholar
  19. 19.
    Păun, G., Rozenberg, G.: A guide to membrane computing. Theoretical Computer Science 287, 73–100 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Jesús Pérez-Jímenez, M., Romero-Campero, F.J.: P systems, a new computational modelling tool for systems biology. In: Priami, C., Plotkin, G. (eds.) Transactions on Computational Systems Biology VI. LNCS (LNBI), vol. 4220, pp. 176–197. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  21. 21.
    Jesús Pérez-Jímenez, M., Romero-Campero, F.J.: A study of the robustness of the EGFR signalling cascade using continuous membrane systems. In: Mira, J., Álvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3561, pp. 268–278. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  22. 22.
    Regev, A., Shapiro, E.: The π-calculus as an abstraction for biomolecular systems. In: Ciobanu, G., Rozenberg, G. (eds.) Modelling in Molecular Biology. Springer, Berlin (2004)Google Scholar
  23. 23.
    Scaffidi, C., Fulda, S., Srinivasan, A., Friesen, C., Li, F., Tomaselli, K.J., Debatin, K.M., Krammer, P.H., Peter, M.E.: Two CD95 (APO-1/Fas) signaling pathways. The Embo Journal 17, 1675–1687 (1998)CrossRefGoogle Scholar
  24. 24.
    Schoeberl, B., Eichler–Jonsson, C., Gilles, E.D., Muller, G.: Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors. Nature Biotechnology 20(4), 370–375 (2002)CrossRefGoogle Scholar
  25. 25.
    Van Kampen, N.G.: Stochastics Processes in Physics and Chemistry. Elsevier Science B.V., Amsterdam (1992)Google Scholar
  26. 26.
    Walker, D.C., Southgate, J., Hill, G., Holcombe, M., Hose, D.R., Wood, S.M., MacNeil, S., Smallwood, R.H.: The epitheliome: modelling the social behaviour of cells. BioSystems 76(1-3), 89–100 (2004)CrossRefGoogle Scholar
  27. 27.
    Wang, K., Yin, X.M., Chao, D.T., Milliman, C.L., Korsmeyer, S.J.: BID: a novel BH3 domain-only death agonist. Genes & Development 10, 2859–2869 (1996)CrossRefGoogle Scholar
  28. 28.
    Wells, A.: EGFR–receptor. Int. Journal Biochem. Cell Biology 31, 637–643 (1999)CrossRefGoogle Scholar
  29. 29.
    Wiley, H.S., Shvartsman, S.Y., Lauffenburger, D.A.: Computational modeling of the EGFR–receptor system: A paradigm for systems biology. Trends in Cell Biology 13(1), 43–50 (2003)CrossRefGoogle Scholar
  30. 30.
  31. 31.
    SciLab Web Site:
  32. 32.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andrei Păun
    • 1
  • Mario J. Pérez-Jiménez
    • 2
  • Francisco J. Romero-Campero
    • 2
  1. 1.Department of Computer Science/IfMLouisiana Tech UniversityRustonUSA
  2. 2.Department of Computer Science and Artificial IntelligenceUniversity of SevillaSevillaSpain

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