Rule-Based Modelling and Model Perturbation

  • Vincent Danos
  • Jérôme Feret
  • Walter Fontana
  • Russ Harmer
  • Jean Krivine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5750)


Rule-based modelling has already proved to be successful for taming the combinatorial complexity, typical of cellular signalling networks, caused by the combination of physical protein-protein interactions and modifications that generate astronomical numbers of distinct molecular species. However, traditional rule-based approaches, based on an unstructured space of agents and rules, remain susceptible to other combinatorial explosions caused by mutated and/or splice variant agents, that share most but not all of their rules with their wild-type counterparts; and by drugs, which must be clearly distinguished from physiological ligands.

In this paper, we define a syntactic extension of Kappa, an established rule-based modelling platform, that enables the expression of a structured space of agents and rules that allows us to express mutated agents, splice variants, families of related proteins and ligand/drug interventions uniformly. This also enables a mode of model construction where, starting from the current consensus model, we attempt to reproduce in numero the mutational—and more generally the ligand/drug perturbational—analyses that were used in the process of inferring those pathways in the first place.


Generic Agent Consensus Model Model Perturbation Dime Partner Multiple Splice Variant 
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.
    Kholodenko, B.N., Demin, O.V., Moehren, G., Hoek, J.B.: Quantification of Short Term Signaling by the Epidermal Growth Factor Receptor. J. Biol. Chem. 274(42), 30169–30181 (1999)CrossRefGoogle Scholar
  2. 2.
    Kiyatkin, A., Aksamitiene, E., Markevich, N.I., Borisov, N.M., Hoek, J.B., Kholodenko, B.N.: Scaffolding protein GAB1 sustains epidermal growth factor-induced mitogenic and survival signaling by multiple positive feedback loops. J. Biol. Chem. 281, 19925–19938 (2006)CrossRefGoogle Scholar
  3. 3.
    Orton, R.J., Sturm, O.E., Vyshemirsky, V., Calder, M., Gilbert, D.R., Kolch, W.: Computational modelling of the receptor tyrosine kinase activated MAPK pathway. Biochemical Journal 392(2), 249–261 (2005)CrossRefGoogle Scholar
  4. 4.
    Schoeberl, B., Eichler-Jonsson, C., Gilles, E.-D., Müller, G.: Computational modeling of the dynamics of the map kinase cascade activated by surface and internalized EGF receptors. Nature Biotechnology 20, 370–375 (2002)CrossRefGoogle Scholar
  5. 5.
    Hlavacek, W.S., Faeder, J.R., Blinov, M.L., Posner, R.G., Hucka, M., Fontana, W.: Rules for Modeling Signal-Transduction Systems. Science’s STKE 2006(344) (2006)Google Scholar
  6. 6.
    Maslov, S., Ispolatov, I.: Propagation of large concentration changes in reversible protein-binding networks. Proceedings of the National Academy of Sciences 104(34), 13655–13660 (2007)CrossRefGoogle Scholar
  7. 7.
    Regev, A., Silverman, W., Shapiro, E.: Representation and simulation of biochemical processes using the π-calculus process algebra. In: Altman, R.B., Dunker, A.K., Hunter, L., Klein, T.E. (eds.) Pacific Symposium on Biocomputing, vol. 6, pp. 459–470. World Scientific Press, Singapore (2001)Google Scholar
  8. 8.
    Regev, A., Shapiro, E.: Cells as computation. Nature 419 (September 2002)Google Scholar
  9. 9.
    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 (2001)Google Scholar
  10. 10.
    Baldi, C., Degano, P., Priami, C.: Causal π-calculus for biochemical modeling. In: Proceedings of the AI*IA Workshop on BioInformatics 2002, pp. 69–72 (2002)Google Scholar
  11. 11.
    Priami, C., Quaglia, P.: Beta Binders for Biological Interactions. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 20–33. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  12. 12.
    Cardelli, L.: Brane Calculi Interactions of Biological Membranes. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 257–278. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Regev, A., Panina, E.M., Silverman, W., Cardelli, L., Shapiro, E.: BioAmbients: an abstraction for biological compartments. Theoretical Computer Science 325, 141–167 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    John, M., Ewald, R., Uhrmacher, A.M.: A Spatial Extension to the π Calculus. Electronic Notes in Theoretical Computer Science, vol. 194(3), pp. 133–148 (2008)Google Scholar
  15. 15.
    Calder, M., Gilmore, S., Hillston, J.: Modelling the influence of RKIP on the ERK signalling pathway using the stochastic process algebra PEPA. In: Priami, C., Ingólfsdóttir, A., Mishra, B., Riis Nielson, H. (eds.) Transactions on Computational Systems Biology VII. LNCS (LNBI), vol. 4230, pp. 1–23. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  16. 16.
    Ciocchetta, F., Hillston, J.: Bio-PEPA: an extension of the process algebra PEPA for biochemical networks. Electronic Notes in Theoretical Computer Science, vol. 194(3), pp. 103–117 (2008)Google Scholar
  17. 17.
    Calzone, L., Fages, F., Soliman, S.: BIOCHAM: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 22(14), 1805–1807 (2006)CrossRefGoogle Scholar
  18. 18.
    Dematte, L., Priami, C., Romanel, A.: The BlenX language: a tutorial. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 313–365. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Blinov, M.L., Faeder, J.R., Hlavacek, W.S.: BioNetGen: software for rule-based modeling of signal transduction based on the interactions of molecular domains. Bioinformatics 20, 3289–3292 (2004)CrossRefGoogle Scholar
  20. 20.
    Dematté, L., Priami, C., Romanel, A., Soyer, O.: Evolving BlenX programs to simulate the evolution of biological networks. Theoretical Computer Science 408(1), 83–96 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Danos, V., Laneve, C.: Formal molecular biology. Theoretical Computer Science 325(1), 69–110 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Danos, V., Feret, J., Fontana, W., Krivine, J.: Abstract Interpretation of Cellular Signalling Networks. In: Logozzo, F., Peled, D.A., Zuck, L.D. (eds.) VMCAI 2008. LNCS, vol. 4905, pp. 83–97. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  23. 23.
    Danos, V., Feret, J., Fontana, W., Harmer, R., Krivine, J.: Rule-Based Modelling of Cellular Signalling. In: Caires, L., Vasconcelos, V.T. (eds.) CONCUR 2007. LNCS, vol. 4703, pp. 17–41. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  24. 24.
    Danos, V., Feret, J., Fontana, W., Krivine, J.: Scalable Simulation of Cellular Signaling Networks. In: Shao, Z. (ed.) APLAS 2007. LNCS, vol. 4807, pp. 139–157. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  25. 25.
    Murphy, L.O., Smith, S., Chen, R.H., Fingar, D.C., Blenis, J.: Molecular interpretation of ERK signal duration by immediate early gene products. Nat. Cell Biol. 4(8), 556–564 (2002)Google Scholar
  26. 26.
    Burgess, A.W., Cho, H.S., Eigenbrot, C., Ferguson, K.M., Garrett, T.P.J., Leahy, D.J., Lemmon, M.A., Sliwkowski, M.X., Ward, C.W., Yokoyama, S.: An Open-and-Shut Case? Recent Insights into the Activation of EGF/ErbB Receptors. Molecular Cell 12(3), 541–552 (2003)CrossRefGoogle Scholar
  27. 27.
    Zhang, X., Gureasko, J., Shen, K., Cole, P.A., Kuriyan, J.: An Allosteric Mechanism for Activation of the Kinase Domain of Epidermal Growth Factor Receptor. Cell 125(6), 1137–1149 (2006)CrossRefGoogle Scholar
  28. 28.
    Sampaio, C., Dance, M., Montagner, A., Edouard, T., Malet, N., Perret, B., Yart, A., Salles, J., Raynal, P.: Signal strength dictates phosphoinositide 3-kinase contribution to Ras/extracellular signal-regulated kinase 1 and 2 activation via differential Gab1/Shp2 recruitment: consequences for resistance to epidermal growth factor receptor inhibition. Mol. Cell Biol. 28(2), 587–600 (2008)CrossRefGoogle Scholar
  29. 29.
    Zhang, X., Pickin, K.A., Bose, R., Jura, N., Cole, P.A., Kuriyan, J.: Inhibition of the EGF receptor by binding of MIG6 to an activating kinase domain interface. Nature 450(7170), 741 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Vincent Danos
    • 1
  • Jérôme Feret
    • 2
  • Walter Fontana
    • 3
  • Russ Harmer
    • 4
  • Jean Krivine
    • 3
    • 5
  1. 1.University of EdinburghUK
  2. 2.INRIA–ENS–CNRSFrance
  3. 3.Harvard Medical SchoolUSA
  4. 4.CNRS–Université Paris DiderotFrance
  5. 5.Institut des Hautes Etudes ScientifiquesFrance

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