Rule-Based Modelling and Model Perturbation
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.
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- 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
- 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.Regev, A., Shapiro, E.: Cells as computation. Nature 419 (September 2002)Google Scholar
- 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.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
- 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.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.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
- 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
- 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