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Bio-PEPA: A framework for the modelling and analysis of biological systems. (English) Zbl 1173.68041
Summary: We present Bio-PEPA, a process algebra for the modelling and the analysis of biochemical networks. It is a modification of PEPA, originally defined for the performance analysis of computer systems, in order to handle some features of biological models, such as stoichiometry and the use of general kinetic laws. Bio-PEPA may be seen as an intermediate, formal, compositional representation of biological systems, on which different kinds of analyses can be carried out. Bio-PEPA is enriched with some notions of equivalence. Specifically, the isomorphism and strong bisimulation for PEPA have been considered and extended to our language. Finally, we show the translation of a biological model into the new language and we report some analysis results.

MSC:
68Q85 Models and methods for concurrent and distributed computing (process algebras, bisimulation, transition nets, etc.)
92B05 General biology and biomathematics
92C40 Biochemistry, molecular biology
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[1] Arkin, A.P.; Rao, C.V., Stochastic chemical kinetics and the quasi-steady-state assumption: application to the Gillespie algorithm, Journal of chemical physics, 11, 4999-5010, (2003)
[2] Aziz, A.; Kanwal, K.; Singhal, V.; Brayton, V., Verifying continuous time Markov chains, (), 269-276
[3] Bornstein, B.J.; Doyle, J.C.; Finney, A.; Funahashi, A.; Hucka, M.; Keating, S.M.; Kitano, H.; Kovitz, B.L.; Matthews, J.; Shapiro, B.E.; Schilstra, M.J., Evolving a lingua franca and associated software infrastructure for computational systems biology: the systems biology markup language (SBML) project, Systems biology, 1, 41-53, (2004)
[4] Ramsey, S.; Orrell, D.; Bolouri, H., Dizzy: stochastic simulation of large-scale genetic regulatory networks, Journal of bioinformatics and computational biology, 3, 2, 415-436, (2005)
[5] Bortolussi, L.; Policriti, A., Modeling biological systems in stochastic concurrent constraint programming, Constraints, 13, 1, (2008) · Zbl 1144.92001
[6] Bortolussi, L.; Policriti, A., Stochastic concurrent constraint programming and differential equations, () · Zbl 1279.92031
[7] L. Bortolussi, Constraint-based approaches to stochastic dynamics of biological systems, University of Udine Ph.D. Thesis Series, CS2007/1, 2007
[8] Bortolussi, L.; Policriti, Alberto, Hybrid systems and biology, (), 424-448, (Chapter for the Tutorial of SFM-08:Bio)
[9] Bundschuh, R.; Hayot, F.; Jayaprakash, C., Fluctuations and slow variables in genetic networks, Biophysical journal, 84, 1606-1615, (2003)
[10] M. Calder, S. Gilmore, J. Hillston, Automatically deriving ODEs from process algebra models of signalling pathways, in: Proc. of CMSB’05, 2005, pp. 204-215
[11] Calder, M.; Gilmore, S.; Hillston, J., Modelling the influence of RKIP on the ERK signalling pathway using the stochastic process algebra PEPA, (), 1-23, Extended version in T. Comp. Sys. Biology VII
[12] Calder, M.; Duguid, A.; Gilmore, S.; Hillston, J., Stronger computational modelling of signalling pathways using both continuous and discrete-space methods, (), 63-77
[13] Calder, M.; Vyshemirsky, V.; Gilbert, D.; Orton, R., Analysis of signalling pathways using continuous time Markov chains, (), 44-67
[14] Cao, Y.; Gillespie, D.T.; Petzold, L., Accelerated stochastic simulation of the stiff enzyme – substrate reaction, Journal of chemical physics, 123, 14, 144917-144929, (2005)
[15] Baier, C.; Katoen, J.-P.; Hermanns, H., Approximate symbolic model checking of continuous-time Markov chains, (), 146-161 · Zbl 0934.03044
[16] Cardelli, L.; Panina, E.M.; Regev, A.; Shapiro, E.; Silverman, W., Bioambients: an abstraction for biological compartments, Theoretical computer science, 325, 1, 141-167, (2004) · Zbl 1069.68569
[17] Chabrier-Rivier, N.; Fages, F.; Soliman, S., Modelling and querying interaction networks in the biochemical abstract machine BIOCHAM, Journal of biological physics and chemistry, 4, 64-73, (2004)
[18] F. Ciocchetta, J. Hillston, Bio-PEPA: A framework for the modelling and analysis of biological systems, Technical report EDI-INF-RR-1231, University of Edinburgh, 2008 · Zbl 1173.68041
[19] Ciocchetta, F.; Hillston, J., Bio-PEPA: an extension of the process algebra PEPA for biochemical networks, (), 103-117 · Zbl 1279.68254
[20] F. Ciocchetta, S. Gilmore, M.L. Guerriero, J. Hillston, Integrated simulation and model-checking for the analysis of biochemical systems, in: Proc. of PASM 2008, in: ENTCS (in press)
[21] Ciocchetta, F.; Guerriero, M.L., Modelling biological compartments in bio-PEPA, (), 77-95 · Zbl 1348.92069
[22] F. Ciocchetta, A. Degasperi, J. Hillston, M. Calder, Some investigations concerning the CTMC and the ODE model derived from Bio-PEPA, in: Proc. of FBTC 2008, in: Electronic Notes in Theoretical Computer Science (ENTCS), vol. 229 (1), 2009, pp. 145-163 · Zbl 1283.92036
[23] Ciocchetta, F.; Priami, C., Biological transactions for quantitative models, (), 55-67 · Zbl 1277.68173
[24] Ciocchetta, F., The blenx language with biological transactions, () · Zbl 1241.92026
[25] Ciocchetta, F.; Priami, C.; Quaglia, P., Modeling Kohn interaction maps with beta-binders: an example, (), 33-48 · Zbl 1151.92308
[26] Costantin, G.; Laudanna, C.; Lecca, P.; Priami, C.; Quaglia, P.; Rossi, B., Language modeling and simulation of autoreactive lymphocytes recruitment in inflamed brain vessels, SIMULATION: transactions of the society for modeling and simulation international, 80, 273-288, (2003)
[27] Danos, V.; Laneve, C., Formal molecular biology, Theoretical computer science, 325, 1, 69-110, (2004) · Zbl 1071.68041
[28] Danos, V.; Feret, J.; Fontana, W.; Harmer, R.; Krivine, J., Ruled-based modelling of cellular signalling, () · Zbl 1151.68723
[29] V. Danos, J. Feret, W. Fontana, J. Krivine, Scalable simulation of cellular signalling networks, in: Proc. of APLAS’07, 2007 · Zbl 1151.68723
[30] V. Danos, J. Feret, W. Fontana, J. Krivine, Abstract interpretation of reachable complexes in biological signalling networks, in: Proc. of VMCAI’08, in: LNCS, Springer, 2008, pp. 83-97, http://dx.doi.org/10.1007/978-3-540-78163-9_11 · Zbl 1138.68650
[31] V. Danos, J. Krivine, Formal molecular biology done in CCS-R, in: Proc. of BioConcur’03, 2003
[32] Dematté, L.; Priami, C.; Romanel, A., Modelling and simulation of biological processes in blenx, SIGMETRICS performance evaluation review, 35, 4, 32-39, (2008) · Zbl 1160.68678
[33] Dematté, L.; Priami, C.; Romanel, A., The blenx language: A tutorial, (), 313-365, (Chapter for the Tutorial of SFM-08:Bio)
[34] Eichler-Jonsson, C.; Gilles, E.D.; Muller, G.; Schoeberl, B., Computational modeling of the dynamics of the MAP kinase cascade activated by surface and internalized EGF receptors, Nature biotechnology, 20, 370-375, (2002)
[35] Geisweiller, N.; Hillston, J.; Stenico, M., Relating continuous and discrete PEPA models of signalling pathways, Theoretical computer science, 404, 1-2, 97-111, (2008) · Zbl 1151.68038
[36] Gillespie, D.T., Exact stochastic simulation of coupled chemical reactions, Journal of physical chemistry, 81, 2340-2361, (1977)
[37] Heath, J.; Kwiatkowska, M.; Norman, G.; Parker, D.; Tymchyshyn, O., Probabilistic model checking of complex biological pathways, Theoretical computer science, 391, 239-257, (2008), (special issue on Converging Sciences: Informatics and Biology) · Zbl 1133.68043
[38] Hillston, J., A compositional approach to performance modelling, (1996), Cambridge University Press
[39] Kanehisa, M., A database for post-genome analysis, Trends in genetics, 13, 375-376, (1997)
[40] KEGG home page. Available at http://sbml.org/kegg2sbml.html
[41] Kierzek, A.M.; Puchalka, J., Bridging the gap between stochastic and deterministic regimes in the kinetic simulations of the biochemical reaction networks, Biophysical journal, 86, 1357-1372, (2004)
[42] Kuttler, C.; Niehren, J., Gene regulation in the \(\pi\)-calculus: simulating cooperativity at the lambda switch, (), 24-55
[43] NuMSV model checker. Available at http://nusmv.irst.itc.it
[44] Laneve, C.; Tarissan, F., A simple calculus for proteins and cells, (), 139-154 · Zbl 1277.68195
[45] Le Novére, N.; Bornstein, B.; Broicher, A.; Courtot, M.; Donizelli, M.; Dharuri, H.; Li, L.; Sauro, H.; Schilstra, M.; Shapiro, B.; Snoep, J.L.; Hucka, M., Biomodels database: A free centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems, Nucleic acids research, 34, D689-D691, (2006)
[46] Priami, C.; Quaglia, P., Beta-binders for biological interactions, (), 20-33 · Zbl 1088.68646
[47] Priami, C.; Regev, A.; Silverman, W.; Shapiro, E., Application of a stochastic name-passing calculus to representation and simulation of molecular processes, Information processing letters, 80, 25-31, (2001) · Zbl 0997.92018
[48] Prism web site. http://www.prismmodelchecker.org/
[49] BIOSPI Project. Available at http://www.wisdom.weizmann.ac.il/biospi/
[50] SPIM, The stochastic Pi-Machine. Available at www.doc.ic.ac.uk/anp/spim/
[51] The Kappa Factory. http://www.lix.polytechnique.fr/ krivine/kappaFactory.html
[52] Segel, I.H., Enzyme kinetics: behaviour and analysis of rapid equilibrium and steady-state enzyme systems, (1993), Wiley-Interscience New-York
[53] Wolkenhauer, O.; Ullah, M.; Kolch, W.; Cho, K.H., Modelling and simulation of intracellular dynamics: choosing an appropriate framework, IEEE transactions on nanobioscience, 3, 200-207, (2004)
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