Qualitative and Quantitative Analysis of a Bio-PEPA Model of the Gp130/JAK/STAT Signalling Pathway

Maria Luisa Guerriero

doi:10.1007/978-3-642-04186-0_5

Transactions on Computational Systems Biology XI pp 90-115

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5750)

Qualitative and Quantitative Analysis of a Bio-PEPA Model of the Gp130/JAK/STAT Signalling Pathway

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Maria Luisa Guerriero

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Abstract

Computational modelling of complex biochemical systems has grown in importance over recent years as a tool for supporting biological studies. Consequently, several formal languages have been recently proposed as modelling languages for biology. Among these, process algebras have been proved capable of providing researchers with new hypotheses on the behaviour of biochemical systems.

Bio-PEPA is a process algebra recently defined for the modelling and analysis of biochemical systems, which provides modellers with a wide range of analysis techniques: models can be analysed by stochastic simulation, model-checking, and mathematical methods based on ordinary differential equations.

In this work, we use Bio-PEPA for modelling the gp130/JAK/STAT signalling pathway, and we use both stochastic simulation and model-checking to analyse several qualitative and quantitative aspects of the system.

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References

1.
Regev, A., Silverman, W., Shapiro, E.: Representation and simulation of biochemical processes using the π-calculus process algebra. In: Proceedings of Pacific Symposium on Biocomputing (PSB 2001), vol. 6, pp. 459–470 (2001)Google Scholar
2.
Regev, A., Panina, E.M., Silverman, W., Cardelli, L., Shapiro, E.Y.: BioAmbients: an Abstraction for Biological Compartments. Theoretical Computer Science 325(1), 141–167 (2004)MathSciNetCrossRefMATHGoogle Scholar
3.
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
4.
Priami, C., Quaglia, P.: Operational patterns in Beta-binders. In: Priami, C. (ed.) Transactions on Computational Systems Biology I. LNCS (LNBI), vol. 3380, pp. 50–65. Springer, Heidelberg (2005)CrossRefGoogle Scholar
5.
Danos, V., Laneve, C.: Formal molecular biology. TCS 325(1) (2004)Google Scholar
6.
Regev, A., Shapiro, E.: Cells as Computation. Nature 419(6905), 343 (2002)CrossRefGoogle Scholar
7.
Ciocchetta, F., Hillston, J.: Bio-PEPA: an extension of the process algebra PEPA for biochemical networks. In: Proc. of FBTC 2007. ENTCS, vol. 194, pp. 103–117 (2008)Google Scholar
8.
Ciocchetta, F., Hillston, J.: Bio-PEPA: a Framework for the Modelling and Analysis of Biological Systems. Theoretical Computer Science 410(33-34), 3065–3084 (2009)MathSciNetCrossRefMATHGoogle Scholar
9.
Hillston, J.: A Compositional Approach to Performance Modelling. Cambridge University Press, Cambridge (1996)CrossRefMATHGoogle Scholar
10.
Ciocchetta, F., Hillston, J.: Calculi for Biological Systems. In: Formal Methods for Computational Systems Biology (SFM 2008). LNCS, vol. 5016, pp. 265–312. Springer, Heidelberg (2008)CrossRefGoogle Scholar
11.
Bio-PEPA Workbench Home Page: http://www.dcs.ed.ac.uk/home/stg/software/biopepa/
12.
Ramsey, S., Orrell, D., Bolouri, H.: Dizzy: stochastic simulation of large-scale genetic regulatory networks. J. Bioinf. Comp. Biol. 3(2), 415–436 (2005)CrossRefGoogle Scholar
13.
PRISM Home Page: http://www.prismmodelchecker.org
14.
Aziz, A., Sanwal, K., Singhal, V., Brayton, R.: Model-checking continuous-time Markov chains. ACM Trans. Comput. Logic 1(1), 162–170 (2000)MathSciNetCrossRefGoogle Scholar
15.
Underhill-Day, N., Heath, J.: Oncostatin M (OSM) Cytostasis of Breast Tumor Cells: Characterization of an OSM Receptor β-Specific Kernel. Cancer Research 66(22), 10891–10901 (2006)CrossRefGoogle Scholar
16.
Heinrich, P., Behrmann, I., Haan, S., Hermanns, H., Müller-Newen, G., Schaper, F.: Principles od interleukin (IL)-6-type cytokine signalling and its regulation. Biochem. J. 374, 1–20 (2003)CrossRefGoogle Scholar
17.
Kisseleva, T., Bhattacharya, S., Braunstein, J., Schindler, C.: Signaling through the JAK/STAT pathway, recent advances and future challenges. Gene 285, 1–24 (2002)CrossRefGoogle Scholar
18.
Swameye, I., Müller, T., Timmer, J., Sandra, O., Klingmüller, U.: Identification of nucleocytoplasmic cycling as a remote sensor in cellular signaling by databased modeling. PNAS 100, 1028–1033 (2003)CrossRefGoogle Scholar
19.
Mahdavi, A., Davey, R.E., Bhola, P., Yin, T., Zandstra, P.W.: Sensitivity Analysis of Intracellular Signaling Pathway Kinetics Predicts Targets for Stem Cell Fate Control. PLoS Computational Biology 3(7), 1257–1267 (2007)CrossRefGoogle Scholar
20.
Singh, A., Jayaraman, A., Hahn, J.: Modeling Regulatory Mechanisms in IL-6 Transduction in Hepatocytes. Biotechnology and Bioengineering 95(5), 850–862 (2006)CrossRefGoogle Scholar
21.
Guerriero, M.L., Dudka, A., Underhill-Day, N., Heath, J.K., Priami, C.: Narrative-based computational modelling of the Gp130/JAK/STAT signalling pathway. BMC Systems Biology 3(1), 40 (2009)CrossRefGoogle Scholar
22.
Bio-PEPA Home Page: http://www.biopepa.org/
23.
Hinton, A., Kwiatkowska, M., Norman, G., Parker, D.: PRISM: A tool for automatic verification of probabilistic systems. In: Hermanns, H., Palsberg, J. (eds.) TACAS 2006. LNCS, vol. 3920, pp. 441–444. Springer, Heidelberg (2006)CrossRefGoogle Scholar
24.
Dizzy Home Page: http://magnet.systemsbiology.net/software/Dizzy
25.
Aziz, A., Kanwal, K., Singhal, V., Brayton, V.: Verifying continuous time Markov chains. In: Alur, R., Henzinger, T.A. (eds.) CAV 1996. LNCS, vol. 1102, pp. 269–276. Springer, Heidelberg (1996)CrossRefGoogle Scholar
26.
Baier, C., Katoen, J.P., Hermanns, H.: Approximate Symbolic Model Checking of Continuous-Time Markov Chains. In: Baeten, J.C.M., Mauw, S. (eds.) CONCUR 1999. LNCS, vol. 1664, pp. 146–161. Springer, Heidelberg (1999)CrossRefGoogle Scholar
27.
Saez-Rodriguez, J., Kremling, A., Gilles, E.: Dissecting the puzzle of life: modularization of signal transduction networks. Computers and Chemical Engineering 29, 619–629 (2005)CrossRefGoogle Scholar
28.
Conzelmann, H., Saez-Rodriguez, J., Sauter, T., Bullinger, E., Allgöwer, F., Gilles, E.: Reduction of mathematical models of signal transduction networks: simulation-based approach applied to EGF receptor signalling. Systems Biology 1(1), 159–169 (2004)CrossRefGoogle Scholar
29.
Monteiro, P., Ropers, D., Mateescu, R., Freitas, A., de Jong, H.: Temporal logic patterns for querying dynamic models of cellular interaction networks. ECCB 24, 227–233 (2008)Google Scholar
30.
Gibson, M., Bruck, J.: Efficient Exact Stochastic Simulation of Chemical Systems with Many Species and Many Channels. The Journal of Chemical Physics 104, 1876–1889 (2000)CrossRefGoogle Scholar
31.
Dematté, L., Priami, C., Romanel, A.: The Beta Workbench: a computational tool to study the dynamics of biological systems. Briefings in Bioinformatics 9(5), 437–449 (2008), http://www.cosbi.eu/Rpty_Soft_BetaWB.php CrossRefGoogle Scholar
32.
Dematté, 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
33.
Heath, J., Kwiatkowska, M., Norman, G., Parker, D., Tymchyshyn, O.: Probabilistic Model Checking of Complex Biological Pathways. Theoretical Computer Science 319, 239–257 (2008)MathSciNetCrossRefMATHGoogle Scholar
34.
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
35.
Calder, M., Vyshemirsky, V., Gilbert, D., Orton, R.: Analysis of signalling pathways using continuous time Markov chains. In: Priami, C., Plotkin, G. (eds.) Transactions on Computational Systems Biology VI. LNCS (LNBI), vol. 4220, pp. 44–67. Springer, Heidelberg (2006)CrossRefGoogle Scholar
36.
The Idd-CSL Home Page: http://www-dssz.informatik.tu-cottbus.de/software/software.html
37.
Heiner, M., Gilbert, D., Donaldson, R.: Petri Nets for Systems and Synthetic Biology. In: Bernardo, M., Degano, P., Zavattaro, G. (eds.) SFM 2008. LNCS, vol. 5016, pp. 215–264. Springer, Heidelberg (2008)CrossRefGoogle Scholar
38.
The BIOCHAM Home Page: http://contraintes.inria.fr/BIOCHAM/
39.
Fages, F., Soliman, S., Chabrier-Rivier, N.: Modelling and querying interaction networks in the biochemical abstract machine BIOCHAM. Journal of Biological Physics and Chemistry 4(2), 64–73 (2004)CrossRefMATHGoogle Scholar
40.
NuSMV Home Page: http://nusmv.irst.itc.it/

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Authors and Affiliations

Maria Luisa Guerriero
- 1

1.Laboratory for Foundations of Computer ScienceThe University of EdinburghUK

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Cite this chapter as:: Guerriero M.L. (2009) Qualitative and Quantitative Analysis of a Bio-PEPA Model of the Gp130/JAK/STAT Signalling Pathway. In: Priami C., Back RJ., Petre I. (eds) Transactions on Computational Systems Biology XI. Lecture Notes in Computer Science, vol 5750. Springer, Berlin, Heidelberg

DOI 10.1007/978-3-642-04186-0_5
Publisher Name Springer, Berlin, Heidelberg
Print ISBN 978-3-642-04185-3
Online ISBN 978-3-642-04186-0
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Qualitative and Quantitative Analysis of a Bio-PEPA Model of the Gp130/JAK/STAT Signalling Pathway

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