Abstract
In this paper, we address the problem of modeling biological regulatory networks thanks to the stochastic π-calculus. We propose a method which extends a logical method, that is the approach of René Thomas. By introducing temporal and stochastic aspects there, we make our formalism closer to biological reality. We then use the SPiM stochastic simulator to illustrate the practical interests of this description. The application example concerns the behaviors of four interacting genes involved in the λ-phage. Interesting results are emerging from the simulations. First, it confirms knowledge of the regulation phenomena. In addition, experiments with different values of the delay parameters give some precious hints of a tendency either for the lytic phase or to the lysogenic phase.
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References
De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1), 67–103 (2002)
Arkin, A., Ross, J., McAdams, H.H.: Stochastic kinetic analysis of developmental pathway bifurcation in phage λ-infected Escherichia coli cells. Genetics 149, 1633–1648 (1998)
Petri, C.A.: Kommunikation mit Automaten. PhD thesis, Bonn: Institut für Instrumentelle Mathematik, Schriften des IIM Nr. 2 (1962); 2nd edn., New York: Griffiss Air Force Base, Technical Report RADC-TR-65–377, vol. 1(suppl. 1), English translation (1966)
Chaouiya, C., Remy, E., Thieffry, D.: Petri net modelling of biological regulatory networks. J. of Discrete Algorithms 6(2), 165–177 (2008)
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)
Siebert, H., Bockmayr, A.: Incorporating time delays into the logical analysis of gene regulatory networks. In: Priami, C. (ed.) CMSB 2006. LNCS (LNBI), vol. 4210, pp. 169–183. Springer, Heidelberg (2006)
Adélaïde, M., Sutre, G.: Parametric analysis and abstraction of genetic regulatory networks. In: Proc. 2nd Workshop on Concurrent Models in Molecular Biol. (BioCONCUR 2004), London, UK. Electronic Notes in Theor. Comp. Sci. Elsevier, Amsterdam (2004)
Ahmad, J., Bernot, G., Comet, J.P., Lime, D., Roux, O.: Hybrid modelling and dynamical analysis of gene regulatory networks with delays. ComPlexUs 3(4), 231–251 (2007)
Eker, S., Laderoute, K., Lincoln, P., Talcott, C.: Pathway logic: executable models of biological networks. In: Fourth International Workshop on Rewriting Logic and Its Applications (WRLA 2002), Elsevier, Amsterdam (2002)
Talcott, C.: Formal executable models of cell signaling primitives. In: Margaria, T., Philippou, A., Steffen, B. (eds.) 2nd International Symposium On Leveraging Applications of Formal Methods, Verification and Validation ISOLA 2006, pp. 303–307 (2006)
Calzone, L., Fages, F., Soliman, S.: Biocham: an environment for modeling biological systems and formalizing experimental knowledge. Bioinformatics 22(14), 1805–1807 (2006)
Calzone, L., Chabrier-rivier, N., Fages, F., Soliman, S., Rocquencourt, I., Contraintes, P.: A machine learning approach to biochemical reaction rules discovery. In: Doyle III, F.J. (ed.) Proceedings of Foundations of Systems Biology and Engineering FOSBE 2005, pp. 375–379 (2005)
Regev, A., Panina, A., Silverman, W., Cardelli, L., Shapiro, E.: Bioambients: An abstraction for biological compartments. Elsevier Science, Amsterdam (2003)
Regev, A., Silverman, W., Shapiro, E.: Representation and simulation of biochemical processes using the pi-calculus process algebra. In: Proc. Pacific Symp. of Biocomputing, vol. 6, pp. 459–470 (2001)
Priami, C., Regev, A., Shapiro, E.Y., Silverman, W.: Application of a stochastic name-passing calculus to representation and simulation of molecular processes. Inf. Process. Lett. 80(1), 25–31 (2001)
Lecca, P., Priami, C.: Cell cycle control in eukaryotes: A biospi model. Electr. Notes Theor. Comput. Sci. 180(3), 51–63 (2007)
Kuttler, C., Niehren, J.: Gene regulation in the pi calculus: Simulating cooperativity at the lambda switch. Transactions on Computational Systems Biology VII 4230, 24–55 (2006)
Ptashne, M.: A genetic switch: Phage λ and higher organisms, 2nd edn. Cell Press and Blackwell science, Malden (1992)
Ackers, G.K., Johnson, A.D., Shea, M.A.: Quantitative model for gene regulation by λ phage repressor. Proceedings of the National Academy of Science USA 79, 1129–1133 (1982)
Shea, M.A., Ackers, G.K.: The or control system of bacteriophage lambda: a physical-chemical model for gene regulation. J. Mol. Biol. 181, 211–230 (1985)
Sauer, R.T.: Molecular characterization of the lambda repressor and its gene ci. Harvard University Press, Cambridge (1979)
Phillips, A., Cardelli, L.: Spim (2007), http://research.microsoft.com/~aphillip/spim/
Priami, C.: Stochastic π-calculus. Computer Journal 6, 578–589 (1995)
Milner, R.: A calculus of mobile processes. Information and computation 100, 1–77 (1992)
Phillips, A., Cardelli, L.: Efficient, correct simulation of biological processes in the stochastic pi-calculus. In: Bošnački, D., Edelkamp, S. (eds.) SPIN 2007. LNCS, vol. 4595, pp. 184–199. Springer, Heidelberg (2007)
Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. The Journal of Physical Chemistry 81(25), 2340–2361 (1977)
Phillips, A.: The SPIM Language. Version 0.05 (2007)
Thieffry, D., Thomas, R.: Dynamical behaviour of biological regulatory networks - ii. immunity control in bacteriophage lambda. Bull. Math. Biol. 57(2), 277–297 (1995)
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Maurin, M., Magnin, M., Roux, O. (2009). Modeling of Genetic Regulatory Network in Stochastic π-Calculus. In: Rajasekaran, S. (eds) Bioinformatics and Computational Biology. BICoB 2009. Lecture Notes in Computer Science(), vol 5462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00727-9_27
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DOI: https://doi.org/10.1007/978-3-642-00727-9_27
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