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Model Revision from Temporal Logic Properties in Computational Systems Biology

  • François Fages
  • Sylvain Soliman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4911)

Abstract

Systems biologists build models of bio-molecular processes from knowledge acquired both at the gene and protein levels, and at the phenotype level through experiments done in wild-life and mutated organisms. In this chapter, we present qualitative and quantitative logic learning tools, and illustrate how they can be useful to the modeler. We focus on biochemical reaction models written in the Systems Biology Markup Language SBML, and interpreted in the Biochemical Abstract Machine BIOCHAM. We first present a model revision algorithm for inferring reaction rules from biological properties expressed in temporal logic. Then we discuss the representations of kinetic models with ordinary differential equations (ODEs) and with stochastic logic programs (SLPs), and describe a parameter search algorithm for finding parameter values satisfying quantitative temporal properties. These methods are illustrated by a simple model of the cell cycle control, and by an application to the modelling of the conditions of synchronization in period of the cell cycle by the circadian cycle.

Keywords

Temporal Logic Kripke Structure Kinetic Expression System Biology Markup Language Circadian Cycle 
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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References

  1. 1.
    Regev, A., Silverman, W., Shapiro, E.Y.: Representation and simulation of biochemical processes using the pi-calculus process algebra. In: Proceedings of the sixth Pacific Symposium of Biocomputing, pp. 459–470 (2001)Google Scholar
  2. 2.
    Cardelli, L.: Brane calculi - interactions of biological membranes. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 257–280. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Regev, A., Panina, E.M., Silverman, W., Cardelli, L., Shapiro, E.: Bioambients: An abstraction for biological compartments. Theoretical Computer Science 325, 141–167 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Danos, V., Laneve, C.: Formal molecular biology. Theoretical Computer Science 325, 69–110 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Phillips, A., Cardelli, L.: A correct abstract machine for the stochastic pi-calculus. Transactions on Computational Systems Biology Special issue of BioConcur (to appear, 2004)Google Scholar
  6. 6.
    Eker, S., Knapp, M., Laderoute, K., Lincoln, P., Meseguer, J., Sönmez, M.K.: Pathway logic: Symbolic analysis of biological signaling. In: Proceedings of the seventh Pacific Symposium on Biocomputing, pp. 400–412 (2002)Google Scholar
  7. 7.
    Chabrier, N., Fages, F.: Symbolic model cheking of biochemical networks. In: Priami, C. (ed.) CMSB 2003. LNCS, vol. 2602, pp. 149–162. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Bernot, G., Comet, J.P., Richard, A., Guespin, J.: A fruitful application of formal methods to biological regulatory networks: Extending thomas’ asynchronous logical approach with temporal logic. Journal of Theoretical Biology 229, 339–347 (2004)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Batt, G., Bergamini, D., de Jong, H., Garavel, H., Mateescu, R.: Model checking genetic regulatory networks using gna and cadp. In: Graf, S., Mounier, L. (eds.) SPIN 2004. LNCS, vol. 2989, Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Calder, M., Vyshemirsky, V., Gilbert, D., Orton, R.: Analysis of signalling pathways using the prism model checker. In: Plotkin, G. (ed.) CMSB 2005: Proceedings of the third international conference on Computational Methods in Systems Biology (2005)Google Scholar
  11. 11.
    Antoniotti, M., Policriti, A., Ugel, N., Mishra, B.: Model building and model checking for biochemical processes. Cell Biochemistry and Biophysics 38, 271–286 (2003)CrossRefGoogle Scholar
  12. 12.
    Calzone, L., Chabrier-Rivier, N., Fages, F., Soliman, S.: Machine learning biochemical networks from temporal logic properties. In: Priami, C., Plotkin, G. (eds.) Transactions on Computational Systems Biology VI. LNCS (LNBI), vol. 4220, pp. 68–94. Springer, Heidelberg (2006) (CMSB 2005 Special Issue)CrossRefGoogle Scholar
  13. 13.
    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, 64–73 (2004)CrossRefGoogle Scholar
  14. 14.
    Calzone, L., Fages, F., Soliman, S.: BIOCHAM: An environment for modeling biological systems and formalizing experimental knowledge. BioInformatics 22, 1805–1807 (2006)CrossRefGoogle Scholar
  15. 15.
    Hucka, M., et al.: The systems biology markup language (SBML): A medium for representation and exchange of biochemical network models. Bioinformatics 19, 524–531 (2003)CrossRefGoogle Scholar
  16. 16.
    Fages, F.: From syntax to semantics in systems biology - towards automated reasoning tools. Transactions on Computational Systems Biology IV 3939, 68–70 (2006)CrossRefMathSciNetGoogle Scholar
  17. 17.
    Muggleton, S.H.: Inverse entailment and progol. New Generation Computing 13, 245–286 (1995)Google Scholar
  18. 18.
    Bryant, C.H., Muggleton, S.H., Oliver, S.G., Kell, D.B., Reiser, P.G.K., King, R.D.: Combining inductive logic programming, active learning and robotics to discover the function of genes. Electronic Transactions in Artificial Intelligence, 6 (2001)Google Scholar
  19. 19.
    Angelopoulos, N., Muggleton, S.H.: Machine learning metabolic pathway descriptions using a probabilistic relational representation. Electronic Transactions in Artificial Intelligence 7 (2002) (also in Proceedings of Machine Intelligence 19)Google Scholar
  20. 20.
    Angelopoulos, N., Muggleton, S.H.: Slps for probabilistic pathways: Modeling and parameter estimation. Technical Report TR 2002/12, Department of Computing, Imperial College, London, UK (2002)Google Scholar
  21. 21.
    Bratko, I., Mozetic, I., Lavrac, N.: KARDIO: A study in Deep and Qualitative Knowledge for Expert Systems. MIT Press, Cambridge (1989)Google Scholar
  22. 22.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (1999)Google Scholar
  23. 23.
    Cimatti, A., Clarke, E., Enrico Giunchiglia, F.G., Pistore, M., Roveri, M., Sebastiani, R., Tacchella, A.: Nusmv 2: An opensource tool for symbolic model checking. In: Brinksma, E., Larsen, K.G. (eds.) CAV 2002. LNCS, vol. 2404, Springer, Heidelberg (2002)CrossRefGoogle Scholar
  24. 24.
    Chabrier-Rivier, N., Chiaverini, M., Danos, V., Fages, F., Schächter, V.: Modeling and querying biochemical interaction networks. Theoretical Computer Science 325, 25–44 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  25. 25.
    Fages, F., Soliman, S.: Type inference in systems biology. In: Priami, C. (ed.) CMSB 2006. LNCS (LNBI), vol. 4210, Springer, Heidelberg (2006)CrossRefGoogle Scholar
  26. 26.
    Gillespie, D.T.: General method for numerically simulating stochastic time evolution of coupled chemical-reactions. Journal of Computational Physics 22, 403–434 (1976)CrossRefMathSciNetGoogle Scholar
  27. 27.
    Gibson, M.A., Bruck, J.: A probabilistic model of a prokaryotic gene and its regulation. In: Bolouri, H., Bower, J. (eds.) Computational Methods in Molecular Biology: From Genotype to Phenotype, MIT Press, Cambridge (2000)Google Scholar
  28. 28.
    Calzone, L., Soliman, S.: Coupling the cell cycle and the circadian cycle. Research Report 5835, INRIA (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • François Fages
    • 1
  • Sylvain Soliman
    • 1
  1. 1.INRIA RocquencourtFrance

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