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Machine Learning Biochemical Networks from Temporal Logic Properties

  • Laurence Calzone
  • Nathalie Chabrier-Rivier
  • François Fages
  • Sylvain Soliman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4220)

Abstract

One central issue in systems biology is the definition of formal languages for describing complex biochemical systems and their behavior at different levels. The biochemical abstract machine BIOCHAM is based on two formal languages, one rule-based language used for modeling biochemical networks, at three abstraction levels corresponding to three semantics: boolean, concentration and population; and one temporal logic language used for formalizing the biological properties of the system. In this paper, we show how the temporal logic language can be turned into a specification language. We describe two algorithms for inferring reaction rules and kinetic parameter values from a temporal specification formalizing the biological data. Then, with an example of the cell cycle control, we illustrate how these machine learning techniques may be useful to the modeler.

Keywords

Model Check Temporal Logic Cell Cycle Control Inductive Logic Programming Kripke Structure 
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. 257–278. Springer, Heidelberg (2005)CrossRefGoogle 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)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Danos, V., Laneve, C.: Formal molecular biology. Theoretical Computer Science 325, 69–110 (2004)MATHCrossRefMathSciNetGoogle 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 2004 (to appear)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 checking 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, pp. 158–163. Springer, Heidelberg (2004)CrossRefGoogle 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 Workshop 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.: 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, Santa Barbara, pp. 375–379 (2005)Google 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.
    Chabrier, N., Fages, F., Soliman, S.: BIOCHAM’s user manual. INRIA (2003–2006)Google Scholar
  15. 15.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (1999)Google Scholar
  16. 16.
    Nagasaki, M., Onami, S., Miyano, S., Kitano, H.: Bio-calculus: Its concept and molecular interaction. In: Proceedings of the Workshop on Genome Informatics, vol. 10, pp. 133–143 (1999)Google Scholar
  17. 17.
    Nagasaki, M., Onami, S., Miyano, S., Kitano, H.: Bio-calculus: Its concept, and an application for molecular interaction. In: Currents in Computational Molecular Biology. Frontiers Science Series, vol. 30. Universal Academy Press (2000); This book is a collection of poster papers presented at the RECOMB 2000 Poster SessionGoogle Scholar
  18. 18.
    Muggleton, S.H.: Inverse entailment and progol. New Generation Computing 13, 245–286 (1995)CrossRefGoogle Scholar
  19. 19.
    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
  20. 20.
    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 19Google Scholar
  21. 21.
    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
  22. 22.
    Qu, Z., MacLellan, W.R., Weiss, J.N.: Dynamics of the cell cycle: checkpoints, sizers, and timers. Biophysics Journal 85, 3600–3611 (2003)CrossRefGoogle Scholar
  23. 23.
    Chabrier-Rivier, N., Fages, F., Soliman, S.: The biochemical abstract machine BIOCHAM. In: Danos, V., Schachter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 172–191. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    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
  25. 25.
    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
  26. 26.
    Chabrier-Rivier, N., Chiaverini, M., Danos, V., Fages, F., Schächter, V.: Modeling and querying biochemical interaction networks. Theoretical Computer Science 325, 25–44 (2004)MATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Batt, G.: Validation de modèles qualitatifs de réseaux de régulation génique: une méthode basée sur des techniques de vérication formelle. PhD thesis, Université Joseph Fourier - Grenoble I (2006)Google Scholar
  28. 28.
    Hansson, H., Jonsson, B.: A logic for reasoning about time and reliability. Formal Aspects of Computing 6, 512–535 (1994)MATHCrossRefGoogle Scholar
  29. 29.
    Cimatti, A., Clarke, E., Giunchiglia, E., Giunchiglia, F., 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, p. 359. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  30. 30.
    Kohn, K.W.: Molecular interaction map of the mammalian cell cycle control and DNA repair systems. Molecular Biology of the Cell 10, 2703–2734 (1999)Google Scholar
  31. 31.
    Schoeberl, B., Eichler-Jonsson, C., Gilles, E., Muller, G.: Computational modeling of the dynamics of the map kinase cascade activated by surface and internalized egf receptors. Nature Biotechnology 20, 370–375 (2002)CrossRefGoogle Scholar
  32. 32.
    Wang, D., Clarke, E.M., Zhu, Y., Kukula, J.: Using cutwidth to improve symbolic simulation and boolean satisfiability. In: IEEE International High Level Design Validation and Test Workshop 2001 (HLDVT 2001), vol. 6 (2001)Google Scholar
  33. 33.
    Berman, C.L.: Circuit width, register allocation, and reduced function graphs. Research Report RC 14127, IBM (1988)Google Scholar
  34. 34.
    Murata, T.: Petri nets: properties, analysis and applications. Proceedings of the IEEE 77, 541–579 (1989)CrossRefGoogle Scholar
  35. 35.
    Kwiatkowska, M.Z., Norman, G., Parker, D.: Prism 2.0: A tool for probabilistic model checking. In: International Conference on Quantitative Evaluation of Systems (QEST 2004), pp. 322–323. IEEE Computer Society, Los Alamitos (2004)CrossRefGoogle Scholar
  36. 36.
    Hérault, T., Lassaigne, R., Magniette, F., Peyronnet, S.: Approximate probabilistic model checking. In: Steffen, B., Levi, G. (eds.) VMCAI 2004. LNCS, vol. 2937, pp. 73–84. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  37. 37.
    Gibson, M.A., Bruck, J.: Efficient exact stochastic simulation of chemical systems with many species and many channels. Journal of Physical Chemistry 104, 1876–1889 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Laurence Calzone
    • 1
  • Nathalie Chabrier-Rivier
    • 1
  • François Fages
    • 1
  • Sylvain Soliman
    • 1
  1. 1.INRIA RocquencourtProjet ContraintesLe ChesnayFrance

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