From Syntax to Semantics in Systems Biology Towards Automated Reasoning Tools

  • François Fages
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3939)

Abstract

Mathematical biology has for a long time investigated the dynamics of biomolecular systems by developing numerical models involving (highly non-linear) differential equations and using tools such as Bifurcation Theory for estimating parameters [1]. Mathematical biology provides a firm ground for the numerical analysis of biological systems. However, state-of-the-art quantitative models can hardly be re-used and composed with other models in a systematic fashion, and are limited to a few tenths of variables [2].

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References

  1. 1.
    Segel, L.A.: Modeling dynamic phenomena in molecular and cellular biology. Cambridge University Press, Cambridge (1984)MATHGoogle Scholar
  2. 2.
    Chen, K.C., Calzone, L., Csikász-Nagy, A., Cross, F.R., Györffy, B., Val, J., Novák, B., Tyson, J.J.: Integrative analysis of cell cycle control in budding yeast. Molecular Biology of the Cell 15, 3841–3862 (2005)CrossRefGoogle Scholar
  3. 3.
    Kanehisa, M., Goto, S.: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research 28, 27–30 (2000)CrossRefGoogle Scholar
  4. 4.
    Keseler, I.M., Collado-Vides, J., Gama-Castro, S., Ingraham, J., Paley, S., Paulsen, I.T., Peralta-Gil, M., Karp, P.D.: EcoCyc: a comprehensive database resource for escherichia coli. Nucleic Acids Research 33, 334–337 (2005)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    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)CrossRefMATHGoogle Scholar
  7. 7.
    Chabrier-Rivier, N., Fages, F., Soliman, S.: The biochemical abstract machine BIOCHAM. In: Danos, V., Schächter, V. (eds.) CMSB 2004. LNCS (LNBI), vol. 3082, pp. 172–191. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    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
  9. 9.
    Calzone, L., Chabrier-Rivier, N., Fages, F., Gentils, L., Soliman, S.: Machine learning bio-molecular interactions from temporal logic properties. In: Plotkin, G. (ed.) CMSB 2005: Proceedings of the third Workshop on Computational Methods in Systems Biology (2005)Google Scholar
  10. 10.
    Gillespie, D.T.: General method for numerically simulating stochastic time evolution of coupled chemical-reactions. Journal of Computational Physics 22, 403–434 (1976)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (1999)Google Scholar
  12. 12.
    Chabrier-Rivier, N., Chiaverini, M., Danos, V., Fages, F., Schächter, V.: Modeling and querying biochemical interaction networks. Modeling and querying biochemical interaction networks 325, 25–44 (2004)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • François Fages
    • 1
  1. 1.Projet Contraintes, INRIA RocquencourtLe ChesnayFrance

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