Temporal Logic Constraints in the Biochemical Abstract Machine BIOCHAM
Recent progress in Biology and data-production technologies push research toward a new interdisciplinary field, named Systems Biology, where the challenge is to break the complexity walls for reasoning about large biomolecular interaction systems. Pioneered by Regev, Silverman and Shapiro, the application of process calculi to the description of biological processes has been a source of inspiration for many researchers coming from the programming language community.
In this presentation, we give an overview of the Biochemical Abstract Machine (BIOCHAM), in which biochemical systems are modeled using a simple language of reaction rules, and the biological properties of the system, known from experiments, are formalized in temporal logic. In this setting, the biological validation of a model can be done by model-checking, both qualitatively and quantitatively. Moreover, the temporal properties can be turned into specifications for learning modifications or refinements of the model, when incorporating new biological knowledge.
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- 2.Chabrier, N., Fages, F., Soliman, S.: BIOCHAM’s user manual. INRIA (2003-2005)Google Scholar
- 3.Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (1999)Google Scholar
- 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
- 8.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
- 10.Calzone, L., Chabrier-Rivier, N., Fages, F., Soliman, S.: A machine learning approach to biochemical reaction rules discovery. In: III, F.J.D. (ed.) Proceedings of Foundations of Systems Biology and Engineering FOSBE 2005, Santa Barbara, pp. 375–379 (2005)Google Scholar
- 11.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