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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
Regev, A., Panina, E.M., Silverman, W., Cardelli, L., Shapiro, E.: Bioambients: An abstraction for biological compartments. Theoretical Computer Science 325, 141–167 (2004)
Danos, V., Laneve, C.: Formal molecular biology. Theoretical Computer Science 325, 69–110 (2004)
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)
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)
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)
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)
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)
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)
Antoniotti, M., Policriti, A., Ugel, N., Mishra, B.: Model building and model checking for biochemical processes. Cell Biochemistry and Biophysics 38, 271–286 (2003)
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)
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)
Chabrier, N., Fages, F., Soliman, S.: BIOCHAM’s user manual. INRIA (2003–2006)
Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (1999)
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)
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 Session
Muggleton, S.H.: Inverse entailment and progol. New Generation Computing 13, 245–286 (1995)
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)
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
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)
Qu, Z., MacLellan, W.R., Weiss, J.N.: Dynamics of the cell cycle: checkpoints, sizers, and timers. Biophysics Journal 85, 3600–3611 (2003)
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)
Gillespie, D.T.: General method for numerically simulating stochastic time evolution of coupled chemical-reactions. Journal of Computational Physics 22, 403–434 (1976)
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)
Chabrier-Rivier, N., Chiaverini, M., Danos, V., Fages, F., Schächter, V.: Modeling and querying biochemical interaction networks. Theoretical Computer Science 325, 25–44 (2004)
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)
Hansson, H., Jonsson, B.: A logic for reasoning about time and reliability. Formal Aspects of Computing 6, 512–535 (1994)
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)
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)
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)
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)
Berman, C.L.: Circuit width, register allocation, and reduced function graphs. Research Report RC 14127, IBM (1988)
Murata, T.: Petri nets: properties, analysis and applications. Proceedings of the IEEE 77, 541–579 (1989)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Calzone, L., Chabrier-Rivier, N., Fages, F., Soliman, S. (2006). Machine Learning Biochemical Networks from Temporal Logic Properties. In: Priami, C., Plotkin, G. (eds) Transactions on Computational Systems Biology VI. Lecture Notes in Computer Science(), vol 4220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11880646_4
Download citation
DOI: https://doi.org/10.1007/11880646_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45779-4
Online ISBN: 978-3-540-46236-1
eBook Packages: Computer ScienceComputer Science (R0)