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Using Hybrid Concurrent Constraint Programming to Model Dynamic Biological Systems

  • Alexander Bockmayr
  • Arnaud Courtois
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2401)

Abstract

Systems biology is a new area in biology that aims at achieving a systems-level understanding of biological systems. While current genome projects provide a huge amount of data on genes or proteins, lots of research is still necessary to understand how the different parts of a biological system interact in order to perform complex biological functions. Computational models that help to analyze, explain or predict the behavior of biological systems play a crucial role in systems biology. The goal of this paper is to show that hybrid concurrent constraint programming [11] may be a promising alternative to existing modeling approaches in systems biology. Hybrid cc is a declarative compositional programming language with a well-defined semantics. It allows one to model and simulate the dynamics of hybrid systems, which exhibit both discrete and continuous change. We show that Hybrid cc can be used naturally to model a variety of biological phenomena, such as reaching thresholds, kinetics, gene interaction or biological pathways.

Keywords

System Biology Hybrid System Constraint Programming Boolean Network Stochastic Noise 
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.
    R. Alur, C. Belta, F. Ivancic, V. Kumar, M. Mintz, G. J. Pappas, H. Rubin, and J. Schug. Hybrid modeling and simulation of biomolecular networks. In Hybrid Systems: Computation and Control, HSCC 2001, pages 19–32. Springer, LNCS 2034, 2001.CrossRefGoogle Scholar
  2. 2.
    A. Bockmayr and A. Courtois. Modeling biological systems in hybrid concurrent constraint programming (Abstract). In 2nd Int. Conf. Systems Biology, ICSB’01, Pasadena, CA, 2001.Google Scholar
  3. 3.
    J. M. Bower and H. Bolouri, editors. Computational modeling of genetic and biochemical networks. MIT Press, 2001.Google Scholar
  4. 4.
    B. Carlson and V. Gupta. Hybrid cc and interval constraints. In Hybrid Systems: Computation and Control, HSCC’98, pages 80–95. Springer, LNCS 1386, 1998.Google Scholar
  5. 5.
    A. Courtois. Modélisation de systèmes biologiques en programmation par contraintes. Rapport de DEA (in French), Univ. Henri Poincaré, LORIA, July 2001.Google Scholar
  6. 6.
    H. de Jong. Modeling and simulation of genetic regulatory systems: a literature review. Journal of Computational Biology, 9(1):69–105, 2001.Google Scholar
  7. 7.
    R. Ghosh and C. Tomlin. Lateral inhibition through Delta-Notch signaling: A piecewise affine hybrid model. In Hybrid Systems: Computation and Control, HSCC 2001, pages 232–246. Springer, LNCS 2034, 2001.CrossRefGoogle Scholar
  8. 8.
    M. A. Gibson and J. Bruck. A probabilistic model of a prokaryotic gene and its regulation. In H. Bolouri, editors. Computational modeling of genetic and biochemical networks. MIT Press, 2001 Bower and Bolouri [3]}, chapter 2, pages 49–71.Google Scholar
  9. 9.
    M. A. Gibson and E. Mjolsness. Modeling the activity of single genes. In H. Bolouri, editors. Computational modeling of genetic and biochemical networks. MIT Press, 2001 Bower and Bolouri [3]}, chapter 1, pages 1–48.Google Scholar
  10. 10.
    V. Gupta, R. Jagadeesan, and P. Panangaden. Stochastic processes as concurrent constraint programs. In 26th ACM Conf. Principles of Programming Languages, POPL’99, San Antonio, CA, pages 189–202. ACM, 1999.Google Scholar
  11. 11.
    V. Gupta, R. Jagadeesan, and V. Saraswat. Computing with continuous change. Science of computer programming, 30(1–2):3–49, 1998.zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    V. Gupta, R. Jagadeesan, V. Saraswat, and D. G. Bobrow. Programming in hybrid constraint languages. In Hybrid Systems II, pages 226–251. Springer, LNCS 999, 1995.Google Scholar
  13. 13.
    M. Hucka, A. Finney, H. M. Sauro, H. Bolouri, J. Doyle, and H. Kitano. The ERATO systems biology workbench: Enabling interaction and exchange between software tools for computational biology. Pacific Symposium on Biocomputing, 7, 2002.Google Scholar
  14. 14.
    T. Ideker, V. Thorsson, J. A. Ranish, R. Christmas, J. Buhler, J. K. Eng, R. Bumgarner, D. R. Goodlett, R. Aebersold, and L. Hood. Integrated genomic and pro-teomic analyses of a systematically perturbed metabolic network. Science, 292:929–934, May 2001.Google Scholar
  15. 15.
    S. A. Kauffman. The origins of order. Oxford Univ. Press, 1993. See also J. Theor. Biol. 22: 437, 1969.Google Scholar
  16. 16.
    H. Kitano, editor. Foundations of system biology. MIT Press, 2001.Google Scholar
  17. 17.
    H. Matsuno, A. Doi, M. Nagasaki, and S. Miyano. Hybrid petri net representation of gene regulatory network. Pacific Symposium on Biocomputing, 5:338–349, 2000.Google Scholar
  18. 18.
    V. N. Reddy, M. L. Mavrovouniotis, and M. N. Liebman. Petri net representation in metabolic pathways. In Intelligent Systems for Molecular Biology, ISMB’93, pages 328–336. AAAI Press, 1993.Google Scholar
  19. 19.
    A. Regev, W. Silverman, and E. Shapiro. Representation and simulation of biochemical processes using the π-calculus process algebra. Pacific Symposium on Biocomputing, 6:459–470, 2001.Google Scholar
  20. 20.
    V. A. Saraswat. Concurrent constraint programming. ACM Doctoral Dissertation Awards. MIT Press, 1993.Google Scholar
  21. 21.
    V. A. Saraswat, R. Jagadeesan, and V. Gupta. Foundations of timed concurrent constraint programming. In 9th Symp. Logic in Computer Science, LICS’94, Paris, pages 71–80. IEEE, 1994.Google Scholar
  22. 22.
    V. A. Saraswat, R. Jagadeesan, and V. Gupta. Timed default concurrent constraint programming. Journal of Symbolic Computation, 22(5/6):475–520, 1996.zbMATHCrossRefMathSciNetGoogle Scholar
  23. 23.
    D. Thieffry and R. Thomas. Qualitative analysis of gene networks. Pacific Symposium on Biocomputing, 3:77–88, 1998.Google Scholar
  24. 24.
    A. van der Schaft and H. Schumacher. An introduction to hybrid dynamical systems. Springer, Lecture Notes in Control and Information Sciences, Vol. 251, 2000.Google Scholar
  25. 25.
    E. O. Voit. Computational analysis of biochemical systems. Cambridge Univ. Press, 2000.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Alexander Bockmayr
    • 1
  • Arnaud Courtois
    • 1
  1. 1.LORIAUniversité Henri PoincaréVandœuvre-lès-NancyFrance

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