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)


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.


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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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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