Machine Learning Biochemical Networks from Temporal Logic Properties

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.
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Contributor : Sylvain Soliman <>
Submitted on : Thursday, April 26, 2018 - 4:24:38 PM
Last modification on : Monday, September 24, 2018 - 2:00:04 PM


  • HAL Id : hal-01779529, version 1



Laurence Calzone, Nathalie Chabrier-Rivier, François Fages, Sylvain Soliman. Machine Learning Biochemical Networks from Temporal Logic Properties. Transactions on Computational Systems Biology VI, 2006, Berlin, Heidelberg, Unknown Region. pp.68--94. ⟨hal-01779529⟩



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