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Machine Learning Biochemical Networks from Temporal Logic Properties

  • Conference paper
Transactions on Computational Systems Biology VI

Part of the book series: Lecture Notes in Computer Science ((TCSB,volume 4220))

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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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

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  • 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)

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