Machine Learning Biochemical Networks from Temporal Logic Properties

Resource type
Authors/contributors
Title
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.
Date
2006
Proceedings Title
Transactions on Computational Systems Biology VI
Place
Berlin, Heidelberg
Publisher
Springer
Pages
68-94
Series
Lecture Notes in Computer Science
Language
en
DOI
10/dd8
ISBN
978-3-540-46236-1
Library Catalog
Springer Link
Extra
ZSCC: NoCitationData[s0]
Citation
Fages, F., Calzone, L., Chabrier-Rivier, N., & Soliman, S. (2006). Machine Learning Biochemical Networks from Temporal Logic Properties. In C. Priami & G. Plotkin (Eds.), Transactions on Computational Systems Biology VI (pp. 68–94). Berlin, Heidelberg: Springer. https://doi.org/10/dd8
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