Model Revision from Temporal Logic Properties in Computational Systems Biology

Abstract : Systems biologists build models of bio-molecular processes from knowledge acquired both at the gene and protein levels, and at the phenotype level through experiments done in wildlife and mutated organisms. In this chapter, we present qualitative and quantitative logic learning tools, and illustrate how they can be useful to the modeler. We focus on biochemical reaction models written in the Systems Biology Markup Language SBML, and interpreted in the Biochemical Abstract Machine BIOCHAM. We first present a model revision algorithm for inferring reaction rules from biological properties expressed in temporal logic. Then we discuss the representations of kinetic models with ordinary differential equations (ODEs) and with stochastic logic programs (SLPs), and describe a parameter search algorithm for finding parameter values satisfying quantitative temporal properties. These methods are illustrated by a simple model of the cell cycle control, and by an application to the modelling of the conditions of synchronization in period of the cell cycle by the circadian cycle.
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Francois Fages, Sylvain Soliman. Model Revision from Temporal Logic Properties in Computational Systems Biology. Luc de Raedt and Paolo Frasconi and Kristian Kersting and Stephen Muggleton. Probabilistic Inductive Logic Programming, pp.287--304, 2008, ⟨10.1007/978-3-540-78652-8_11⟩. ⟨hal-01431378⟩

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