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Context-sensitive flow analyses: a hierarchy of model reductions

Ferdinanda Camporesi 1, 2 Jérôme Feret 1 Jonathan Hayman 1, 3
1 ABSTRACTION - Abstract Interpretation and Static Analysis
DI-ENS - Département d'informatique de l'École normale supérieure, Inria Paris-Rocquencourt, CNRS - Centre National de la Recherche Scientifique : UMR 8548
Abstract : Rule-based modelling allows very compact descriptions of protein-protein interaction networks. However, combinatorial complexity increases again when one attempts to describe formally the behaviour of the networks, which motivates the use of abstractions to make these models more coarse-grained. Context-insensitive abstractions of the intrinsic flow of information among the sites of chemical complexes through the rules have been proposed to infer sound coarse-graining, providing an efficient way to find macro-variables and the corresponding reduced models. In this paper, we propose a framework to allow the tuning of the context-sensitivity of the information flow analyses and show how these finer analyses can be used to find fewer macro-variables and smaller reduced differential models.
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https://hal.inria.fr/hal-00846893
Contributor : Jérôme Feret <>
Submitted on : Monday, July 22, 2013 - 10:36:20 AM
Last modification on : Thursday, July 1, 2021 - 5:58:03 PM

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Ferdinanda Camporesi, Jérôme Feret, Jonathan Hayman. Context-sensitive flow analyses: a hierarchy of model reductions. CMSB - 11th Conference on Computational Methods in Systems Biology - 2013, Ashutosh Gupta and Thomas A. Henzinger, Sep 2013, Klosterneuburg, Austria. pp.220-233, ⟨10.1007/978-3-642-40708-6_17⟩. ⟨hal-00846893⟩

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