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Combining Refinement of Parametric Models with Goal-Oriented Reduction of Dynamics

  • Stefan Haar
  • Juraj Kolčák
  • Loïc Paulevé
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11388)

Abstract

Parametric models abstract part of the specification of dynamical models by integral parameters. They are for example used in computational systems biology, notably with parametric regulatory networks, which specify the global architecture (interactions) of the networks, while parameterising the precise rules for drawing the possible temporal evolutions of the states of the components. A key challenge is then to identify the discrete parameters corresponding to concrete models with desired dynamical properties. This paper addresses the restriction of the abstract execution of parametric regulatory (discrete) networks by the means of static analysis of reachability properties (goal states). Initially defined at the level of concrete parameterised models, the goal-oriented reduction of dynamics is lifted to parametric networks, and is proven to preserve all the minimal traces to the specified goal states. It results that one can jointly perform the refinement of parametric networks (restriction of domain of parameters) while reducing the necessary transitions to explore and preserving reachability properties of interest.

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.LSV, CNRS & ENS Paris-Saclay, Université Paris-SaclayCachanFrance
  2. 2.National Institute of InformaticsTokyoJapan
  3. 3.LRI UMR 8623, Univ. Paris-Sud – CNRS, Université Paris-SaclayOrsayFrance
  4. 4.Univ. Bordeaux, Bordeaux INP, CNRS, LaBRI, UMR5800TalenceFrance

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