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High-Performance Symbolic Parameter Synthesis of Biological Models: A Case Study

  • Martin Demko
  • Nikola Beneš
  • Luboš Brim
  • Samuel Pastva
  • David ŠafránekEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9859)

Abstract

Complex behaviour arising in biological systems is described by highly parameterised dynamical models. Most of the parameters are mutually dependent and therefore it is hard and computationally demanding to find admissible parameter values with respect to hypothesised constraints and wet-lab measurements. Recently, we have developed several high-performance techniques for parameter synthesis that are based on parallel coloured model checking. These methods allow to obtain parameter values that guarantee satisfaction of a given set of dynamical properties and parameter constraints. In this paper, we review the applicability of our techniques in the context of biological systems. In particular, we provide an extended analysis of a genetic switch controlling the regulation in mammalian cell cycle phase transition and a synthetic pathway for biodegradation of a toxic pollutant in E. coli.

Keywords

Model Check Atomic Proposition Parameter Synthesis Statistical Model Check Computational Tree Logic 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Martin Demko
    • 1
  • Nikola Beneš
    • 1
  • Luboš Brim
    • 1
  • Samuel Pastva
    • 1
  • David Šafránek
    • 1
    Email author
  1. 1.Systems Biology Laboratory, Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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