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Expressive Statistical Model Checking of Genetic Networks with Delayed Stochastic Dynamics

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Computational Methods in Systems Biology (CMSB 2012)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 7605))

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Abstract

The recently introduced Hybrid Automata Stochastic Logic (HASL) establishes a powerful framework for the analysis of a broad class of stochastic processes, namely Discrete Event Stochastic Processes (DESPs). Here we demonstrate the potential of HASL based verification in the context of genetic circuits. To this aim we consider the analysis of a model of gene expression with delayed stochastic dynamics, a class of systems whose dynamics includes both Markovian and non-Markovian events. We identify a number of relevant properties related to this model, formally express them in HASL terms and, assess them with COSMOS, a statistical model checker for HASL model checking. We demonstrate that this allows assessing the “performances” of a biological system beyond the capability of other stochastic logics.

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Author information

Authors and Affiliations

  1. Ecole Centrale Paris, France

    Paolo Ballarini

  2. Tampere University of Technology, Finland

    Jarno Mäkelä & Andre S. Ribeiro

Authors
  1. Paolo Ballarini
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  2. Jarno Mäkelä
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  3. Andre S. Ribeiro
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Editor information

Editors and Affiliations

  1. School of Information Systems, Computing and Mathematics, Brunel University, UB8 3PH, Uxbridge, UK

    David Gilbert

  2. Computer Science Institute, University of Technology, 03013, Cottbus, Germany

    Monika Heiner

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Ballarini, P., Mäkelä, J., Ribeiro, A.S. (2012). Expressive Statistical Model Checking of Genetic Networks with Delayed Stochastic Dynamics. In: Gilbert, D., Heiner, M. (eds) Computational Methods in Systems Biology. CMSB 2012. Lecture Notes in Computer Science(), vol 7605. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33636-2_4

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  • DOI: https://doi.org/10.1007/978-3-642-33636-2_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33635-5

  • Online ISBN: 978-3-642-33636-2

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