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Formal Analysis of Qualitative Long-Term Behaviour in Parametrised Boolean Networks

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Formal Methods and Software Engineering (ICFEM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11852))

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Abstract

Boolean networks offer an elegant way to model the behaviour of complex systems with positive and negative feedback. The long-term behaviour of a Boolean network is characterised by its attractors. Depending on various logical parameters, a Boolean network can exhibit vastly different types of behaviour. Hence, the structure and quality of attractors can undergo a significant change known in systems theory as attractor bifurcation. In this paper, we establish formally the notion of attractor bifurcation for Boolean networks. We propose a semi-symbolic approach to attractor bifurcation analysis based on a parallel algorithm. We use machine-learning techniques to construct a compact, human-readable, representation of the bifurcation analysis results. We demonstrate the method on a set of highly parametrised Boolean networks.

D. Šafránek—This work has been supported by the Czech Science Foundation grant No. 18-00178S.

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Authors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Nikola Beneš, Luboš Brim, Samuel Pastva, Jakub Poláček & David Šafránek

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  1. Nikola Beneš
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  2. Luboš Brim
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  3. Samuel Pastva
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  4. Jakub Poláček
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  5. David Šafránek
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Correspondence to David Šafránek .

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Editors and Affiliations

  1. IRIT/INPT - ENSEEIHT, Toulouse, France

    Yamine Ait-Ameur

  2. Teesside University, Middlesbrough, UK

    Shengchao Qin

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Beneš, N., Brim, L., Pastva, S., Poláček, J., Šafránek, D. (2019). Formal Analysis of Qualitative Long-Term Behaviour in Parametrised Boolean Networks. In: Ait-Ameur, Y., Qin, S. (eds) Formal Methods and Software Engineering. ICFEM 2019. Lecture Notes in Computer Science(), vol 11852. Springer, Cham. https://doi.org/10.1007/978-3-030-32409-4_22

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  • DOI: https://doi.org/10.1007/978-3-030-32409-4_22

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  • Online ISBN: 978-3-030-32409-4

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