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

  • Nikola Beneš
  • Luboš Brim
  • Samuel Pastva
  • Jakub Poláček
  • David ŠafránekEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11852)

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.

Keywords

Attractor analysis Machine learning Boolean networks 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Nikola Beneš
    • 1
  • Luboš Brim
    • 1
  • Samuel Pastva
    • 1
  • Jakub Poláček
    • 1
  • David Šafránek
    • 1
    Email author
  1. 1.Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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