Publication details

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

Authors

BENEŠ Nikola BRIM Luboš PASTVA Samuel POLÁČEK Jakub ŠAFRÁNEK David

Year of publication 2019
Type Article in Proceedings
Conference Formal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Shenzhen, China, November 5-9, 2019, Proceedings
MU Faculty or unit

Faculty of Informatics

Citation
Web http://dx.doi.org/10.1007/978-3-030-32409-4_22
Doi http://dx.doi.org/10.1007/978-3-030-32409-4_22
Keywords Attractor analysis; Machine learning; Boolean networks
Description 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.
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