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

Logo poskytovatele

Varování

Publikace nespadá pod Fakultu sportovních studií, ale pod Fakultu informatiky. Oficiální stránka publikace je na webu muni.cz.
Autoři

BENEŠ Nikola BRIM Luboš PASTVA Samuel POLÁČEK Jakub ŠAFRÁNEK David

Rok publikování 2019
Druh Článek ve sborníku
Konference Formal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Shenzhen, China, November 5-9, 2019, Proceedings
Fakulta / Pracoviště MU

Fakulta informatiky

Citace
www 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
Klíčová slova Attractor analysis; Machine learning; Boolean networks
Popis 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.
Související projekty:

Používáte starou verzi internetového prohlížeče. Doporučujeme aktualizovat Váš prohlížeč na nejnovější verzi.

Další info