Ontology type: schema:Chapter
2019-10-28
AUTHORSNikola Beneš , Luboš Brim , Samuel Pastva , Jakub Poláček , David Šafránek
ABSTRACTBoolean 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. More... »
PAGES353-369
Formal Methods and Software Engineering
ISBN
978-3-030-32408-7
978-3-030-32409-4
http://scigraph.springernature.com/pub.10.1007/978-3-030-32409-4_22
DOIhttp://dx.doi.org/10.1007/978-3-030-32409-4_22
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