| BENEŠ, Nikola, Luboš BRIM, Samuel PASTVA, Jakub POLÁČEK and David ŠAFRÁNEK. Formal Analysis of Qualitative Long-Term Behaviour in Parametrised Boolean Networks. In Ait Ameur et al. Formal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Shenzhen, China, November 5-9, 2019, Proceedings. Heidelberg: Springer, 2019, p. 353-369. ISBN 978-3-030-32408-7. Available from: https://dx.doi.org/10.1007/978-3-030-32409-4_22. |
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@inproceedings{1610819,
author = {Beneš, Nikola and Brim, Luboš and Pastva, Samuel and Poláček, Jakub and Šafránek, David},
address = {Heidelberg},
booktitle = {Formal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Shenzhen, China, November 5-9, 2019, Proceedings},
doi = {http://dx.doi.org/10.1007/978-3-030-32409-4_22},
editor = {Ait Ameur et al.},
keywords = {Attractor analysis; Machine learning; Boolean networks},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Heidelberg},
isbn = {978-3-030-32408-7},
pages = {353-369},
publisher = {Springer},
title = {Formal Analysis of Qualitative Long-Term Behaviour in Parametrised Boolean Networks.},
url = {http://dx.doi.org/10.1007/978-3-030-32409-4_22},
year = {2019}
}
TY - JOUR
ID - 1610819
AU - Beneš, Nikola - Brim, Luboš - Pastva, Samuel - Poláček, Jakub - Šafránek, David
PY - 2019
TI - Formal Analysis of Qualitative Long-Term Behaviour in Parametrised Boolean Networks.
PB - Springer
CY - Heidelberg
SN - 9783030324087
KW - Attractor analysis
KW - Machine learning
KW - Boolean networks
UR - http://dx.doi.org/10.1007/978-3-030-32409-4_22
N2 - 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.
ER -
BENEŠ, Nikola, Luboš BRIM, Samuel PASTVA, Jakub POLÁČEK and David ŠAFRÁNEK. Formal Analysis of Qualitative Long-Term Behaviour in Parametrised Boolean Networks. In Ait Ameur et al. \textit{Formal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Shenzhen, China, November 5-9, 2019, Proceedings}. Heidelberg: Springer, 2019, p.~353-369. ISBN~978-3-030-32408-7. Available from: https://dx.doi.org/10.1007/978-3-030-32409-4\_{}22.
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