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

D 2019

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

BENEŠ, Nikola, Luboš BRIM, Samuel PASTVA, Jakub POLÁČEK, David ŠAFRÁNEK et. al.

Basic information

Original name

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

Authors

BENEŠ, Nikola (203 Czech Republic, belonging to the institution), Luboš BRIM (203 Czech Republic, belonging to the institution), Samuel PASTVA (703 Slovakia, belonging to the institution), Jakub POLÁČEK (703 Slovakia, belonging to the institution) and David ŠAFRÁNEK (203 Czech Republic, guarantor, belonging to the institution)

Edition

Heidelberg, Formal Methods and Software Engineering - 21st International Conference on Formal Engineering Methods, ICFEM 2019, Shenzhen, China, November 5-9, 2019, Proceedings, p. 353-369, 17 pp. 2019

Publisher

Springer

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

References:

URL

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/19:00108117

Organization unit

Faculty of Informatics

ISBN

978-3-030-32408-7

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-32409-4_22

Keywords in English

Attractor analysis; Machine learning; Boolean networks

Abstract

V originále

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.

Links

GA18-00178S, research and development project

Name: Diskrétní bifurkační analýza reaktivních systémů

Investor: Czech Science Foundation

Citovat

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.

@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.

Detailed Information on Publication Record