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.

Základní údaje

Originální název

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

Autoři

BENEŠ, Nikola (203 Česká republika, domácí), Luboš BRIM (203 Česká republika, domácí), Samuel PASTVA (703 Slovensko, domácí), Jakub POLÁČEK (703 Slovensko, domácí) a David ŠAFRÁNEK (203 Česká republika, garant, domácí)

Vydání

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

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10200 1.2 Computer and information sciences

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/19:00108117

Organizační jednotka

Fakulta informatiky

ISBN

978-3-030-32408-7

ISSN

DOI

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

Klíčová slova anglicky

Attractor analysis; Machine learning; Boolean networks

Štítky

core_B, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 15. 4. 2021 12:15, doc. RNDr. David Šafránek, Ph.D.

Anotace

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.

Návaznosti

GA18-00178S, projekt VaV

Název: Diskrétní bifurkační analýza reaktivních systémů

Investor: Grantová agentura ČR, Diskrétní bifurkační analýza reaktivních systémů

Citovat

BENEŠ, Nikola, Luboš BRIM, Samuel PASTVA, Jakub POLÁČEK a 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, s. 353-369. ISBN 978-3-030-32408-7. Dostupné z: 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 a 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, s.~353-369. ISBN~978-3-030-32408-7. Dostupné z: https://dx.doi.org/10.1007/978-3-030-32409-4\_{}22.

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