Detailed Information on Publication Record
2017
Detecting Attractors in Biological Models with Uncertain Parameters
BRIM, Luboš, Jiří BARNAT, David ŠAFRÁNEK, Nikola BENEŠ, Martin DEMKO et. al.Basic information
Original name
Detecting Attractors in Biological Models with Uncertain Parameters
Authors
BRIM, Luboš (203 Czech Republic, guarantor, belonging to the institution), Jiří BARNAT (203 Czech Republic, belonging to the institution), David ŠAFRÁNEK (203 Czech Republic, belonging to the institution), Nikola BENEŠ (203 Czech Republic, belonging to the institution), Martin DEMKO (703 Slovakia, belonging to the institution), Samuel PASTVA (703 Slovakia, belonging to the institution) and Matej HAJNAL (703 Slovakia, belonging to the institution)
Edition
LNCS 10545. Cham, Computational Methods in Systems Biology. CMSB 2017, p. 40-56, 17 pp. 2017
Publisher
Springer International Publishing
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Czech Republic
Confidentiality degree
není předmětem státního či obchodního tajemství
Publication form
printed version "print"
Impact factor
Impact factor: 0.402 in 2005
RIV identification code
RIV/00216224:14330/17:00094899
Organization unit
Faculty of Informatics
ISBN
978-3-319-67470-4
ISSN
UT WoS
000542715600003
Keywords in English
model checking; systems biology; Computational Tree Logic; dynamical systems; distributed algorithms;
Tags
Tags
International impact, Reviewed
Změněno: 13/5/2021 13:12, doc. RNDr. David Šafránek, Ph.D.
Abstract
V originále
Complex behaviour arising in biological systems is typically characterised by various kinds of attractors. An important problem in this area is to determine these attractors. Biological systems are usually described by highly parametrised dynamical models that can be represented as parametrised graphs typically constructed as discrete abstractions of continuous-time models. In such models, attractors are observed in the form of terminal strongly connected components (tSCCs). In this paper, we introduce a novel method for detecting tSCCs in parametrised graphs. The method is supplied with a parallel algorithm and evaluated on discrete abstractions of several non-linear biological models.
Links
GA15-11089S, research and development project |
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LM2015055, research and development project |
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MUNI/A/0992/2016, interní kód MU |
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