Detailed Information on Publication Record
2021
Aeon 2021: Bifurcation Decision Trees in Boolean Networks
BENEŠ, Nikola, Luboš BRIM, Samuel PASTVA and David ŠAFRÁNEKBasic information
Original name
Aeon 2021: Bifurcation Decision Trees in 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) and David ŠAFRÁNEK (203 Czech Republic, guarantor, belonging to the institution)
Edition
Cham, International Conference on Computational Methods in Systems Biology (CMSB 2021), p. 230-237, 8 pp. 2021
Publisher
Springer
Other information
Language
English
Type of outcome
Stať ve sborníku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Germany
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/00216224:14330/21:00124485
Organization unit
Faculty of Informatics
ISBN
978-3-030-85632-8
ISSN
Keywords in English
Boolean networks; attractors; software tool
Tags
Tags
International impact, Reviewed
Změněno: 6/1/2023 10:03, prof. RNDr. Luboš Brim, CSc.
Abstract
V originále
Aeon is a recent tool which enables efficient analysis of long-term behaviour of asynchronous Boolean networks with unknown parameters. In this tool paper, we present a novel major release of Aeon (Aeon 2021) which introduces substantial new features compared to the original version. These include (i) enhanced static analysis functionality that verifies integrity of the Boolean network with its regulatory graph; (ii) state-space visualisation of individual attractors; (iii) stability analysis of network variables with respect to parameters; and finally, (iv) a novel decision-tree based interactive visualisation module allowing the exploration of complex relationships between parameters and network behaviour. Aeon 2021 is open-source, fully compatible with SBML-qual models, and available as an online application with an independent native compute engine responsible for resource-intensive tasks. The paper artefact is available via https://doi.org/10.5281/zenodo.5008293.