Taming Asynchrony for Attractor Detection in Large Boolean Networks

Andrzej Mizera; Jun Pang; Hongyang Qu; Qixia Yuan

doi:10.1109/TCBB.2018.2850901

Taming Asynchrony for Attractor Detection in Large Boolean Networks

Andrzej Mizera
, Jun Pang
, Hongyang Qu
, Qixia Yuan^*

^*Corresponding author for this work

Nutrition, Microbiome and Immunity

Research output: Contribution to journal › Conference article › peer-review

35 Citations (Scopus)

Abstract

Boolean networks is a well-established formalism for modelling biological systems. A vital challenge for analyzing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life biological networks.

Original language	English
Article number	8398459
Pages (from-to)	31-42
Number of pages	12
Journal	IEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume	16
Issue number	1
DOIs	https://doi.org/10.1109/TCBB.2018.2850901
Publication status	Published - 1 Jan 2019

Keywords

Asynchronous Boolean networks
attractor detection
binary decision diagram
gene regulatory networks
strongly connected components

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10.1109/TCBB.2018.2850901

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abstract = "Boolean networks is a well-established formalism for modelling biological systems. A vital challenge for analyzing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life biological networks.",

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author = "Andrzej Mizera and Jun Pang and Hongyang Qu and Qixia Yuan",

note = "Funding Information: Qixia Yuan is supported by the National Research Fund, Luxembourg (grant 7814267). This work is also partially supported by the research project SEC-PBN (funded by the University of Luxembourg) and the ANR-FNR project AlgoReCell (INTER/ ANR/15/11191283). Andrzej Mizera contributed to this work while holding a postdoctoral researcher position at the Computer Science and Communications Research Unit, University of Luxembourg. Publisher Copyright: {\textcopyright} 2004-2012 IEEE.",

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AU - Pang, Jun

AU - Qu, Hongyang

AU - Yuan, Qixia

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N2 - Boolean networks is a well-established formalism for modelling biological systems. A vital challenge for analyzing a Boolean network is to identify all the attractors. This becomes more challenging for large asynchronous Boolean networks, due to the asynchronous scheme. Existing methods are prohibited due to the well-known state-space explosion problem in large Boolean networks. In this paper, we tackle this challenge by proposing a SCC-based decomposition method. We prove the correctness of our proposed method and demonstrate its efficiency with two real-life biological networks.

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KW - Asynchronous Boolean networks

KW - attractor detection

KW - binary decision diagram

KW - gene regulatory networks

KW - strongly connected components

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Taming Asynchrony for Attractor Detection in Large Boolean Networks

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Keywords

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