The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Redes booleanas (BNs) são consideradas modelos formais populares para a dinâmica de redes reguladoras de genes. Existem muitos tipos diferentes de BNs, dependendo de seu esquema de atualização (síncronos, assíncronos, determinísticos ou não determinísticos), como Redes Booleanas Aleatórias Clássicas (CRBNs), Redes Booleanas Aleatórias Assíncronas (ARBNs), Redes Booleanas Aleatórias Assíncronas Generalizadas ( GARBNs), Redes Booleanas Aleatórias Assíncronas Determinísticas (DARBNs) e Redes Booleanas Aleatórias Assíncronas Generalizadas Determinísticas (DGARBNs). Um importante comportamento de longo prazo dos BNs, o chamado atrator, pode fornecer informações valiosas sobre a biologia de sistemas (por exemplo, as origens do câncer). No artigo anterior [1], estudamos propriedades de atratores de GARBNs, suas relações com atratores de CRBNs, também propusemos diferentes algoritmos para detecção de atratores. Neste artigo, propomos um novo algoritmo baseado na verificação de modelo limitado baseado em SAT para superar problemas inerentes a esses algoritmos. Resultados experimentais comprovam a eficácia do novo algoritmo. Mostramos também que estudar atratores de GARBNs pode abrir caminhos potenciais para estudar atratores de ARBNs.
Van Giang TRINH
the Japan Advanced Institute of Science and Technology
Kunihiko HIRAISHI
the Japan Advanced Institute of Science and Technology
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Van Giang TRINH, Kunihiko HIRAISHI, "A Study on Attractors of Generalized Asynchronous Random Boolean Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 8, pp. 987-994, August 2020, doi: 10.1587/transfun.2019EAP1163.
Abstract: Boolean networks (BNs) are considered as popular formal models for the dynamics of gene regulatory networks. There are many different types of BNs, depending on their updating scheme (synchronous, asynchronous, deterministic, or non-deterministic), such as Classical Random Boolean Networks (CRBNs), Asynchronous Random Boolean Networks (ARBNs), Generalized Asynchronous Random Boolean Networks (GARBNs), Deterministic Asynchronous Random Boolean Networks (DARBNs), and Deterministic Generalized Asynchronous Random Boolean Networks (DGARBNs). An important long-term behavior of BNs, so-called attractor, can provide valuable insights into systems biology (e.g., the origins of cancer). In the previous paper [1], we have studied properties of attractors of GARBNs, their relations with attractors of CRBNs, also proposed different algorithms for attractor detection. In this paper, we propose a new algorithm based on SAT-based bounded model checking to overcome inherent problems in these algorithms. Experimental results prove the effectiveness of the new algorithm. We also show that studying attractors of GARBNs can pave potential ways to study attractors of ARBNs.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019EAP1163/_p
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@ARTICLE{e103-a_8_987,
author={Van Giang TRINH, Kunihiko HIRAISHI, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Study on Attractors of Generalized Asynchronous Random Boolean Networks},
year={2020},
volume={E103-A},
number={8},
pages={987-994},
abstract={Boolean networks (BNs) are considered as popular formal models for the dynamics of gene regulatory networks. There are many different types of BNs, depending on their updating scheme (synchronous, asynchronous, deterministic, or non-deterministic), such as Classical Random Boolean Networks (CRBNs), Asynchronous Random Boolean Networks (ARBNs), Generalized Asynchronous Random Boolean Networks (GARBNs), Deterministic Asynchronous Random Boolean Networks (DARBNs), and Deterministic Generalized Asynchronous Random Boolean Networks (DGARBNs). An important long-term behavior of BNs, so-called attractor, can provide valuable insights into systems biology (e.g., the origins of cancer). In the previous paper [1], we have studied properties of attractors of GARBNs, their relations with attractors of CRBNs, also proposed different algorithms for attractor detection. In this paper, we propose a new algorithm based on SAT-based bounded model checking to overcome inherent problems in these algorithms. Experimental results prove the effectiveness of the new algorithm. We also show that studying attractors of GARBNs can pave potential ways to study attractors of ARBNs.},
keywords={},
doi={10.1587/transfun.2019EAP1163},
ISSN={1745-1337},
month={August},}
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TY - JOUR
TI - A Study on Attractors of Generalized Asynchronous Random Boolean Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 987
EP - 994
AU - Van Giang TRINH
AU - Kunihiko HIRAISHI
PY - 2020
DO - 10.1587/transfun.2019EAP1163
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2020
AB - Boolean networks (BNs) are considered as popular formal models for the dynamics of gene regulatory networks. There are many different types of BNs, depending on their updating scheme (synchronous, asynchronous, deterministic, or non-deterministic), such as Classical Random Boolean Networks (CRBNs), Asynchronous Random Boolean Networks (ARBNs), Generalized Asynchronous Random Boolean Networks (GARBNs), Deterministic Asynchronous Random Boolean Networks (DARBNs), and Deterministic Generalized Asynchronous Random Boolean Networks (DGARBNs). An important long-term behavior of BNs, so-called attractor, can provide valuable insights into systems biology (e.g., the origins of cancer). In the previous paper [1], we have studied properties of attractors of GARBNs, their relations with attractors of CRBNs, also proposed different algorithms for attractor detection. In this paper, we propose a new algorithm based on SAT-based bounded model checking to overcome inherent problems in these algorithms. Experimental results prove the effectiveness of the new algorithm. We also show that studying attractors of GARBNs can pave potential ways to study attractors of ARBNs.
ER -