IEICE TRANSACTIONS on Fundamentals
A Study on Attractors of Generalized Asynchronous Random Boolean Networks
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.8 pp.987-994
- Publication Date
- 2020/08/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2019EAP1163
- Type of Manuscript
- PAPER
- Category
- Mathematical Systems Science
Authors
Van Giang TRINH
the Japan Advanced Institute of Science and Technology
Kunihiko HIRAISHI
the Japan Advanced Institute of Science and Technology
Keyword
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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},}
Copy
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 -
English
