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Hokkaido University Collection of Scholarly and Academic Papers >
Graduate School of Information Science and Technology / Faculty of Information Science and Technology >
Peer-reviewed Journal Articles, etc >
Fixed Point Preserving Model Reduction of Boolean Networks Focusing on Complement and Absorption Laws
| Title: | Fixed Point Preserving Model Reduction of Boolean Networks Focusing on Complement and Absorption Laws |
| Authors: | Motoyama, Fuma Browse this author | | Kobayashi, Koichi Browse this author →KAKEN DB | | Yamashita, Yuh Browse this author →KAKEN DB |
| Keywords: | Boolean network | | key Boolean network | | complement and absorption laws | | interaction graph | | model reduction |
| Issue Date: | 1-May-2023 |
| Publisher: | IEICE - Institute of the Electronics, Information and Communication Engineers |
| Journal Title: | IEICE transactions on fundamentals of electronics communications and computer sciences |
| Volume: | E106A |
| Issue: | 5 |
| Start Page: | 721 |
| End Page: | 728 |
| Publisher DOI: | 10.1587/transfun.2022MAP0009 |
| Abstract: | A Boolean network (BN) is well known as a discrete model for analysis and control of complex networks such as gene regulatory net-works. Since complex networks are large-scale in general, it is important to consider model reduction. In this paper, we consider model reduction that the information on fixed points (singleton attractors) is preserved. In model reduction studied here, the interaction graph obtained from a given BN is utilized. In the existing method, the minimum feedback vertex set (FVS) of the interaction graph is focused on. The dimension of the state is reduced to the number of elements of the minimum FVS. In the proposed method, we focus on complement and absorption laws of Boolean functions in substitution operations of a Boolean function into other one. By simpli-fying Boolean functions, the dimension of the state may be further reduced. Through a numerical example, we present that by the proposed method, the dimension of the state can be reduced for BNs that the dimension of the state cannot be reduced by the existing method. |
| Rights: | copyright©2023 IEICE |
| Type: | article |
| URI: | http://hdl.handle.net/2115/90180 |
| Appears in Collections: | 情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: 小林 孝一
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