Attractor-Transition Control of Complex Biological Networks: A Constant Control Approach

IF 10.5 1区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS IEEE Transactions on Cybernetics Pub Date : 2024-10-23 DOI:10.1109/TCYB.2024.3473945
Jung-Min Yang;Chun-Kyung Lee;Namhee Kim;Kwang-Hyun Cho
{"title":"Attractor-Transition Control of Complex Biological Networks: A Constant Control Approach","authors":"Jung-Min Yang;Chun-Kyung Lee;Namhee Kim;Kwang-Hyun Cho","doi":"10.1109/TCYB.2024.3473945","DOIUrl":null,"url":null,"abstract":"This article presents attractor-transition control of complex biological networks represented by Boolean networks (BNs) wherein the BN is steered from a prescribed initial attractor toward a desired one. The proposed approach leverages the similarity between attractors and Boolean algebraic properties embedded in the underlying state transition equations. To enhance the clarity of expression regarding stabilization toward the desired attractor, a simple coordinate transformation is performed on the considered BN. Based on the characteristics of transformed state equations, self-stabilizing state variables requiring no control efforts are derived in the first. Next, by applying the feedback vertex set (FVS) control scheme, control inputs stabilizing the remaining state variables are determined. The proposed control scheme exhibits versatility by accommodating both fixed-point and cyclic attractors. We validate the effectiveness of the proposed strategy through extensive numerical experiments conducted on random BNs as well as complex biological systems. In adherence to the reproducible research initiative, detailed results of numerical experiments and all the implementation codes are provided on the authors’ website: \n<uri>https://github.com/choonlog/AttractorTransition</uri>\n.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"55 1","pages":"24-37"},"PeriodicalIF":10.5000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cybernetics","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10731478/","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

This article presents attractor-transition control of complex biological networks represented by Boolean networks (BNs) wherein the BN is steered from a prescribed initial attractor toward a desired one. The proposed approach leverages the similarity between attractors and Boolean algebraic properties embedded in the underlying state transition equations. To enhance the clarity of expression regarding stabilization toward the desired attractor, a simple coordinate transformation is performed on the considered BN. Based on the characteristics of transformed state equations, self-stabilizing state variables requiring no control efforts are derived in the first. Next, by applying the feedback vertex set (FVS) control scheme, control inputs stabilizing the remaining state variables are determined. The proposed control scheme exhibits versatility by accommodating both fixed-point and cyclic attractors. We validate the effectiveness of the proposed strategy through extensive numerical experiments conducted on random BNs as well as complex biological systems. In adherence to the reproducible research initiative, detailed results of numerical experiments and all the implementation codes are provided on the authors’ website: https://github.com/choonlog/AttractorTransition .
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
复杂生物网络的吸引器-转换控制:恒定控制方法
本文提出了以布尔网络(BN)为代表的复杂生物网络的吸引子-转移控制,其中BN从规定的初始吸引子转向期望的吸引子。所提出的方法利用了吸引子和嵌入在底层状态转移方程中的布尔代数性质之间的相似性。为了提高对期望吸引子稳定性的清晰表达,对所考虑的BN进行了简单的坐标变换。首先根据变换状态方程的特点,导出了不需要控制的自稳定状态变量。其次,通过应用反馈顶点集(FVS)控制方案,确定稳定剩余状态变量的控制输入。所提出的控制方案具有通用性,可容纳不动点和循环吸引子。我们通过在随机生物网络和复杂生物系统上进行的大量数值实验验证了所提出策略的有效性。根据可重复研究的倡议,数值实验的详细结果和所有实现代码都在作者的网站上提供:https://github.com/choonlog/AttractorTransition。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Transactions on Cybernetics
IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS
CiteScore
25.40
自引率
11.00%
发文量
1869
期刊介绍: The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.
期刊最新文献
Event-Based Estimation Over Hydrogen AAV-Based Relay Network With Silent Packet Loss. LASFNet: A Lightweight Attention-Guided Self-Modulation Feature Fusion Network for Multimodal Object Detection. HEQP: A Hypergraph Neural Network-Based Evolutionary Method for Large-Scale QCQPs. Adaptive Iterative Learning Reliable Control of Nonrepetitive Systems With Multiple Iteration-Varying Parametric Uncertainties. Robotic Assistive Optimization and Control Using Neural Dynamics and Adaptive Neural Network.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1