中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification

文献类型:期刊论文

作者Guo, Wei-Feng1; Zhang, Shao-Wu1; Chen, Luonan1; Shi, Qian-Qian2; Zhang, Cheng-Ming2; Zeng, Tao2; Chen, Luonan2,3
刊名BMC GENOMICS
出版日期2018
卷号19期号:supl.1页码:924
ISSN号1471-2164
关键词Edge Biomarkers Cancer-cells Controllability Individuals Sensitivity Resistance Profiles Disease Genes
DOI10.1186/s12864-017-4332-z
文献子类Article; Proceedings Paper
英文摘要

Background: The advances in target control of complex networks not only can offer new insights into the general control dynamics of complex systems, but also be useful for the practical application in systems biology, such as discovering new therapeutic targets for disease intervention. In many cases, e.g. drug target identification in biological networks, we usually require a target control on a subset of nodes (i.e., disease-associated genes) with minimum cost, and we further expect that more driver nodes consistent with a certain well-selected network nodes (i.e., prior-known drug-target genes). Results: Therefore, motivated by this fact, we pose and address a new and practical problem called as target control problem with objectives-guided optimization (TCO): how could we control the interested variables (or targets) of a system with the optional driver nodes by minimizing the total quantity of drivers and meantime maximizing the quantity of constrained nodes among those drivers. Here, we design an efficient algorithm (TCOA) to find the optional driver nodes for controlling targets in complex networks. We apply our TCOA to several real-world networks, and the results support that our TCOA can identify more precise driver nodes than the existing control-fucus approaches. Furthermore, we have applied TCOA to two bimolecular expert-curate networks. Source code for our TCOA is freely available from http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm or https://github.com/WilfongGuo/guoweifeng. Conclusions: In the previous theoretical research for the full control, there exists an observation and conclusion that the driver nodes tend to be low-degree nodes. However, for target control the biological networks, we find interestingly that the driver nodes tend to be high-degree nodes, which is more consistent with the biological experimental observations. Furthermore, our results supply the novel insights into how we can efficiently target control a complex system, and especially many evidences on the practical strategic utility of TCOA to incorporate prior drug information into potential drug-target forecasts. Thus applicably, our method paves a novel and efficient way to identify the drug targets for leading the phenotype transitions of underlying biological networks.

WOS研究方向Biotechnology & Applied Microbiology ; Genetics & Heredity
语种英语
WOS记录号WOS:000422886100007
版本出版稿
源URL[http://202.127.25.143/handle/331003/3371]  
专题生化所2018年发文
通讯作者Zhang, Shao-Wu; Zeng, Tao
作者单位1.Northwestern Polytech Univ, Sch Automat, Key Lab Informat Fus Technol, Minist Educ, Xian 710072, Shaanxi, Peoples R China;
2.Univ Chinese Acad Sci, Inst Biochem & Cell Biol, Key Lab Syst Biol, Shanghai 200000, Peoples R China;
3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai 200000, Peoples R China
推荐引用方式
GB/T 7714
Guo, Wei-Feng,Zhang, Shao-Wu,Chen, Luonan,et al. A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification[J]. BMC GENOMICS,2018,19(supl.1):924.
APA Guo, Wei-Feng.,Zhang, Shao-Wu.,Chen, Luonan.,Shi, Qian-Qian.,Zhang, Cheng-Ming.,...&Chen, Luonan.(2018).A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.BMC GENOMICS,19(supl.1),924.
MLA Guo, Wei-Feng,et al."A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification".BMC GENOMICS 19.supl.1(2018):924.

入库方式: OAI收割

来源:上海生物化学与细胞生物学研究所

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