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 |
DOI | 10.1186/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
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