Wei-Feng Guo1, Shao-Wu Zhang2, Qian-Qian Shi3, Cheng-Ming Zhang3, Tao Zeng4, Luonan Chen5,6,7. 1. Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China. 2. Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China. zhangsw@nwpu.edu.cn. 3. Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, University of Chinese Academy of sciences, Shanghai, 200000, China. 4. Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, University of Chinese Academy of sciences, Shanghai, 200000, China. zengtao@sibs.ac.cn. 5. Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi'an, 710072, China. lnchen@sibs.ac.cn. 6. Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, University of Chinese Academy of sciences, Shanghai, 200000, China. lnchen@sibs.ac.cn. 7. School of Life Science and Technology, ShanghaiTech University, Shanghai, 200000, China. lnchen@sibs.ac.cn.
Abstract
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.
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.
Entities:
Keywords:
Drug targets; Dynamics of complex networks; Objectives-guided optimization; Target control
Authors: Richard Marcotte; Kevin R Brown; Fernando Suarez; Azin Sayad; Konstantina Karamboulas; Paul M Krzyzanowski; Fabrice Sircoulomb; Mauricio Medrano; Yaroslav Fedyshyn; Judice L Y Koh; Dewald van Dyk; Bodhana Fedyshyn; Marianna Luhova; Glauber C Brito; Franco J Vizeacoumar; Frederick S Vizeacoumar; Alessandro Datti; Dahlia Kasimer; Alla Buzina; Patricia Mero; Christine Misquitta; Josee Normand; Maliha Haider; Troy Ketela; Jeffrey L Wrana; Robert Rottapel; Benjamin G Neel; Jason Moffat Journal: Cancer Discov Date: 2011-12-29 Impact factor: 39.397