计算机科学技术学报 ›› 2019,Vol. 34 ›› Issue (1): 16-34.doi: 10.1007/s11390-019-1896-x

所属专题： 不能删除； Artificial Intelligence and Pattern Recognition； Emerging Areas

• • 上一篇    下一篇

可控性及其在生物网络的应用

Lin Wu1, Min Li2, Jian-Xin Wang2, and Fang-Xiang Wu1,2,3,*, Senior Member, IEEE   

  1. 1 Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N59, Canada;
    2 School of Information Science and Engineering, Central South University, Changsha 410083, China;
    3 Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
  • 收稿日期:2018-07-01 修回日期:2018-11-08 出版日期:2019-01-05 发布日期:2019-01-12
  • 通讯作者: Fang-Xiang Wu E-mail:jxwang@csu.edu.cn
  • 作者简介:Lin Wu received his B.Sc. degree in computer science and technology from Central South University, Changsha, in 2012. Currently, he is working toward his Ph.D. degree in the Division of Biomedical Engineering at the University of Saskatchewan, Saskatoon, Canada. His current research interests include analysis of large-scale biological data, modeling, analysis, and control of biomolecular networks.
  • 基金资助:
    The work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), the National Natural Science Foundation of China under Grant Nos. 61772552 and 61622213, and Chinese Scholarship Council (CSC).

Controllability and Its Applications to Biological Networks

Lin Wu1, Min Li2, Jian-Xin Wang2, and Fang-Xiang Wu1,2,3,*, Senior Member, IEEE   

  1. 1 Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N59, Canada;
    2 School of Information Science and Engineering, Central South University, Changsha 410083, China;
    3 Department of Mechanical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
  • Received:2018-07-01 Revised:2018-11-08 Online:2019-01-05 Published:2019-01-12
  • Contact: Fang-Xiang Wu E-mail:jxwang@csu.edu.cn
  • About author:Lin Wu received his B.Sc. degree in computer science and technology from Central South University, Changsha, in 2012. Currently, he is working toward his Ph.D. degree in the Division of Biomedical Engineering at the University of Saskatchewan, Saskatoon, Canada. His current research interests include analysis of large-scale biological data, modeling, analysis, and control of biomolecular networks.
  • Supported by:
    The work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), the National Natural Science Foundation of China under Grant Nos. 61772552 and 61622213, and Chinese Scholarship Council (CSC).

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生物元素通常需要通过相互作用来实现各自的功能，并因此形成生物网络。掌握控制生物网络的能力对于制药，医疗和科研有着重要意义。尽管有许多数学计算方法研究怎么将动态系统转移到目标状态，但这些方法并不适用于复杂的生物网络。这其中的原因在于缺少用于描述生物元素间相互作用的精确的动力学模型，以及因为计算量庞大，许多数学计算方法并不适用于大型的网络。最近，控制论中的概念-可控性，被应用在研究复杂网络的动态中。这篇文章介绍了复杂网络可控性及其在生物网络上的应用的最新研究进展。
建立动力学模型是研究生物网络动态的首要问题。首先，我们介绍了一个广泛应用于研究复杂网络可控性的动力学模型。接着我们介绍了最近的关于网络可控性的理论和使网络可控的方法。最后，我们介绍了可控性研究在生物网络上的应用。应用结果表明生物网络可控性研究对很多生理和医疗问题带来启发，比如从系统的角度揭示了生理机制以及药物靶点识别。
目的：综述网络可控性在生物网络的应用。
创新点：从可控性这个控制论中的概念出发，研究复杂生物网络。
方法：文章首先综述了普通复杂网络可控性研究的最近进展，包括理论以及相关算法。然后介绍了这些算法应用到不同生物网络中的结果以及发现。
结论：网络的可控性研究提供了一个从系统角度研究生物网络的方法。大量应用结果表明可控性研究能够揭示许多生理和病理现象，并且在药物靶点识别等医疗方向有巨大应用前景。

关键词: 生物网络, 网络可控性, 控制节点

Abstract: Biological elements usually exert their functions through interactions with others to form various types of biological networks. The ability of controlling the dynamics of biological networks is of enormous benefits to pharmaceutical and medical industry as well as scientific research. Though there are many mathematical methods for steering dynamic systems towards desired states, the methods are usually not feasible for applying to complex biological networks. The difficulties come from the lack of accurate model that can capture the dynamics of interactions between biological elements and the fact that many mathematical methods are computationally intractable for large-scale networks. Recently, a concept in control theory—controllability, has been applied to investigate the dynamics of complex networks. In this article, recent advances on the controllability of complex networks and applications to biological networks are reviewed. Developing dynamic models is the prior concern for analyzing dynamics of biological networks. First, we introduce a widely used dynamic model for investigating controllability of complex networks. Then recent studies of theorems and algorithms for having complex biological networks controllable in general or specific application scenarios are reviewed. Finally, applications to real biological networks manifest that investigating the controllability of biological networks can shed lights on many critical physiological or medical problems, such as revealing biological mechanisms and identifying drug targets, from a systematic perspective.

Key words: biological network, network controllability, steering node

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