A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.
A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification.
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一种寻找目标控制复杂网络最佳驱动节点的新算法及其在药物靶标识别中的应用
DOI：
10.1186/s12864-017-4332-z
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发表时间：
2018-01-19
期刊：
影响因子：
4.4
通讯作者：
Chen L
中科院分区：
文献类型：
--
作者：
Guo WF;Zhang SW;Shi QQ;Zhang CM;Zeng T;Chen L
背景复杂网络靶向控制的研究进展不仅可以对复杂系统的一般控制动力学提供新的见解，而且有助于系统生物学的实际应用，如发现新的疾病干预治疗靶点。在许多情况下，例如生物网络中的药物靶标识别，我们通常需要以最小的代价对节点子集(即疾病相关基因)进行靶标控制，并进一步期望更多的驱动节点与某个精心选择的网络节点(即已知的药物靶标基因)保持一致。我们提出并解决了一个新的实际问题--目标引导优化目标控制问题：如何通过最小化驱动节点总数，同时最大化这些驱动节点之间的约束节点数量，来控制具有可选驱动节点的系统中感兴趣的变量(或目标)。在这里，我们设计了一种有效的算法(TCoA)来寻找复杂网络中控制目标的可选驱动节点。我们将我们的TCoA应用于几个真实世界的网络，结果支持我们的TCoA可以比现有的控制-FUUS方法识别更精确的驱动节点。此外，我们还将TCoA应用于两个双分子专家-专家网络。我们的TCoA的源代码可以从http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm或https://github.com/WilfongGuo/guoweifeng免费获得。结论在之前关于完全控制的理论研究中，存在一个观察和结论，即驱动节点倾向于低度节点。然而，对于生物网络的目标控制，我们有趣地发现，驱动节点往往是高度节点，这与生物实验观察到的更一致。此外，我们的结果为我们如何有效地靶向控制一个复杂的系统提供了新的见解，特别是为TCoA将先前的药物信息纳入潜在的药物靶标预测的实际战略效用提供了许多证据。因此，我们的方法为识别引导潜在生物网络表型转换的药物靶点铺平了一条新的和有效的途径。
BackgroundThe 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).ResultsTherefore, 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 .ConclusionsIn 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.
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DOI：
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