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首页> 外文期刊>BMC Genomics >A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification
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A novel algorithm for finding optimal driver nodes to target control complex networks and its applications for drug targets identification

机译：寻找最佳驾驶员节点以控制复杂网络的新算法及其在药物靶标识别中的应用

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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). 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 . 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.
机译：复杂网络目标控制的进展不仅可以为复杂系统的一般控制动力学提供新的见解，而且对于系统生物学中的实际应用（例如发现疾病干预的新治疗目标）也很有用。在许多情况下，例如在生物网络中进行药物目标识别时，我们通常需要以最小的成本对节点子集（即与疾病相关的基因）进行目标控制，并且我们进一步希望有更多的驱动程序节点与某些经过精心选择的网络节点（即先前已知的药物靶向基因）。因此，基于这一事实，我们提出并解决了一个新的实际问题，即目标导向优化（TCO）的目标控制问题：如何通过以下方法控制具有可选驱动程序节点的系统的感兴趣变量（或目标）：最小化驱动程序的总数，同时最大化那些驱动程序中受约束节点的数量。在这里，我们设计了一种有效的算法（TCOA），以找到用于控制复杂网络中目标的可选驱动程序节点。我们将TCOA应用于多个实际网络，结果证明，与现有的控制焦点方法相比，我们的TCOA可以识别更精确的驱动程序节点。此外，我们已将TCOA应用于两个双分子专家管理网络。可从http://sysbio.sibcb.ac.cn/cb/chenlab/software.htm或https://github.com/WilfongGuo/guoweifeng免费获得我们的TCOA的源代码。在先前关于完全控制的理论研究中，存在观察和结论，即驱动器节点倾向于是低度节点。然而，对于目标控制生物网络，我们有趣地发现驱动程序节点倾向于是高度节点，这与生物学实验观察结果更加一致。此外，我们的研究结果为我们如何有效地控制复杂系统提供了新颖的见解，尤其是关于TCOA将先前药物信息纳入潜在药物靶标预测的实际战略实用性的许多证据。因此，适用地，我们的方法为鉴定潜在的潜在生物学网络表型转变的药物靶标铺平了新的有效途径。

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