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Identification of critical connectors in the directed reaction-centric graphs of microbial metabolic networks

机译:识别在微生物代谢网络的指导反应中心图中的关键连接器

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Detection of central nodes in asymmetrically directed biological networks depends on centrality metrics quantifying individual nodes' importance in a network. In topological analyses on metabolic networks, various centrality metrics have been mostly applied to metabolite-centric graphs. However, centrality metrics including those not depending on high connections are largely unexplored for directed reaction-centric graphs. We applied directed versions of centrality metrics to directed reaction-centric graphs of microbial metabolic networks. To investigate the local role of a node, we developed a novel metric, cascade number, considering how many nodes are closed off from information flow when a particular node is removed. High modularity and scale-freeness were found in the directed reaction-centric graphs and betweenness centrality tended to belong to densely connected modules. Cascade number and bridging centrality identified cascade subnetworks controlling local information flow and irreplaceable bridging nodes between functional modules, respectively. Reactions highly ranked with bridging centrality and cascade number tended to be essential, compared to reactions that other central metrics detected. We demonstrate that cascade number and bridging centrality are useful to identify key reactions controlling local information flow in directed reaction-centric graphs of microbial metabolic networks. Knowledge about the local flow connectivity and connections between local modules will contribute to understand how metabolic pathways are assembled.
机译:在非对称定向生物网络中检测中央节点取决于量化网络中的各个节点的重要性。在代谢网络上的拓扑分析中,各种中心度量大多应用于以代谢物为中心的图形。然而,包括不属于高连接的中心度量,主要是针对指导的反应形式的图形而不是探索。我们将各个中心度量的指示版本应用于指导的微生物代谢网络的以指导的反应为中心。为了调查节点的本地作用,我们开发了一种新颖的公制级联号码,考虑删除特定节点时,考虑从信息流中截止了多少个节点。在指导的反应形式的图形中发现了高模块化和尺度 - Freeness,并且往往属于密集连接的模块的中心地位。级联数字和桥接中心分别确定了级联子网,分别控制了功能模块之间的本地信息流和不可替代的桥接节点。与其他中央度量检测到的反应相比,对桥接中心和级联数字具有高度排名的反应是必不可少的。我们证明级联数量和桥接中心是有用的,可用于识别控制局部信息流的关键反应,以指导的微生物代谢网络的以中心为中心的图形。关于本地模块之间的本地流连接和连接的知识将有助于了解代谢途径是如何组装的。

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