Identification of Important Nodes in Directed Biological Networks: A Network Motif Approach

Identification of important nodes in complex networks has attracted an increasing attention over the last decade. Various measures have been proposed to characterize the importance of nodes in complex networks, such as the degree, betweenness and PageRank. Different measures consider different aspects of complex networks. Although there are numerous results reported on undirected complex networks, few results have been reported on directed biological networks. Based on network motifs and principal component analysis (PCA), this paper aims at introducing a new measure to characterize node importance in directed biological networks. Investigations on five real-world biological networks indicate that the proposed method can robustly identify actually important nodes in different networks, such as finding command interneurons, global regulators and non-hub but evolutionary conserved actually important nodes in biological networks. Receiver Operating Characteristic (ROC) curves for the five networks indicate remarkable prediction accuracy of the proposed measure. The proposed index provides an alternative complex network metric. Potential implications of the related investigations include identifying network control and regulation targets, biological networks modeling and analysis, as well as networked medicine.近十年来，复杂网络中重要节点的识别问题受到了学界日益广泛的关注。学界已提出多种测度以刻画复杂网络内节点的重要性，例如度（degree）、介数（betweenness）与PageRank算法。不同测度从复杂网络的不同维度考量节点重要性。尽管针对无向复杂网络已有大量研究成果，但针对有向生物网络的相关报道却相对匮乏。本文基于网络模体（network motifs）与主成分分析（PCA），提出一种全新的测度以刻画有向生物网络中的节点重要性。对五个真实生物网络的研究分析表明，所提方法能够稳健地识别不同网络中的实际重要节点，例如在生物网络中找到命令性中间神经元（command interneurons）、全局调控因子（global regulators）以及非枢纽但具备进化保守性的实际重要节点。针对这五个网络的受试者工作特征曲线（Receiver Operating Characteristic, ROC）显示，所提测度具备优异的预测精度。该指标为复杂网络提供了一种新的测度维度。相关研究的潜在应用价值涵盖网络控制与调控靶点识别、生物网络建模与分析，以及网络医学领域。