Michoel Tom, Joshi Anagha, Nachtergaele Bruno, Van de Peer Yves
Freiburg Institute for Advanced Studies, University of Freiburg, Albertstrasse 19, Freiburg, Germany.
Mol Biosyst. 2011 Oct;7(10):2769-78. doi: 10.1039/c1mb05241a. Epub 2011 Aug 23.
Topological network motifs represent functional relationships within and between regulatory and protein-protein interaction networks. Enriched motifs often aggregate into self-contained units forming functional modules. Theoretical models for network evolution by duplication-divergence mechanisms and for network topology by hierarchical scale-free networks have suggested a one-to-one relation between network motif enrichment and aggregation, but this relation has never been tested quantitatively in real biological interaction networks. Here we introduce a novel method for assessing the statistical significance of network motif aggregation and for identifying clusters of overlapping network motifs. Using an integrated network of transcriptional, posttranslational and protein-protein interactions in yeast we show that network motif aggregation reflects a local modularity property which is independent of network motif enrichment. In particular our method identified novel functional network themes for a set of motifs which are not enriched yet aggregate significantly and challenges the conventional view that network motif enrichment is the most basic organizational principle of complex networks.
拓扑网络基序代表调控网络和蛋白质-蛋白质相互作用网络内部以及它们之间的功能关系。富集的基序常常聚集成自成一体的单元，形成功能模块。通过复制-分化机制进行网络演化以及通过分层无标度网络进行网络拓扑的理论模型表明，网络基序富集与聚集之间存在一对一的关系，但这种关系从未在真实的生物相互作用网络中进行过定量测试。在此，我们引入了一种新方法，用于评估网络基序聚集的统计显著性并识别重叠网络基序的簇。利用酵母中转录、翻译后和蛋白质-蛋白质相互作用的整合网络，我们表明网络基序聚集反映了一种局部模块化特性，该特性独立于网络基序富集。特别是，我们的方法为一组尚未富集但聚集显著的基序识别出了新的功能网络主题，并对网络基序富集是复杂网络最基本组织原则的传统观点提出了挑战。
