Department of Statistics and Applied Probability, National University of Singapore NUS, Singapore 117546, Singapore.
Nat Commun. 2013;4:2241. doi: 10.1038/ncomms3241.
Small over-represented motifs in biological networks often form essential functional units of biological processes. A natural question is to gauge whether a motif occurs abundantly or rarely in a biological network. Here we develop an accurate method to estimate the occurrences of a motif in the entire network from noisy and incomplete data, and apply it to eukaryotic interactomes and cell-specific transcription factor regulatory networks. The number of triangles in the human interactome is about 194 times that in the Saccharomyces cerevisiae interactome. A strong positive linear correlation exists between the numbers of occurrences of triad and quadriad motifs in human cell-specific transcription factor regulatory networks. Our findings show that the proposed method is general and powerful for counting motifs and can be applied to any network regardless of its topological structure.
生物网络中较小的过表达模体通常形成生物过程的基本功能单元。一个自然的问题是衡量一个模体在生物网络中是大量出现还是很少出现。在这里,我们开发了一种从噪声和不完整的数据中准确估计整个网络中模体出现次数的方法,并将其应用于真核生物相互作用组和细胞特异性转录因子调控网络。人类相互作用组中的三角形数量大约是酿酒酵母相互作用组中的 194 倍。在人类细胞特异性转录因子调控网络中,三聚体和四联体模体出现次数之间存在很强的正线性相关性。我们的研究结果表明,所提出的方法是一种通用且强大的计数模体的方法,可以应用于任何网络,而与网络的拓扑结构无关。