Lee Robin E C, Megeney Lynn A
Ottawa Health Research Institute, Molecular Medicine Program, Ottawa, Canada.
BMC Bioinformatics. 2005 Nov 9;6:271. doi: 10.1186/1471-2105-6-271.
The availability of interaction databases provides an opportunity for researchers to utilize immense amounts of data exclusively in silico. Recently there has been an emphasis on studying the global properties of biological interactions using network analysis. While this type of analysis offers a wide variety of global insights it has surprisingly not been used to examine more localized interactions based on mechanism. In as such we have particular interest in the role of key topological components in signal transduction cascades as they are vital regulators of healthy and diseased cell states.
We have used publicly available databases and a novel software tool termed Hubview to model the interactions of a subset of the yeast interactome, specifically protein kinases and their interaction partners. Analysis of the connectivity distribution has inferred a fat-tailed degree distribution with parameters consistent with those found in other biological networks. In addition, Hubview identified a functional clustering of a large group of kinases, distributed between three separate groupings. The complexity and average degree for each of these clusters is indicative of a specialized function (cell cycle propagation, DNA repair and pheromone response) and relative age for each cluster.
Using connectivity analysis on a functional subset of proteins we have evidence that reinforces the scale free topology as a model for protein network evolution. We have identified the hub components of the kinase network and observed a tendency for these kinases to cluster together on a functional basis. As such, these results suggest an inherent trend to preserve scale free characteristics at a domain based modular level within large evolvable networks.
相互作用数据库的可用性为研究人员提供了仅在计算机上利用大量数据的机会。最近,人们强调使用网络分析来研究生物相互作用的全局特性。虽然这种分析提供了各种各样的全局见解,但令人惊讶的是,它尚未被用于基于机制研究更局部的相互作用。因此,我们特别关注关键拓扑成分在信号转导级联中的作用,因为它们是健康和患病细胞状态的重要调节因子。
我们使用了公开可用的数据库和一种名为Hubview的新型软件工具,对酵母相互作用组的一个子集(特别是蛋白激酶及其相互作用伙伴)的相互作用进行建模。对连通性分布的分析推断出一个胖尾度分布,其参数与在其他生物网络中发现的参数一致。此外,Hubview识别出一大组激酶的功能聚类,分布在三个不同的分组中。这些聚类中每一个的复杂性和平均度都表明了一种特殊功能(细胞周期传播、DNA修复和信息素反应)以及每个聚类的相对年龄。
通过对蛋白质功能子集进行连通性分析,我们有证据支持无标度拓扑作为蛋白质网络进化模型。我们已经识别出激酶网络的枢纽成分,并观察到这些激酶在功能基础上聚集在一起的趋势。因此,这些结果表明在大型可进化网络中,在基于结构域的模块水平上存在保留无标度特征的内在趋势。