Global snapshot of a protein interaction network - a percolation based approach
Global snapshot of a protein interaction network - a percolation based approach
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DOI：
10.1093/bioinformatics/btg339
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发表时间：
2003-12-12
期刊：
影响因子：
5.8
通讯作者：
Samanta, MP
中科院分区：
文献类型：
--
作者：
Chin, CS;Samanta, MP
动机：生物学上重要的信息可以通过使用基于图论的网络分析技术对大规模蛋白质相互作用数据进行建模来揭示。然而，目前正在使用的方法从本地连接数据中得出关于网络全局特征的结论。一个更系统的方法是定义全局量，测量（1）蛋白质与网络其他部分的联系有多强，以及（2）相互作用对网络完整性的贡献有多大，并将它们与其他来源的表型数据联系起来。在本文中，我们介绍了这样的全球连通性措施，并开发了一个随机算法的基础上渗流随机graphics.Results：我们表明，在全球连通性方面，基本蛋白质的分布是不同的背景。这一观察结果突出了网络中必需蛋白和非必需蛋白之间的根本差异。我们还发现，从不同的实验方法，如免疫沉淀和双杂交技术获得的相互作用数据有助于不同的网络完整性。不同实验方法之间的这种差异可以提供对这些技术之间存在的系统性偏差的洞察。
Motivation: Biologically significant information can be revealed by modeling large-scale protein interaction data using graph theory based network analysis techniques. However, the methods that are currently being used draw conclusions about the global features of the network from local connectivity data. A more systematic approach would be to define global quantities that measure (1) how strongly a protein ties with the other parts of the network and (2) how significantly an interaction contributes to the integrity of the network, and connect them with phenotype data from other sources. In this paper, we introduce such global connectivity measures and develop a stochastic algorithm based upon percolation in random graphs to compute them.Results: We show that, in terms of global connectivities, the distribution of essential proteins is distinct from the background. This observation highlights a fundamental difference between the essential and the non-essential proteins in the network. We also find that the interaction data obtained from different experimental methods such as immunoprecipitation and two-hybrid techniques contribute differently to network integrities. Such difference between different experimental methods can provide insight into the systematic bias present among these techniques.