Spectrum analysis based method for dynamics and collective analysis of protein-protein interaction networks

The importance of understanding biological interaction networks has fueled the development of numerous interaction data generation techniques, databases and prediction tools. Generation of high-confident interaction networks formulates the first step towards the study for protein–protein interactions (PPI). A number of experimental methods, based on distinct, physical principles have been developed to identify PPI such as the yeast two-hybrid method (Y2H). In this work, we focus on one example of biological networks, namely the yeast protein interaction network (YPIN). In YPIN, we design and implement a computational model that captures the discrete and stochastic nature of protein interactions. In this model, we apply spectrum analysis method to the variance of the protein nodes which play an important role in the PPI networks, which can show the topology structure of dynamic and collective performances of PPI networks. We take YPIN, such as 48 "quasi-cliques" and 6 "quasi-bipartites" separated from 11855 yeast PPI networks with 2617 proteins, as an example and apply spectrum analysis to show the topology structure of dynamic and collective analysis of PPI networks and the performances. The obtained results may be valuable for deciphering unknown protein functions, determining protein complexes, and inventing drugs. PRIB 2008 proceedings found at: http://dx.doi.org/10.1007/978-3-540-88436-1 Contributors: Monash University. Faculty of Information Technology. Gippsland School of Information Technology ; Chetty, Madhu ; Ahmad, Shandar ; Ngom, Alioune ; Teng, Shyh Wei ; Third IAPR International Conference on Pattern Recognition in Bioinformatics (PRIB) (3rd : 2008 : Melbourne, Australia) ; Coverage: Rights: Copyright by Third IAPR International Conference on Pattern Recognition in Bioinformatics. All rights reserved.解析生物相互作用网络的重要性，推动了大量相互作用数据生成技术、数据库及预测工具的发展。构建高置信度相互作用网络，是开展蛋白质-蛋白质相互作用（protein–protein interactions, PPI）研究的首要步骤。目前已基于不同物理原理开发出多种实验方法用于鉴定PPI，例如酵母双杂交法（yeast two-hybrid method, Y2H）。 本研究聚焦于一类生物网络实例，即酵母蛋白质相互作用网络（yeast protein interaction network, YPIN）。针对YPIN，我们设计并实现了一个计算模型，可捕捉蛋白质相互作用的离散性与随机特性。在该模型中，我们对PPI网络中发挥关键作用的蛋白质节点的方差进行频谱分析，以此揭示PPI网络动态与集体行为的拓扑结构特征。 我们以YPIN为研究实例，该实例取自包含2617个蛋白质的11855个酵母PPI网络，其中包含48个“准团簇”与6个“准二分图”；通过频谱分析，我们展示了PPI网络动态与集体特性的拓扑结构及其表现。本研究所得结果，可为解析未知蛋白质功能、确定蛋白质复合物以及研发药物提供有价值的参考。 相关论文收录于2008年第三届IAPR国际模式识别生物信息学学术会议（PRIB 2008）论文集，可通过以下链接获取：http://dx.doi.org/10.1007/978-3-540-88436-1 贡献者：莫纳什大学（Monash University）信息技术学院吉普斯兰信息技术分校；切蒂·马杜（Chetty, Madhu）；艾哈迈德·尚达尔（Ahmad, Shandar）；恩戈姆·阿利乌内（Ngom, Alioune）；滕诗伟（Teng, Shyh Wei）；第三届IAPR国际模式识别生物信息学学术会议（2008年，澳大利亚墨尔本） 覆盖范围： 版权声明：版权归第三届IAPR国际模式识别生物信息学学术会议所有，保留所有权利。