Discovery of Complex Recurring Protein Interaction Patterns within Interactomes: Algorithms, Applications and Software

相互作用组中复杂重复蛋白质相互作用模式的发现：算法、应用程序和软件

• 批准号: 0850063
• 负责人: Mona Singh
• 金额: $ 61.3万
• 依托单位: Princeton University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0850063&HistoricalAwards=false
• 关键词: Discovery; Complex; Recurring; Protein; Interaction

A grant has been awarded to Princeton University to develop computational tools to facilitate research in biological networks through discovery and analysis of recurring patterns of relations among biological units such as proteins. Searching for recurring patterns in biological data has been the backbone of much research and analysis in computational biology, and has been essential in uncovering biological function. In the last few years, there has been an explosion in the availability of protein-protein interaction data, and we now have large-scale interaction networks for human and many model organisms.The overall goal of this research is to develop the necessary computational infrastructure for applying recurring pattern analysis---which has already proven to be useful in analyzing biological sequence, structure and expression data---to biological networks. The specific goals of this research are: (1) To develop a computational framework for uncovering what types of proteins preferentially work together, with the goal of revealing patterns underlying cellular organization and protein functioning. (2) To apply these algorithms to existing interaction networks across the evolutionary range, with the goals of understanding how the recurring network units within organisms differ and of revealing how new types of proteins are incorporated into existing networks. (3) To develop software that, given a particular protein sequence, uncovers the recurring network interaction patterns it participates in, with the goal of placing the input protein within the context of its cellular pathways and modules, and thereby gaining insight into its function. The proposed research is coupled with the development of a new undergraduate course in bioinformatics, with a final project focusing on network analysis. Software and results of this project will be available from the website http://compbio.cs.princeton.edu.

已向普林斯顿大学提供一笔赠款，用于开发计算工具，通过发现和分析蛋白质等生物单位之间关系的重复模式，促进生物网络的研究。在生物数据中寻找重复模式一直是计算生物学中许多研究和分析的支柱，并且在揭示生物功能方面至关重要。 在过去的几年里，蛋白质相互作用数据的可用性出现了爆炸式的增长，我们现在已经有了人类和许多模式生物的大规模相互作用网络，本研究的总体目标是开发必要的计算基础设施，以将重复模式分析应用于生物网络，重复模式分析在分析生物序列、结构和表达数据方面已经被证明是有用的。 本研究的具体目标是：（1）开发一个计算框架，用于揭示哪些类型的蛋白质优先一起工作，目的是揭示细胞组织和蛋白质功能的潜在模式。(2)将这些算法应用于整个进化范围内现有的相互作用网络，目的是了解生物体内重复出现的网络单元如何不同，并揭示新型蛋白质如何被纳入现有网络。 (3)开发软件，给定特定的蛋白质序列，揭示它参与的重复网络相互作用模式，目标是将输入蛋白质置于其细胞通路和模块的背景下，从而深入了解其功能。 拟议的研究是再加上一个新的本科生课程在生物信息学的发展，与网络分析的重点最终项目。该项目的软件和结果将在网站http://compbio.cs.princeton.edu上提供。