挖掘蛋白质网络动态特征的方法研究

The analysis of protein-protein interactions is fundamental to the understanding of cellular organization, process, and functions. Recent high-throughput techniques have enriched the available protein interaction data and facilitated the construction of integrated protein-protein interaction networks. Despite the rapid accumulation of systems-level biological data, understanding the dynamic nature of cellular activity remains a difficult and, until recently, a much overlooked task. The reason is that protein-protein interaction data are static. The majority of protein interaction network modeling and analysis has been solely focused on static properties. In this project, we explicitly attempt to detangle the dynamic complexity of protein networks by identifying dynamic modules and detecting changes in dynamic modules. Moreover, there widely exist regulation relationship and upstream/downstream relations between interacting proteins when they are involved in various networks of signal transduction, transcriptional regulation, cell cycle, or metabolism, etc. However, predicting the signaling directions faces a substantial challenge. The reason is that high-throughput protein interaction data are undirected. In this project, we explicitly attempt to infer the high confidence signaling pathways by orienting protein interaction edges. Also, we attempt to discover the impact and interactions of age, energy intake and gender of Mouse central nervous system based on the obvious studies. This project will provide a more comprehensive view of biological systems, bring us to a fuller understanding of our genetic constitution and thus to a more sustainable and healthier future.

生命系统复杂性最重要的特征不仅在于其组成成分的复杂性，更在于各组成成分之间的关系，而在所有的这些关系中，蛋白质之间的相互作用在生命活动中起着核心作用。随着越来越多可利用的蛋白质相互作用网络数据的出现，基于数据挖掘的蛋白质网络分析成为生物信息学领域的研究热点。本项目拟针对蛋白质网络复杂性、动态性等特征，研究蛋白质间相互作用的动态变化机制，提出有效的、合理的、能够反映真实生命过程的分析、挖掘方法，在此基础上研究一些重大疾病病变的机理。主要包括：(1)蛋白质间相互作用动态机制的研究;(2)蛋白质见相互作用信号流方向性的研究;(3) 与大脑衰老相结合的应用研究。本项目的研究将能够系统地了解蛋白质网络及其模块化结构的动态变化机制，从结构和功能等角度提供对蛋白质网络的全新理解。

生命系统复杂性最重要的特征不仅在于其组成成分的复杂性，更在于各组成成分之间的关系，而在所有的这些关系中，蛋白质之间的相互作用在生命活动中起着核心作用。随着越来越多可利用的蛋白质相互作用网络数据的出现，基于数据挖掘的蛋白质网络分析成为生物信息学领域的研究热点。本项目针对蛋白质网络复杂性、动态性等特征，利用高通量基因表达数据及测序数据等蕴含蛋白质动态特征的生物数据，采用多数据融合的方法研究蛋白质间相互作用的动态变化机制，提出了有效的动态蛋白质构建方法以及动态蛋白质模块及复合物识别方法，在此基础上研究了癌症相关基因识别方法。本项目的研究对系统地了解蛋白质网络及其模块化结构的动态变化机制起到了推动作用，从而提供了对蛋白质网络的全新理解。