关键词: 蛋白质网络, 动态, 基因表达, 蛋白质复合物, 复杂疾病

Abstract: The rapid development of proteomics and high-throughput technologies, has produced a large amount of protein-protein interaction (PPI) data, which provides a foundation for further understanding the interactions between proteins and the biomedical mechanism of complex diseases. In an organism, a protein-protein interaction network (PIN) consists of all the proteins and their interactions. Most of the traditional studies on PINs are based on static networks. However, due to the dynamics of protein expressions and the dynamics of PPIs, the real PINs change with time and conditions. Protein function modules related with the occurrence and development of diseases are also bound with this dynamic change. Researchers have shifted their attentions from the static properties to dynamic properties, and proposed a series of methods for the construction of dynamic PINs. This paper is to review the construction, analysis and applications of dynamic PINs. Firstly, the existing dynamic PIN construction methods are discussed in three categories: the methods based on dynamic protein expressions, the methods based on multi-state expression and correlation changes and the methods based on spatial-temporal dynamic changes. The first category embodies the protein dynamic expression varying with time; the second category reflects the changes in the expression-related relationship between proteins under different conditions; while the third category describes the dynamic of proteins and the interactions in time and space. Then, the dynamic analysis of the proteins and the related subnetworks of the dynamic PINs are reviewed. Furthermore，the main applications in the complex diseases of dynamic PINs are discussed in details, such as the identification of protein complexes/functional modules, the detection of biomarkers, and the prediction of disease genes, etc. Finally, the challenges and future research directions of dynamic PINs are discussed.

Key words: protein-protein interaction network, dynamic, gene expression, protein complexes, complex diseases