人类重大疾病的发生和发展都与蛋白质的动态变化过程密切相关，在动态蛋白质相互作用(Protein-protein Interaction, PPI)网络中识别蛋白质复合物非常关键。.本项目针对蛋白质网络的动态性，基于时间序列的基因表达数据，构建动态PPI网络；采用果蝇优化机理、基于拓扑势场的马尔科夫聚类、商空间下的粒计算等理论方法来提出动态蛋白质网络复合物挖掘算法，以期提高复合物识别的正确率，进而利用群智能优化机理对模型及参数进行优化，设计算法的计算流程及参数选取依据；建立细胞正常生理学状态和特定疾病状态下的PPI网络，分析蛋白质复合物动态变化的机制与其功能的内在关系，寻找致病蛋白质以及这些动态变化导致人类疾病的深层原因。.此方面的研究可以帮助我们预测蛋白质功能，寻找疾病分子标记和药物靶标，为相关疾病的诊治提供实验基础和理论依据。

The occurrence and development of complex human diseases are closely related to the protein dynamics. Therefore, it is appealing to identify protein complexes from dynamic protein-protein interaction (PPI) networks. .Based on the dynamic characteristics of protein networks, we are going to construct the dynamic PPI networks based on the time series gene expression data. In order to improve the correct rate of identification, we will propose efficient algorithms for identifying protein complexes in dynamic networks by using the fruit fly optimization mechanism, MCL method combined with topological potential field, granular computing in quotient space, and so on. Furthermore, we intend to employ swarm intelligent optimization mechanism to optimize the models and their parameters. In particular, we are going to design the calculation steps and parameter selection principle. Finally we will establish the PPI networks of normal cell physiological state and a particular disease state, analyze the inherent relationship between the mechanism of dynamic change of protein complexes and its function, and look for the disease-related proteins and the deep rationales that these dynamic changes lead to the human diseases. .This project can help predict protein functions, molecular disease markers and drug targets while providing experimental and theoretical fundamentals for the diagnosis and treatment of complex diseases.