Dynamic tracking of functional modules in massive biological data sets

Functional modules are an important aspect of living cells and are made up of proteins that participate in a particular cellular process while they may not be directly interacting with each other at all times. In recent years, while most researchers have focused on detecting functional modules from static proteinprotein interaction (PPI) networks where the networks are treated as static graphs derived from aggregated data across all available experiments or from a single snapshot at a particular time, temporal nature of genomic and proteomic data has been realized by researchers. Recently, the analysis of dynamic networks has been a hot topic in data mining. Dynamic networks are structures with objects and links between the objects that vary in time. Temporary information in dynamic networks can be used to reveal many important phenomena such as bursts of activities in social networks and evolution of functional modules in protein interaction networks. In this talk, I will present our computational approaches to identify the roles of functional modules and to track the patterns of modules in dynamic biological networks. Significant modules which are correlated to observable biological processes can be identified, for example, those functional modules which form and progress across different stages of a cancer. Through identifying these functional modules in the progression process, we are able to detect the critical groups of proteins that are responsible for the transition of different cancer stages. Our approaches will discover how the strength of each detected modules changes over the entire observation period. I will also demonstrate the application of our approach in a variety of biomedical applications.