Trajectory analysis — Linking genomic and proteomic data with disease progression

Abstract

Biological networks are dynamic and modular. Identifying dynamic functional modules is key to elucidating biological insight and disease mechanism. In recent years, while most researchers have focused on detecting functional modules from static protein-protein 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 context-specific transcriptomic and proteomic data has been recognized by researchers. Meanwhile, the analysis of dynamic networks has been a hot topic in data mining and social networks. 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 address several critical challenges to construct robust, dynamic gene interaction networks, and present our computational approaches to identify disease-relevant functional modules and to track the progression patterns of modules in dynamic biological networks. Significant modules which are correlated to phenotypes of interest 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 can also 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.