Most proteins achieve a particular function by interacting with other proteins. It has been generally recognized that interactions between proteins affect many biological processes and play a fundamental role in many diseases. Systematic identification of protein-protein interaction (PPI) networks within a living cell has significant applications and implications in life science since it may lead, for example, to a better understanding of the mechanisms of protein function and cellular processes, and the design of new, effective therapeutic approaches.
The recognized significance of PPI has inspired huge efforts to map interaction networks on a proteome-wide scale in different species. These PPI networks are commonly modelled as graphs with nodes corresponding to proteins and edges corresponding tointeractions.
This proposal aims to apply graph theory approaches to the analysis of protein interaction networks. A new graph model will be proposed to represent a protein. Different graph theoretic properties of proteins associated with different functional groups will be studied. A new graph clustering which employs graph-based similarity measures will be developed to extract functional modules from PPI networks
First Supervisor: Wang, HY Dr
Second Supervisor: Zheng, H Dr
Third Supervisor: Wang, H Dr
Collaboration: This project does not involve collaboration with another establishment