PhD Defense: Structure and Evolution of Biological Networks:
Structure and Evolution of Biological Networks: A Systems Biology Approach to Understanding Protein Function Andrew D. Fox
Computational systems biology aims to understand complexity in living cells through network models derived from high-throughput genomic data. Research presented spans diverse areas of the field, touching on problems of network inference, significance estimation for predicted networks, conservation in protein networks, and network-driven protein function prediction. We develop a robust, efficient and flexible web application (BNET) for evaluating predicted biological networks. The tool makes use of a powerful statistical network evaluation model and has applications across a broad range of both computational and experimental systems biology research. Transfer of systems-level information from model organisms to the human network is common in biomedical research, yet the true rate of conservation is unknown. We construct an analytic model of evolutionary conservation using protein network data in four species. Our model describes the relationship between node degree and level of conservation, and this has important implications for robust annotation transfer between species. We finally develop a novel algorithm that uses protein subnetwork structure to identify key regulators of human disease. Case studies on genome-wide diabetes and cancer profile data sets reveal that the algorithm identifies known key regulators of both cancer progression and diabetes. Our results support the thesis that network models are a valuable tools for understanding biological systems at multiple levels of abstraction.