Computational models in systems biology

While much of molecular biology research has led to a wealth of knowledge about individual cellular components and their functions, it has become increasingly clear that most cellular functions are carried out by complex networks of interconnected components, and that the characterization of isolated cellular components is not sufficient to understand the cell's complexity. In recent years, the development of high-throughput technologies has provided the scientific community with exciting new opportunities for systematically studying biological networks on a whole-genome scale. One of the great challenges currently confronting scientists in systems biology research is how to computationally model and elucidate the function and the mechanisms of the complex biological networks from these high-throughput biological data sets. In this talk, I will discuss some machine learning methods recently developed in my group for uncovering genes involved in the same pathways and for predicting protein-protein interactions and protein functions.