1. Network Medicine

The aim is to understand how drug and disease function, via the human protein-protein interactome. Drug and diseases are represented by their corresponding target proteins or disease-associated proteins, and their topological patterns on the protein interactome can indicate their functions. By deciphing these networkpatterns, one can predict drug-disease treatment, disease development, and drug-drug interaction, etc.

2. Modeling signal transduction networks in biology

The aim is to understand how biological complex sytem function as a whole, i.e. how low-level molecular interactions, chemical reactions, etc. intergrate into a high-level biological system, in order to perform a function. One can construct a network and a corresponding dynamic model to reflect a biologicl signal transduction process. The dynamic repertoire and behavior of the model can indicate the biological systems functions. We develop network-based dynamic models that reflect biological signal transduction processes, and we develop the mathematical formalisms as well as algorithms to understand the dynamic repertoire of these models. In this way, we show insight about signl transduction, predict unknown signaling elements, and predict how to control the biological process.