We are a computational biology group interested in developing statistical computational methods to understand regulatory networks driving cellular functions. We are interested in identifying networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and constructing predictive models from these networks. This can help us understand (1) how environmental information is processed in cells to mount appropriate condition-specific responses, (2) how these networks change across different contexts such as environmental stresses, cell-types, tissues, diseases, and, (3) how these networks have evolved to suit organism life-style and habitat. Most important, by comparing such networks across many different contexts, we can identify the general organizational principles as well as notable exceptions that underlie condition-specific and organism-specific behavior.