Computational Methods for Deciphering Disease Networks

A Department of Computational Biology Seminar

Featuring Dr. Mona Singh, Wang Family Professor in Computer Science, Department of Computer Science & Lewis-Sigler Institute for Integrative Genomics, Princeton University

Networks of molecular interactions underlie virtually all functions executed within a cell. Networks thus provide a powerful foundation within which to interpret a wide range of rapidly accumulating biological data.

In this talk, I will present formulations and algorithms that leverage the structure and function of biological networks in order to discern the effects of disease mutations.

First, I will introduce a framework that can rapidly integrate multiple sources of information about molecular functionality in order to discover key interactions within a network that tend to be disrupted in cancers. Crucially, our approach is based on analytical calculations that obviate the need to perform time-prohibitive permutation-based significance tests.

Second, I will discuss methods for uncovering noncoding mutations that can alter regulatory networks in cancer. Finally, I will describe an approach that leverages large compendia of DNA-binding specificities for transcription factors in order to infer protein-DNA interaction interfaces and uncover the effects of mutations within transcription factors. Overall, our work showcases the versatility and power of a network viewpoint in advancing biomedical discovery.