Dr. Sushmita Roy, University of Wisconsin – Madison

Inference of regulatory network dynamics in developmental and evolutionary lineages
Regulatory networks connect regulatory proteins (e.g., transcription factors and signaling proteins) to target genes and control what genes are expressed when, translating the information encoded in an organism’s genome to context-specific responses. Identification of these networks is important to advance our understanding of many biological processes such as development, disease, response to stress, and evolution.  In this talk I will present computational methods to tackle a few key problems in the inference of gene regulatory networks. The first part of the talk will focus on computational problems in mammalian regulatory networks. Understanding mammalian regulatory networks is a major challenge because of the number of regulators, large amounts of non-coding regulatory DNA, and multiple levels of regulation including chromatin state (determining what DNA is accessible by a transcription factor), and the three-dimensional organization of the genome (how DNA is packaged in the nucleus). I will present computational methods to tackle three problems in mammalian gene regulatory systems: (i) Integrative inference of genome-scale regulatory network inference, (ii) Chromatin state dynamics on cell lineages, (iii) Long-range gene regulation. Using these approaches we have derived useful insights about mammalian gene regulation including the identification of key regulators in host response and chromatin state dynamics during cell state transitions.  In the second part of the talk I will present computational methods to study gene regulatory programs in evolutionary lineages. Using our approaches, we are studying the evolution of gene regulatory processes in yeast, plants and vertebrate phylogenies.

Sushmita Roy’s research focuses on developing statistical computational methods to identify the networks driving cellular functions by integrating different types of genome-wide datasets, that measure different aspects of the cellular state.
Roy is interested in identifying networks under different environmental, developmental and evolutionary contexts, comparing these networks across contexts, and constructing predictive models from these networks.

Specifically, some research topics of interest are:

Inference of structure and function of regulatory networks
Comparative analysis of expression modules across species
Evolution of gene regulation
Relational learning to predict function
Modeling condition-specific functional behavior
Learning causal networks
Predictive models of phenotypic response