Our lab is interested in Systems Biology of gene regulation. Gene expression variations play a major role in driving phenotypic variations. Because gene expression can be modulated at multiple levels, it is a challenging task to study gene regulatory systems. The advent of various functional genomics technologies increasingly allows us to interrogate the status of a cell’s components and to determine how, when, and where these molecules interact with each other. On the other hand, the availability of a large number of sequenced genomes has enabled powerful comparative approaches to study a variety of biological questions. Our research takes advantage of the strength of Systems Biology and Comparative Genomics to understand gene regulation.

We focus our effort on understanding gene regulatory networks in normal and disease development. These efforts will help us better understand how the different processes controlling gene expression are coordinated in the cell and deepen our knowledge of organismal development and disease processes. Towards this goal, we are conducting interdisciplinary research, combining wet-lab experiments and computation along the following two lines:

Model gene regulatory networks in development and disease

We are studying gene regulatory networks controlling development and differentiation of hematopoietic stem cells and lymphocytes and oncogenesis using omics assays and computational modeling. In this biological context, we are pursuing the following projects:

i) Identify transcriptional enhancers that control stage-specific gene expression. We are developing computational tools to predict enhancers. We are also developing a high through-put assay to validate our computational predictions.

ii) Understand the interaction between transcription factor binding and chromatin modifications and its effect on gene expression during stem cell fate specification and pathogenesis.

iii) Integrate genomic and interactome data to discover gene regulatory pathways during stem cell fate specification and pathogenesis.

Discover molecular networks as biomarkers for human diseases
Molecular interaction networks are increasingly serving as tools to unravel the basis of human diseases.  We are developing network-based approaches to identifying disease-related sub-networks that can serve as biomarkers for the diagnosis and prognosis of diseases and as candidates for novel therapeutics.

We gratefully acknowledge support from the following funding agencies: