Research Overview
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. By combining the strength of Systems Biology and Comparative Genomics, our research focuses on developing a novel Comparative Systems Biology approach to study gene regulation.
Within this broadly-defined research area, we focus most of our effort on understanding gene regulatory networks and molecular pathways that give rise to 1) stem cell phenotype (self-renewal and pluripotency); and 2) human diseases. The first project is aimed mostly at reconstructing the network and the second project is focused on using molecular interaction networks to link genotypes with disease phenotypes. 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 both computational and experimental research.
Gene regulatory network
We are developing novel tools to model gene regulatory networks at multiple level, including signal transduction, transcriptional regulation, and epigenetic regulation. Specifically, we are working on the following projects:
i) Identifying transcriptional enhancers that control cell-type/tissue-specific gene expression. This is a combined computational and experimental project. We are developing tools to predict tissue-specific enhancers as well as high through-put assay to validate our computational predictions and to generate new input data to train our computational methods.
ii) Modeling the combinatorial effects of transcription factor binding, nucleosome occupancy, and chromatin modifications on gene expression.
iii) Integrating multiple types of interactome data, including protein-protein interactions, protein-DNA interactions (i.e. binding of a regulatory protein to its cis-regulatory sequences) to discover novel regulatory pathways.
Molecular network in human diseases
Molecular interaction networks are increasingly serving as powerful tools to unravel the basis of human diseases. We are developing network-based approaches to identifying new disease genes and disease-related sub-networks. We are particularly interested in cancers and metabolic diseases.
