Research summary:

We use computational and experimental approaches to study cellular differentiation and evolution. We aim to address two scientific questions: 1) how gene expression evolves as a consequence of genome sequence evolution; and 2) how genetic network regulate early cell fate decision. We study two biological processes: 1) differentiation of embryonic stem cells, and 2) mammalian preimplantation development. We generate genomic data, develop probabilistic models, use computational inference and experimental validation to understand how gene expression is regulated and how such regulatory mechanisms evolve. 

Individual projects:
 

Identification of transcriptional networks in high eukaryote organisms. The control of gene transcription is a crucial component in regulating many important biological processes. For example, in the early stages of development, cell fate decisions and differentiation programs are often controlled by the expression of key transcription factor and receptor molecules whose presence or absence help to specify the cell fate, or to activate or suppress a particular differentiation pathway. We are interested in identifying the active transcription factors and their DNA binding sites in certain biological processes. Especially we are generating genomic data and developing computational methodologies to:

(1) identify long range enhancers
(2) model the cooperation of multiple transcription factors
(3) identify critical transcriptional regulators for cell differentiation

Identification signaling and regulatory pathways that regulate cells' response to environmental stimuli. We develop novel comparative genomic methods on gene expression data and aim to discover essential regulatory pathways for fundamental biological processes such as differentiation and aging.

Inferring gene functions through the use of gene expression data, ChIP-chip data, epigenetic modification data together with prior knowledge on singling pathways, Gene Ontology and protein domains. We work on statistical models and machine learning methods that jointly utilize genomics data and prior functional knowledge to infer gene functions, protein-DNA and protein-protein interactions.

Partial list of collaboration projects and collaborators

• Transcription network in embryonic stem cells, collaborating with Huck Hui Ng, Fei Wang, Tetsuya Tanaka.

• Preimplantation development, collaborating with Harris Lewin, Chad Cowan.

• Regulation of Hox genes, collaborating with Robb Krumlauf.

• Cyclic genes and somitogenesis, collaborating with Olivier Pourquie.

• Genomics of behavioral maturation, collaborating with Gene Robinson.

• Evolution of cis-regulatory elements, collaborating with H. Rex Gaskins.

• Gene copy number change and colon cancer, collaborating with Hanlee Ji and Ronald Davis

• Genomics of life history evolution in Drosophila, collaborating with Kimberly Hughes
   

Research support from

NSF

National Center for Supercomputing Applications (NSCA)

Illinois Regenerative Medicine Institute (IRMI)

Carle Foundation Translational Research Program

University of Illinois Research Board

Shanghai Committee of Science and Technology, China