Department of Internal Medicine
Bioinformatics and Computational Biology Faculty
Kai Tan, Ph.D.
Departments of Internal Medicine and Biomedical Engineering
Research in our lab is directed towards understanding gene regulatory networks in normal and disease development. In particular, we would like to gain mechanistic understanding of how genetic and epigenetic factors interact to control gene expression. 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 overall goal, we are conducting interdisciplinary research that combines wet-lab experiments and computation. Within the broad area of gene regulation, we focus our effort on understanding gene regulatory networks that control: 1) stem cell phenotype (fate specification); and 2) disease development.
Model gene regulatory networks in development
We are studying gene networks controlling hematopoietic stem cell fate using functional genomic assays and computational modeling. In this biological context, we are pursuing the following projects:
- Identify transcriptional enhancers that control developmental-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.
- Understand the interaction between transcription factor binding and chromatin modifications and its effect on gene expression during hematopoietic stem cell fate specification.
- Integrate genomic and interactome data to discover gene regulatory pathways during hematopoietic stem cell fate specification.
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.
Links of Interest
Honors, Awards, and Organizations
- Teresina Peck Rowell Scholarship (1995-1997)
- Magna Cum Laude, Departmental Honor in Biochemistry(1997)
- IBM postdoctoral fellowship (2004-2006)
- NSF Computing Innovation Fellows Award (2009)
- PhRMA Foundation Junior Faculty Research Starter Award (2009)
- March of Dime Basil O’Connor Research Award (2011)\
- Sigma Xi
- International Society for Computational Biology
- International Society for Systems Biology
- Internal Society for Stem Cell Research
- Li Teng, Firpi H and Tan K. 2011. Enhancers in embryonic stem cells are enriched for transposable elements and genetic variations associated with cancers. Nucleic Acids Res. doi:10.103/nar/gkr476
- Ucar D, Hu Q and Tan K. 2011. Combinatorial chromatin modification patterns in the human genome revealed by subspace clustering. Nucleic Acids Res. 39:4063-75.
- Kuo D, Licon K, Bandyopadhyay S, Chuang R, Luo C, Catalana J, Ravasi T, Tan K*, and Ideker T*. 2010. Co-evolution within a transcriptional network by compensatory trans and cis mutations. Genome Res. 20:1672-8.
- Kim J. and Tan K. 2010. Discover protein complexes in protein-protein interaction networks using parametric local modularity. BMC Bioinformatics. 11(1):521.
- Kuo D*, Tan K*, Zinman G, Ravasi T, Bar-Joseph Z, and Ideker T. 2010. Evolutionary divergence in the fungal response to fluconazole revealed by soft clustering. Genome Biol. 11:R77.
- Firpi HA, Ucar D, and Tan K. 2010. Discover regulatory DNA elements using chromatin signatures and artificial neural network. Bioinformatics. 26(13):1579-86.
- Ravasi T, Cannistraci CV, Katayama S, Bajic VB, Tan K, and FANTOM consortium & Riken Omics Science Center. 2010. An atlas of combinatorial transcriptional regulation in mouse and man. Cell. 140:744-752.
- Ideker T, Tan K, and Uetz P. Visualization and integration of protein interaction networks in Protein-Protein Interactions: A molecular cloning manual. Cold Spring Harbor Laboratory Press. 2005.
- Tan K, Ideker T.; Protein interaction networks in Biological networks. World Scientific, New Jersey. 2007.