Department of Internal Medicine

Administrative Faculty and Staff

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Kai Tan, Ph.D.
Assistant Professor
Departments of Internal Medicine and Biomedical Engineering

Research Overview

Our lab is interested in Systems Biology of gene regulation.  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 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.  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:

1. 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.
2. Modeling the combinatorial effects of transcription factor binding, nucleosome occupancy, and chromatin modifications on gene expression.
3. Integrating multiple types of interactome data, such as protein-protein interactions, protein-DNA interactions (i.e. binding of a regulatory protein to its cis-regulatory sequences), and genetic interactions to discover novel gene 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.

Honors, Awards, and Organizations

• Teresina Peck Rowell Scholarship, Beloit College (1995-97)
• Magna Cum Laude, Departmental Honor in Biochemistry, Beloit College (1997)
• Predoctoral Fellowship, Washington University in St. Louis (1998-2000)
• Travel Fellowship, The 8th conference on Intelligent Systems in Molecular Biology (2000)
• IBM postdoctoral fellowship (2004-2006)
• Travel Fellowship, The 16th conference on Intelligent Systems in Molecular Biology (2008)

Recent Publications

1. Tan K, Tegner J, and Ravasi T. Integrated approaches to uncover transcriptional regulatory networks in mammalian cells. Genomics. 91(3): 219-231, 2008.
2. Tan K, Feizi H, Luo C, Fan S, Ravasi T, and Ideker T. A systems approach to delineate functions of paralogous transcription factors: Role of the Yap family in the DNA damage response. Proc. Natl. Acad. Sci. 105(8):2934-2939, 2008.
3. Tan K, Shlomi T, Feizi H, Ideker T, and Sharan R. Transcriptional regulation of protein complexes within and across species. Proc. Natl. Acad. Sci. 104(4):1283-1288, 2007.
4. Tan K, McCue LA, and Stormo GD. Making connections between novel transcription factors and their DNA motifs. Genome Research. 15(2):312-320, 2005.
5. Liu JJ, Tan K, and Stormo GD. Computational identification of the Spo0A-phosphate regulon that is essential for the cellular differentiation and development in Gram-positive spore forming bacteria. Nucleic Acids Research. 31(23):6891-6903, 2003.
6. Stormo GD and Tan K. Mining Genome databases to identify and understand new gene regulatory systems. Current Opinion in Microbiology. 5:149-153, 2002.
7. Tan K, Moreno-Hagelsieb G, Cooado-Vides J, Stormo, GD. A comparative genomics approach to prediction of new members of regulons. Genome Research. 11(4): 566-584, 2001.

Book Chapters

1. 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.
2. Tan K, Ideker T.; Protein interaction networks in Biological networks. World Scientific, New Jersey. 2007.

Links of Interest

• Kai Tan's laboratory
• University of Iowa College of Engineering

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