Department of Internal Medicine

Bioinformatics and Computational Biology Faculty


Kai Tan

Education:
Ph.D., Washington University,
St. Louis
BA., Beloit College, Wisconsin

Postdoctoral Scholar:
University of California,
San Diego

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

Research Overview

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:

  1. 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.
  2. Understand the interaction between transcription factor binding and chromatin modifications and its effect on gene expression during hematopoietic stem cell fate specification.
  3. 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

Recent Publications

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.

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