John Tsang, Ph.D.Building 4, Room 128D 4 Memorial DriveBethesda, MD 20892-0421Phone: 301-496-0304Fax: email@example.com
Chief, Systems Genomics and Bioinformatics Unit, LSBHead of Computational Systems Biology, Trans-NIH Center for Human Immunology (CHI)
Complex interactions among biomolecules (e.g., proteins, DNA, and RNA) and cells maintain homeostasis and orchestrate responses to perturbations in biological systems. Understanding the connectivity, topology, and workings of these interactions is a key toward predictive intervention and prevention of human pathologies. Toward this end, we are interested in developing and applying computational and experimental genomics approaches to dissect the topology, function, and operational and design principles of molecular and cellular circuits (e.g., circuits involving interactions among genes, cells, proteins, microRNAs, secreted cytokines). Our particular emphasis is on circuits that underlie 1) the programming and plasticity of innate immune cells such as macrophages, 2) host-microbiota interactions, and 3) human immune responses (e.g., post-vaccination and infection) and inflammation (e.g., those associated with diseases such as obesity, atherosclerosis, and cancer)—can we utilize time- and space-resolved large-scale data sets to infer and better understand how immune response and chronic inflammation are orchestrated in humans?
We apply perturbations to or utilize natural variations in cells and systems, measure their effects broadly (e.g., genome-wide gene expression, abundances of cell populations), computationally integrate the data to infer the connectivity and relationship among molecules and cells, and then analyze the function, collective properties, and design principles of these systems and circuits. Experimentally, we use tools such as next-generation sequencing (Illumina Hiseq), flow cytometry, and quantitative PCR. Computationally, we employ or develop approaches motivated by (or borrow verbatim from) multivariate statistics, Bayesian network inference, linear models, graph algorithms, and stochastic modeling. We also aim to develop broadly applicable tools when it is apparent that they can be applied in diverse settings (e.g., our development of mirBridge to infer microRNA functions and co-targeting using gene sets [Tsang JS et al.]).
Postdoctoral/predoctoral fellowships and staff positions are available. Motivated individuals with backgrounds in computational biology, genomics, experimental biology (e.g., immunology, molecular biology), and bioinformatics are welcome to inquire.
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Dr. Tsang received his Ph.D. in biophysics from Harvard University and B.A.Sc. and M.Math. in computer engineering and computer science, respectively, from the University of Waterloo in Canada.
Dr. Tsang has been working on systems biology and genomics research in both academic and industrial settings for over 10 years. After graduating from Waterloo in 2000, he helped pioneer high-throughput computational and experimental methods to annotate the then-freshly sequenced human genome using custom DNA microarrays at Rosetta Inpharmatics and then led a bioinformatics group at Caprion Proteomics. During his Ph.D., he rotated in several laboratories at Harvard and Massachusetts Institute of Technology (MIT) before joining Alexander van Oudenaarden’s laboratory at MIT to work on the systems biology of microRNAs and stochastic gene expression in yeast. After earning his Ph.D. in 2008, he returned to Rosetta/Merck Research Laboratories to work with Dr. Eric Schadt on integrative genomics and genetics of gene expression in human and mouse.
He started his own lab at the National Institutes of Health in August 2010, where he has been leading a research program to develop and apply computational and experimental approaches to tackle problems in immunology (i.e., “systems immunology”). He was also jointly appointed as the head of computational systems biology at the Trans-NIH Center for Human Immunology (CHI), where he recruited and now leads a group of computational biologists to integrate and analyze large-scale data sets (e.g., genotypes, gene expression, flow cytometry) to dissect the human immune system in health and disease.
Mani Narayanan (Ph.D., Computational Genomics, UC-Berkeley, United States)Andrew Martins (Ph.D., Immunology, University of Western Ontario, Canada)Zhao Yang (Ph.D., Molecular Biology, McGill University, Canada)Hui Cheng (Ph.D., Bioinformatics, Virginia Tech, United States)
Center for Human Immunology, Autoimmunity, & Inflammation (CHI)Foo Cheung (Ph.D., Molecular Biology, University of Edinburgh, United Kingdom)Yuri Kotliarov (Ph.D., Engineering Chemistry, University of Tokyo, Japan)Zhi Xie (Ph.D., Bioinformatics, Lincoln University, New Zealand)
Mukherji S, Ebert MS, Zheng GX, Tsang JS, Sharp PA, van Oudenaarden A. MicroRNAS can generate thresholds in target gene expression. Nat Genet. 2011 Aug 21;10.1038/ng.905 [Epub].
Fraser HB, Babak T, Tsang J, Zhou Y, Zhang B, Mehrabian M, Schadt EE. Systematic detection of polygenic cis-regulatory evolution. PLoS Genet. 2011 Mar;7(3):e1002023.
Tsang JS*, Ebert MS, van Oudenaarden A. Genome-wide dissection of microRNA functions and cotargeting networks using gene set signatures. Mol Cell. 2010 Apr 9;38(1):140-53. *corresponding author
Zhao E, Keller MP, Rabaglia ME, Oler AT, Stapleton DS, Schueler KL, Neto EC, Moon JY, Wang P, Wang IM, Lum PY, Ivanovska I, Cleary M, Greenawalt D, Tsang J, Choi YJ, Kleinhanz R, Shang J, Zhou YP, Howard AD, Zhang BB, Kendziorski C, Thornberry NA, Yandell BS, Schadt EE, Attie AD. Obesity and genetics regulate microRNAs in islets, liver, and adipose of diabetic mice. Mamm Genome. 2009 Aug;20(8):476-85.
Tsang J, Zhu J, van Oudenaarden A. MicroRNA-mediated feedback and feedforward loops are recurrent network motifs in mammals. Mol Cell. 2007 Jun 8; 26(5):753-67.
Tsang J, van Oudenaarden A. Exciting fluctuations: monitoring competence induction dynamics at the single-cell level. Mol Syst Biol. 2006;2:2006.0025.
Last Updated June 03, 2013
Last Reviewed February 15, 2013