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John Tsang, Ph.D.
Building 4, Room 128D
4 Memorial Drive
Bethesda, MD 20892-0421
Phone: 301-496-0304
Fax: 301-496-0222

Laboratory of Systems Biology

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John Tsang, Ph.D.

Photo of John Tsang, Ph.D.

Chief, Systems Genomics and Bioinformatics Unit, LSB
Head of Computational Systems Biology, Trans-NIH Center for Human Immunology (CHI)

Major Areas of Research

  • Systems immunology
  • Integrative genomics: Computational approaches to integrate diverse data types to obtain novel biological insights; biological circuit reconstruction
  • The functions of phenotypic heterogeneity and stochastic gene expression of innate immune cells
  • The systems biology of host-microbiome interactions
  • MicroRNA functions and networks and their roles in imparting phenotypic robustness
  • Statistical genetics

Program Description

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.]).

Fellowship and Training Opportunities

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.

Research Group

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)

Selected Publications

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.

Project Highlights

  1. Innate immune cells such as macrophages are notoriously heterogeneous across tissues and even within individual tissues. What underlies macrophage plasticity and heterogeneity? What is the function of this plasticity? How do macrophages respond to combinatorial stimuli? Can responses to complex stimuli (e.g., multiple types of ligands) be predicted and understood based on responses to constituent stimulus of complex inputs? The goal is to analyze the spectrum of macrophage responses at the population and single-cell levels and dissect the topology and function of circuits, including the roles of microRNAs, that regulate cellular heterogeneity and plasticity in macrophages.

  2. Trillions of microbes (collectively called the microbiota) dwell on epithelial surfaces of the human body. They provide essential metabolic, immune, and homeostatic functions (among many others). Host-microbiota interactions, especially those involving the host immune system, are complex and not well understood. We are developing integrative genomics approaches to infer and understand the function and topology of host-microbiota interaction networks (e.g., host genes and networks that shape gut microbiota composition and function and vice versa). The tools we are developing include deep 16s and metagenomic sequencing using the Illumina Hiseq and computational methods that integrate gene expression, community profiling, and immune phenotyping data.

  3. In collaboration with CHI and NIH colleagues, we are analyzing the genetic, molecular, and network signatures and drivers of human immune responses to seasonal and H1N1 flu vaccination. One of the main challenges is the development of novel approaches to take advantage of the multiple types of large-scale data sets we have generated to measure the state of the human immune system at multiple time-points before and after vaccination. A key goal is to understand what factors (e.g., genes and interactions among immune parameters) drive response variation in the population. Another goal is to use the vaccination as a perturbation to infer the “wiring” of the human immune system.

  4. We maintain a strong interest in microRNA functions and networks. We have integrated genomics and expression data to infer network motifs and their functions involving microRNAs (Tsang J et al.); developed a robust computational tool (by simultaneously accounting for multiple systematic biases) to predict microRNA functions and co-targeting networks (Tsang JS et al.); and inferred genetic variants that drive microRNA expression variation and utilized these variations to predict microRNA functions in human and mouse (to be submitted and Zhao E et al.). We are further developing these ideas and tools and are applying them to analyze microRNA functions in the immune system and beyond.
MicroRNA co-targeting network inferred by mirBridge
MicroRNA co-targeting network inferred by mirBridge. A majority of microRNAs co-target with a few “hub” microRNAs (yellow nodes on the left). Clique-like structures (some containing micro RNAs with known shared functions) are apparent (above), suggesting that multiple microRNAs tend to work together to perform specific functions.
Circuits showing microRNA, target, and common regulator. See description below
m=microRNA; T=target; U=common regulator By integrating genomics and expression data, we inferred that tightly coupled circuits involving microRNA and their target(s) are highly prevalent in the human and mouse genomes. The prevalence of “incoherent” circuits is especially intriguing and suggests that microRNAs likely play homeostatic, noise-buffering, and fine-tuning roles in diverse contexts.

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Last Updated June 03, 2013

Last Reviewed February 15, 2013