Utilizing natural variations and systems biology to study the human immune system

Dr. Tsang is a Principal Investigator at the intramural research program of the National Institute of Allergy and Infectious Diseases, where he leads a laboratory investigating systems immunology. He is also the founding director of computational systems biology at the Trans-NIH Center for Human Immunology (CHI), where he leads efforts to integrate and analyze of large-scale data sets to dissect the human immune system in health and disease.

His lab develops and applies systems biology approaches – combining computation, modeling and experiments – to study the immune system at both the organismal and cellular levels. At the organismal level and working together with colleagues at the CHI, his lab has been utilizing systems biology and large-scale data-driven approaches to study the human immune system using multiplexed technologies in human cohorts. The resulting multi-modal data sets are analyzed and modeled in an integrative manner to 1) uncover biomarkers of immune responsiveness and health, 2) infer connectivities among components of the immune system, and ultimately, 3) understand how immune responses are orchestrated across scales – from molecules to cells to cell-to-cell interactions in space and time. At the cellular level, his lab has been using macrophages as a model to study immune cell adaptations to the environment at both the cell-population and single-cell levels, particularly in assessing and modeling cell-to-cell heterogeneity in transcriptional responses and studying the functional consequences of cell-to-cell variations at both the network and cellular levels.

Dr. Tsang received his PhD in biophysics from Harvard University/Massachusetts Institute of Technology, M.Math and B.A.Sc in computer science and computer engineering from the University of Waterloo in Canada. Prior to joining the NIH, Dr. Tsang was a research scientist at Merck Research Laboratories’ Rosetta Inpharmatics Division, where he was integrating large-scale genetic and gene expression data to infer and assess gene networks associated with human diseases.