Wang Lab

Wang Lab includes a computational lab and an experimental lab. Traditionally Wang Lab was mainly working on computational systems biology, machine learning and AI. Since 2016, Wang Lab has shifted toward conducting both computational and experimental systems biology in cancer and immunology.

The computational work includes: (1) big medical data analysis (2) machine learning, deep learning and artificial intelligence (3) AI-based predictive model construction. We are developing novel AI algorithms (machine learning, deep learning) for modeling of molecular networks and cancer biomarker discovery, and also developing new concepts for data analysis toward interpreting data, generating, prioritizing and testing new hypotheses.

The wet-lab work includes: (1) single-cell RNA-seq, single-cell ATAC-seq and single-cell DNA-seq analysis of tumor and immune cells (2) functional genomics such as CRISPR technologies of cancer immunotherapy (3) mouse model studies for cancer immunotherapy.

Examples of the computationally driven work:

eTumor (electronic tumor) projects for precision medicine

A collection of projects which aim to develop algorithms and computational tools (based on the cancer hallmark network framework) for predicting drug targets, metastasis and drug resistance for individual subclones within a tumor based on the tumor's exome-sequencing data.

Germline genomics of cancer

We showed that germline pathogenic variants can influence immune capacity in cancer patients at a population level by (1) shaping tumor somatic mutations and antigen-presentation capacity or (2) influencing NK cell’s function to modulate lymphocyte infiltration in the tumor microenvironment. These results highlight the indispensable roles of germline genome in immunity and cancer development.

iHeathcare Systems (intelligent/personalized Healthcare Systems)

The long-term goal of this project is to timely monitor people's health, warn disease and provide information for clinicians and individuals for prevention, diagnosis and treatment by constructing predictive models using the data of host genomics, epigenetics and oral/gut microbiome (linking to diet) and other information which represent life style information and physiological data collected by smart devices such as iPhone and iWatch.

Examples of the experimentally driven work:

Single-cell sequencing for tumor subclonal network evolution and immune system

We are applying cutting-edge technologies to profile tumor subclonal gene regulatory networks, and investigate network evolution at the subclonal level under immune pressure.

Convert cold tumors into hot tumors for improving immunotherapy

We have developed methods predicting key tumor genes which are responding to TILs and cold tumors. We are using mouse models to examine drug targets for converting cold tumors into hot tumors for improving immunotherapy.

EDITORIAL BOARDS

PLoS Computational Biology, Website

Scientific Reports, Website

BMC Systems Biology, Website

Genomics, Proteomics and Bioinformatics, Website

Animal Cells and Systems, Website

Annals of Translational Medicine, Website

MORE INFORMATION: Lab News //// Publications

About Edwin

Edwin has a undergraduate training in Computer Science and a PhD training in Molecular Genetics (UBC - University of British Columbia, 2002). After one-year postdoc training at FlyBase, a genome database of fly, he moved to NRC as a PI. In 2016, he became an AISH Chair Professor at University of Calgary. His pioneering work of cancer network motifs has been featured in the college textbook, GENETICS (2014/2017) written by a Nobel Laureate, Dr. Hartwell and the father of systems biology, Dr. Hood. His pioneering work of microRNA of signaling networks opens the new research area: network biology of non-coding RNAs.