Our lab has several projects related to the inference of biological networks. We have developed approaches that perturb specific sets of genes or transcripts using genome editing (eg CRISPR) in a high-throughput, multivariate pseudo-random manner. The edits can be knockouts, knock-downs or over-expression of the targets, or a combination thereof. Single cell high-throughput profiling of transcription, select proteins, or chromosomal conformations are used to deduce underlying biological networks - the regulatory and correlative relationships between genes and gene products.

These projects bring together all the components of our lab:

  • Single cell multi-modal -omic profiling;
  • Genome editing;
  • Next generation sequencing;
  • Data science and analysis of high-throughput data; and
  • Deep learning for inference of biological networks.

Sanny Khurdia, Aki Kirbizakis and Van Bettauer (computational biology) are leads on these projects. We are also lucky to have Aaybod assisting as part of his genomics diploma.

We apply this to different biological systems including in the context of breast cancer with Sylvie Mader’s group at the IRIC/UdeM.