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