Yeast Integrative Biology Project

The long-term goal of the functional genomics component of our project is to use automated genetics, network construction and expression profiling to generate a comprehensive genetic description of the biology of model eukaryotic cells. It is abundantly clear that the basic wiring of the eukaryotic cell is conserved and that orthologous genes are universally present in organisms. Just as studies in model organisms have identified thousands of human disease genes, the development of comprehensive gene and protein interaction networks in model systems will define basic principles of genetic interaction and gene function in human cells.

With funding from previous Genome Canada rounds, we pioneered the large-scale construction of yeast double mutants by synthetic genetic array (SGA) analysis and the subsequent analysis of synthetic lethal genetic interactions to identify genes that compensate or buffer each other. We are using the protein-protein interaction network, as already established for soluble proteins and as proposed for membrane proteins, and other functional information to direct a new round of SGA analysis. This global systems-level project can be completed efficiently by screening for genetic interactions with genes in the same functional neighborhood of the query gene (e.g. DNA synthesis/repair, cell polarity, secretion). Integration of the neighborhood sub-networks will generate our first glimpse of the global genetic interaction network for a eukaryotic cell. We are also expanding our network neighborhood approach to include ‘essential’ genes i.e. those that are required for cell viability. Our 'Native Promoters' project is developing key reagent sets that will allow us to apply our automated genetic platform to query other types of genetic interactions. The 'Transcriptional Networks' project involves systematically and uniformly determining the DNA binding specificity of all known and predicted sequence-specific DNA-binding transcription factors in yeast and performing extensive computational analyses using this information.