While initiating my interdisciplinary undergraduate studies in the MSSC BSc program run by the Statistics Department at SFU, I decided to prepare for graduate training in bioinformatics. At the time, SFU did not offer an undergraduate program that integrated the key bioinformatics disciplines: molecular biology, computer science, and statistics. This lead to an initiative to tailor my undergraduate MSSC program for training in bioinformatics and it was this revised program that I undertook and completed in 2003.
The importance of statistics for biological studies was highlighted during my graduate training research. The analysis of a biological problem often requires application of one or more statistical methods to explain the significance of and/or model the phenomena encapsulated in the data. For example, during my PhD work I incorporated statistical methods in algorithms to assess protein sequence homology, detect differentially expressed genes, and predict transcription factor (TF) cooperativity networks. I am currently pursuing a postdoctoral fellowship at McGill University where I am experimentally validating a TF cooperativity model (developed during my PhD) for genes expressed in oligodendrocytes. Dysregulation of the oligodendrocyte gene set is implicated in diseases such as multiple sclerosis. During this research I will be employing statistical approaches for establishing experimental designs, conducting hypotheses testing in mice, and establishing model revisions. Deciphering the DNA-binding TFs and cooperative mechanisms responsible for regulation of these oligodendrocyte genes is necessary for determining potential therapeutic strategies. The application of statistical analysis methods is a fundamental component of this discovery process.