Bioinformatics Research in France



Daniel Hogan, Bioinformatics PhD student

Daniel Hogan, a Bioinformatics PhD student at the University of Saskatchewan, has the opportunity to study abroad in France where he will collaborate with fellow researchers in his field. Daniel received a Mitacs Globalink Research Award, a program that supports research collaborations between Canada and select partner countries.

While in France, Daniel will be working under the supervision of professors Dr. François Meurens (Oniris Nantes, France) and Dr. Tony Kusalik (U of S) to develop a tool researchers can use to predict epitopes in pathogens that afflict livestock. He will also be working with Dr. François Bagaini (Sochaux, France), developer of an earlier tool, to improve and generalize an existing approach for predicting epitopes for cattle.

Research Project

Each day we encounter a host of microscopic organisms that would prove fatal were it not for the cells of the immune system guarding our tissue. These immune cells are remarkably effective at protecting us from disease, a task that entails discerning pathogens from healthy tissue. Discerning these threats is achieved by the binding of a cellular receptor to molecular components exclusive to pathogen proteins. These molecular components are called epitopes, and knowledge of them is important for the development of diagnostics and vaccines, tools that are used to control and prevent disease.

Finding epitopes experimentally is expensive and not always successful, so predictive models have been devised to complement these experimental approaches. So far, data-driven models have been the most successful, but are only as effective as the quantity and scale of the data upon which they're trained. Animal disease, which presents an economic as well as existential threat to humans, is particularly vulnerable to this problem because much of the existing epitope data relates to human and mouse. Daniel's research involves finding suitable strategies for leveraging the abundance of human data for predicting epitopes in pathogens that infect livestock. He will achieve this using a range of techniques including protein structure prediction and machine learning.

About Daniel

After receiving his Engineering degree from the University of New Brunswick, Daniel returned to Saskatoon, Saskatchewan to look for work. He enrolled in a few Computer Science courses at the University of Saskatchewan to keep himself busy but found himself addicted to the coursework, particularly its applications to biology. He completed the bioinformatics Master's program under the supervision of Dr. Tony Kusalik and entered the PhD program last year where he found his niche studying how statistical and machine learning techniques could be used to decipher the complex behaviour of the immune system.