Health care spending in Canada reached $183.1 billion in 2009, according to the Canadian Institute for Health Information, an increase of 5.5% over 2008. Seniors make up over 13% of Canada’s population and this number is forecasted to grow to 27% by 2056 according to Statistics Canada. The World Health Organization reported that nearly 89% of all deaths in Canada are a result of chronic conditions such as Diabetes, respiratory disease, cancer and cardiovascular disease. Research aimed at modeling the development of disease, uncovering the role that genes play in health, predicting and containing the spread of disease and controlling the variability of patient compliance to medication regimens is critical to the long-term viability of Canada’s healthcare system.
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Dr. Leon Glass, McGill University & Dr. Edward Vigmond , University of Calgary
This team uses mathematics to further the understanding of cardiac arrhythmias and to develop new methods to predict which patients are at risk for arrhythmias and methods for their control. In the past year systems were developed to analyze changes in the electrical properties of heart tissue.
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Dr. Shelley Bull, University of Toronto
Using statistical modelling and analysis, this team aims to discover and characterize genes that influence susceptibility to disease and lead to a better understanding of how genes function, resulting in the development of new approaches to the diagnosis and treatment of common diseases.
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Dr. Jianhong Wu , York University
Assembled at the height of the SARS outbreak in 2003, this project develops a variety of modeling templates designed to fit different types of disease dynamics and management.
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Dr. Jiri Patera, Université de Montréal
This team worked to develop a framework in 3D using lie algebra that will enable the combination of MRI, Optical and EEG imaging techniques to provide doctors with a comprehensive image of the human brain.
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[url=mailto:mkaern@me.com]Dr. Mads Kaern[/url] , University of Ottawa
The goal of the MITACS-funded research program on reverse-engineering cellular complexity is to develop new mathematical tools and algorithms for analyzing genetic switching networks.
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