Grants and Contributions:

Mathematical modeling of SARS-CoV-2 life cycle and COVID-19 vaccine response
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Agreement Date:
Mar 1, 2021 - Mar 1, 2022
This Project proposes a modeling platform to quickly explore vaccine designs using simulation and Machine Learning algorithms. A recently developed base model describes the life-cycle of the virus and includes a general immune response. Extension of the model to study pathogen mutation and ‘viral fitness’ is currently underway. The main focus of the proposed work will be the development of a complementary mathematical model that rigorously describes the host immune system to elucidate the complex interactions between interferon signaling pathways and the adaptive immune response to SARS-CoV-2 infection. Collectively these models will be crucial to defining vaccine strategies, and will provide a qualitative and quantitative platform to test and guide vaccine development. Since December 2020, there has been a few new strains of the coronavirus. Namely the UK (SARS-CoV-2 VOC 202012/01) and South Africa strain (501Y.V2) are among the more prevalent. The UK strain has already spread to Canada. The current vaccine was not developed with these new strains in mind. For that matter, further mutation of the virus can be anticipated. The current distributed vaccine that was approved in the interim is a mRNA based vaccine. While this vaccine completed clinical trials overseas, it was not tested in Canada and Health Canada have an interim approval due to the emergency. There are many vaccine candidates using other technologies that are currently in development in Canada and overseas such as instance Providence Therapeutics, Medicago, and VBI. This Project proposes a modeling framework that can be applied to test new candidates and compare to those that have been approved/distributed in Canada. There are significant values in testing vaccines against new strains of the virus and optimizing current vaccine design against new strains. The Project’s mathematical model can be quickly modified to rapidly address the new challenges SARS-CoV-2 variants pose.
National Research Council Canada
Expected Results:

In the short term, anticipated outcomes will be strengthened collaborations across industry, academia, and government to support research excellence. In the medium term, anticipated outcomes will be the development of new and potentially disruptive technologies with collaborators. In the long term, find collaborative solutions to public policy challenges and create stronger innovation systems.

Toronto, Ontario, CA M3J 1P3
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Agreement Type:
Report Type:
Grants and Contributions
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Recipient Type:
Recipient's Legal Name:
York University
Federal Riding Name:
Humber River–Black Creek
Federal Riding Number:
Collaborative Science, Technology and Innovation Program - Collaborative R&D Initiatives
Program Purpose:

Collaborate on multiparty research and development programs to catalyze transformative, high-risk, high-reward research with the potential for game-changing scientific discoveries and technological breakthroughs in priority areas.

541710 - R&D in the physical, engineering and life sciences