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The recent emergence of the SARS-CoV-2 virus, the etiological agent of COVID-19, has caused worldwide repercussions in just a few short weeks. The tremendous negative social, economic and health impact will be felt for a considerable time around the globe. Numerous complementary strategies must be taken to address this issue and to ameliorate the consequences. One such strategy that is clearly required is a better understanding of the fundamental biology of SARS-CoV-2. The objective of this project is to create a multiscale model of the intracellular and intercellular viral dynamics of the SARS-CoV-2 virus. A systems virology approach is used. This project would deliver an understanding of how the virus works and will provide the framework necessary for numerous fundamental studies and applied efforts for developing effective drugs and therapies. The developed model would be released under an open source license and made freely available from the GitHub website. The project would train one graduate student in the area of computational systems virology.
This project creates intracellular and intercellular models of the SARS-2019-CoV-2 virus based on the current understanding of the virus reaction network. The reaction network topology is translated into a mathematical model with the use of the theory of reaction kinetics. A set of ordinary differential equations describing the dynamics of viral replication within an infected cell is formulated with the use of the mass action kinetics paradigm. With the use of Monte Carlo sampling of the model?s parameter space, as well as sensitivity analysis and stability analysis, mathematical and biological constraints are identified. Stochastic simulation is carried out to determine whether subpopulations of infected cells exist with potentially different phenotypes. Finally, with the use of principles grounded in basic calculus, the intracellular model is coupled to a standard intercellular model of viral replication dynamics. The multiscale models undergo the same analyses as the intracellular model and is also implemented stochastically to determine if incorporating higher scale information influences prediction of subpopulation distribution.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
