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Apollo: A comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across
Deshan Perera1, Evan Li1, Frank van der Meer2
1Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB T2N 4N1, Canada.
Biorxiv : the Preprint Server for Biology
|October 17, 2024
Summary
Apollo, a new computational simulator, models within-host viral evolution and infection dynamics. This tool aids in interpreting epidemiological predictions and validating viral transmission inference tools.
Area of Science:
- Virology
- Computational Biology
- Epidemiology
Background:
- Modern sequencing enables within-host viral evolution studies alongside transmission dynamics.
- Lack of computational simulators hinders interpretation of epidemiological predictions and validation of inference tools.
Purpose of the Study:
- To develop a computational tool for simulating within-host viral evolution and infection dynamics.
- To address the limitations in interpreting epidemiological predictions and validating viral transmission inference tools.
Main Methods:
- Developed Apollo, a GPU-accelerated, out-of-core simulation tool.
- Apollo handles large-scale simulations (millions of viral genomes) and complex population genetic models.
- Simulated viral genomes and transmission networks to validate existing tools.
Main Results:
- Apollo accurately replicates real within-host viral evolution, recapturing observed HIV sequences.
- The tool successfully validated a widely used viral transmission inference tool.
- Limitations of the transmission inference tool were uncovered using Apollo-simulated data.
Conclusions:
- Apollo provides a crucial tool for studying within-host viral evolution and infection dynamics.
- The simulator aids in interpreting epidemiological predictions and validating computational inference methods.
- Apollo enhances the understanding of viral evolution and transmission by bridging within-host and between-host dynamics.
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