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Apollo: a comprehensive GPU-powered within-host simulator for viral evolution and infection dynamics across
Deshan Perera1, Evan Li1, Paul Mk Gordon2
1Department of Biochemistry & Molecular Biology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
Nature Communications
|July 2, 2025
Summary
We developed Apollo, a novel computational simulator for within-host viral evolution. This tool aids in understanding viral dynamics and transmission, improving epidemiological models and inference tools.
Area of Science:
- Virology
- Computational Biology
- Epidemiology
Background:
- Modern sequencing enables studying within-host viral evolution and inter-host transmission.
- 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 enable characterization of viral dynamics across population, tissue, and cellular levels.
- To validate viral transmission inference tools and understand their limitations.
Main Methods:
- Developed Apollo, a GPU-accelerated, out-of-core simulation tool.
- Apollo is scalable to hundreds of millions of viral genomes.
- Handles complex demographic and population genetic models.
Main Results:
- Apollo accurately replicates real within-host viral evolution, recapturing observed sequences from HIV and SARS-CoV-2.
- Simulated viral genomes and transmission networks were used to validate 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 simulating within-host viral evolution and infection dynamics.
- The tool enhances the interpretation of epidemiological predictions and the validation of inference methods.
- Apollo facilitates a deeper understanding of viral evolution in the context of transmission and population genetics.
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