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    This study developed a multiscale simulation model for SARS-CoV-2 dynamics in lung tissue. The model aids in understanding viral propagation, immune responses, and identifying therapeutic targets for COVID-19.

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    Area of Science:

    • Computational Biology
    • Virology
    • Immunology

    Background:

    • Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a significant global health threat.
    • Limited understanding exists regarding the multiscale interactions of SARS-CoV-2, from molecular virus-receptor binding to tissue-level propagation and immune responses.
    • Varied clinical outcomes, including asymptomatic infections and acute respiratory distress syndrome (ARDS), are observed, influenced by factors like age and inflammation.

    Approach:

    • Developed and refined a prototype multiscale simulation model for SARS-CoV-2 dynamics in lung tissue.
    • Utilized open-source code and an interactive online model for rapid, international collaboration.
    • Integrated expertise from virology, immunology, mathematical biology, and computational science.

    Key Points:

    • Multiscale models can elucidate emergent dynamics driving divergent infection outcomes.
    • Models can identify therapeutic intervention "choke points" and potential biomarkers for patient stratification.
    • The open-source, modular framework facilitates rapid adaptation for studying viral diseases and immune responses.

    Conclusions:

    • Multiscale simulation models are crucial for understanding complex viral dynamics and predicting disease outcomes.
    • The developed framework offers a reusable approach for studying viral infections and immune responses in tissues.
    • This collaborative effort accelerates the development of therapeutic strategies against SARS-CoV-2 and potentially other viral diseases.