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Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

View ORCID ProfileMelanie E. Moses, Steven Hofmeyr, Judy L. Cannon, Akil Andrews, Rebekah Gridley, Monica Hinga, Kirtus Leyba, Abigail Pribisova, Vanessa Surjadidjaja, Humayra Tasnim, Stephanie Forrest
doi: https://doi.org/10.1101/2021.05.19.444569
Melanie E. Moses
1Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA
2Santa Fe Institute, Santa Fe, New Mexico, USA
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  • ORCID record for Melanie E. Moses
  • For correspondence: melaniem@unm.edu
Steven Hofmeyr
3Lawrence Berkeley National Laboratory, Berkeley, California, USA
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Judy L. Cannon
4Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
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Akil Andrews
1Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA
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Rebekah Gridley
4Department of Molecular Genetics and Microbiology, University of New Mexico School of Medicine, Albuquerque, New Mexico, USA
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Monica Hinga
1Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA
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Kirtus Leyba
5Biodesign Institute, Arizona State University, Tempe, Arizona, USA
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Abigail Pribisova
1Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA
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Vanessa Surjadidjaja
1Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA
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Humayra Tasnim
1Department of Computer Science, University of New Mexico, Albuquerque, New Mexico, USA
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Stephanie Forrest
2Santa Fe Institute, Santa Fe, New Mexico, USA
5Biodesign Institute, Arizona State University, Tempe, Arizona, USA
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Abstract

A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection, and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed ODE model. These results illustrate how realistic spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection.

Summary A key question in SARS-CoV-2 infection is why viral loads and patient outcomes are so different across individuals. Because it’s difficult to see how the virus spreads in the lungs of infected people, we developed Spatial Immune Model of Coronavirus (SIMCoV), a computational model that simulates hundreds of millions of cells, including lung cells and immune cells. SIMCoV simulates how virus grows and then declines, and the simulations match data observed in patients. SIMCoV shows that when there are more initial infection sites, the virus grows to a higher peak. The model also shows how the timing of the immune response, particularly the T cell response, can affect how long the virus persists and whether it is ultimately cleared from the lungs. SIMCoV shows that the different viral loads in different patients can be explained by how many different places the virus is initially seeded inside their lungs. We explicitly add the branching airway structure of the lung into the model and show that virus spreads slightly faster than it would in a two-dimensional layer of lung cells, but much slower than traditional mathematical models based on differential equations. These results illustrate how realistic spatial computational models can improve understanding of how SARS-CoV-2 infection spreads in the lung.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • This manuscript has been revised to provide more detail and clarity in Figs 3-5 and Discussion. Figure 8 has been added. References have been updated and added.

  • https://github.com/AdaptiveComputationLab/simcov

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted November 07, 2021.
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Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection
Melanie E. Moses, Steven Hofmeyr, Judy L. Cannon, Akil Andrews, Rebekah Gridley, Monica Hinga, Kirtus Leyba, Abigail Pribisova, Vanessa Surjadidjaja, Humayra Tasnim, Stephanie Forrest
bioRxiv 2021.05.19.444569; doi: https://doi.org/10.1101/2021.05.19.444569
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Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection
Melanie E. Moses, Steven Hofmeyr, Judy L. Cannon, Akil Andrews, Rebekah Gridley, Monica Hinga, Kirtus Leyba, Abigail Pribisova, Vanessa Surjadidjaja, Humayra Tasnim, Stephanie Forrest
bioRxiv 2021.05.19.444569; doi: https://doi.org/10.1101/2021.05.19.444569

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