A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

  1. T. J. Sego
  2. Josua O. Aponte-Serrano
  3. Juliano Ferrari Gianlupi
  4. Samuel R. Heaps
  5. Kira Breithaupt
  6. Lutz Brusch
  7. Jessica Crawshaw
  8. James M. Osborne
  9. Ellen M. Quardokus
  10. Richard K. Plemper
  11. James A. Glazier

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Abstract

Simulations of tissue-specific effects of primary acute viral infections like COVID-19 are essential for understanding disease outcomes and optimizing therapies. Such simulations need to support continuous updating in response to rapid advances in understanding of infection mechanisms, and parallel development of components by multiple groups. We present an open-source platform for multiscale spatiotemporal simulation of an epithelial tissue, viral infection, cellular immune response and tissue damage, specifically designed to be modular and extensible to support continuous updating and parallel development. The base simulation of a simplified patch of epithelial tissue and immune response exhibits distinct patterns of infection dynamics from widespread infection, to recurrence, to clearance. Slower viral internalization and faster immune-cell recruitment slow infection and promote containment. Because antiviral drugs can have side effects and show reduced clinical effectiveness when given later during infection, we studied the effects on progression of treatment potency and time-of-first treatment after infection. In simulations, even a low potency therapy with a drug which reduces the replication rate of viral RNA greatly decreases the total tissue damage and virus burden when given near the beginning of infection. Many combinations of dosage and treatment time lead to stochastic outcomes, with some simulation replicas showing clearance or control (treatment success), while others show rapid infection of all epithelial cells (treatment failure). Thus, while a high potency therapy usually is less effective when given later, treatments at late times are occasionally effective. We illustrate how to extend the platform to model specific virus types ( e . g ., hepatitis C) and add additional cellular mechanisms (tissue recovery and variable cell susceptibility to infection), using our software modules and publicly-available software repository.

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  1. Version published to 10.1371/journal.pcbi.1008451
  2. Version published to 10.1101/2020.04.27.064139v4 on bioRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.04.27.064139: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    The COVID-19 simulation can also be run online without requiring any installations or downloads on the nanoHUB servers at https://nanohub.org/tools/cc3dcovid19/.
    nanoHUB
    suggested: (nanoHUB, RRID:SCR_013963)
    Line 8 imports the Python standard module “random” for generating random numbers, which, like the rest of the Python standard library and many others, is distributed with CompuCell3D.
    Python
    suggested: (IPython, RRID:SCR_001658)
    It should be noted that deployment of the SimpleRecoverySteppable class is not limited to usage directly in CompuCell3D as a simulated steppable.
    CompuCell3D
    suggested: (CompuCell3D, RRID:SCR_003052)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: Please consider improving the rainbow (“jet”) colormap(s) used on pages 16, 91 and 37. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

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  4. Version published to 10.1101/2020.04.27.064139v3 on bioRxiv
  5. Version published to 10.1101/2020.04.27.064139v2 on bioRxiv
  6. Version published to 10.1101/2020.04.27.064139v1 on bioRxiv