Iterative community-driven development of a SARS-CoV-2 tissue simulator

  1. Michael Getz
  2. Yafei Wang
  3. Gary An
  4. Maansi Asthana
  5. Andrew Becker
  6. Chase Cockrell
  7. Nicholson Collier
  8. Morgan Craig
  9. Courtney L. Davis
  10. James R. Faeder
  11. Ashlee N. Ford Versypt
  12. Tarunendu Mapder
  13. Juliano F. Gianlupi
  14. James A. Glazier
  15. Sara Hamis
  16. Randy Heiland
  17. Thomas Hillen
  18. Dennis Hou
  19. Mohammad Aminul Islam
  20. Adrianne L. Jenner
  21. Furkan Kurtoglu
  22. Caroline I. Larkin
  23. Bing Liu
  24. Fiona Macfarlane
  25. Pablo Maygrundter
  26. Penelope A Morel
  27. Aarthi Narayanan
  28. Jonathan Ozik
  29. Elsje Pienaar
  30. Padmini Rangamani
  31. Ali Sinan Saglam
  32. Jason Edward Shoemaker
  33. Amber M. Smith
  34. Jordan J.A. Weaver
  35. Paul Macklin

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Abstract

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable “choke points” for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

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  1. Version published to 10.1101/2020.04.02.019075v5 on bioRxiv
  2. Version published to 10.1101/2020.04.02.019075v4 on bioRxiv
  3. Version published to 10.1101/2020.04.02.019075v3 on bioRxiv
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    SciScore for 10.1101/2020.04.02.019075: (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 EMEWS (extreme model exploration with Swift) framework133 was developed to directly address this issue and to provide a broadly applicable cyberinfrastructure to lower the barriers for utilization of advanced, large-scale model exploration on HPC resources.
    Swift
    suggested: (Swift, RRID:SCR_013018)
    However, since 2017, they have adopted and promoted the use of Jupyter notebooks140, with accompanying Python modules to provide GUI widgets and visualization.
    Python
    suggested: (IPython, RRID:SCR_001658)
    Those platforms, such as nanoHUB, that provide easy-to-use web-based GUIs and APIs and offer affordable pricing will likely have their rate of adoption continue to increase, especially among researchers who may lack the expertise or resources to install complex pieces of software.
    nanoHUB
    suggested: (nanoHUB, RRID:SCR_013963)
    Model parameters are set by editing XML (extensible markup language) configuration files, and the models save data as a combination of vector graphics outputs (scalable vector graphics: SVG) and XML and MATLAB data (.mat) files based on the draft MultiCellDS data standard141.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)
    They cooperate with the overall leads to create model releases (which will always bundle the most stable version of each submodel), update the nanoHUB models, and update the bioRxiv preprint.
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    Results from OddPub: Thank you for sharing your code and data.

    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 38 and 39. 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.

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  5. Version published to 10.1101/2020.04.02.019075v2 on bioRxiv
  6. Version published to 10.1101/2020.04.02.019075v1 on bioRxiv