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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.

In-depth summary

Claude Opus 5.5 · xhigh effort · 8 Nov 2026 · from the full text of version 5 (10 Nov 2021) · CC BY 4.0

Open-source agent-based PhysiCell model of SARS-CoV-2 infection in lung epithelium; version 5 clears virus in about 6 days in simulation, and delayed T cell arrival increases epithelial loss.

COVID-19 outcomes arise from nonlinear interactions between intracellular viral replication, tissue-scale spread and immune responses, which cell-population ODE models do not capture at single-cell resolution. As the paper describes, intracellular replication models had not been coupled to spatial tissue models with immune dynamics, and agent-based spread models have generally lacked detailed intracellular replication; this open, modular framework combines them.

Key results

  1. SuggestedWith default v5 parameters, simulated viral replication peaks around day 4 and virus is cleared by about day 6. The authors describe v5 as the first version able to eliminate infection without destroying the entire epithelium.

    Fig 5.2, N = 12 replicates over 15 days: interferon rises quickly and persists. CD4+ T cells enter briefly, while CD8+ T cells stay for all 15 days. Ig production only ramps up after day 12, once most virus is gone, so antibody gives no major protection in the first infection. · Overview of v5 model results with base…; Discussion; Figure legends

  2. SuggestedAdding transit delays for dendritic cells (tissue to lymph node) and T cells (lymph node to tissue) postpones T cell arrival and lowers epithelial survival at day 15, with little change in macrophage, neutrophil or fibroblast recruitment.

    Four settings were compared: no delay; dendritic cell delay only (0.5 days); dendritic cell 0.5 days plus T cell 0.25 days (base); doubled delays (1 day and 0.5 days). Epithelial survival falls as delays increase (Fig 5.3B). The authors attribute this to CD8+ T cell-driven viral clearance. Replicate count not stated. · Impact of a time delay; Discussion of current model version; Figure legends

  3. SuggestedTissue was re-challenged from starting immune states interpolated between naive and post-infection ('educated'), with f the fraction of prior immunity retained. Protection holds after substantial waning: of the three levels tested, only f = 0.002 shows more damage and a viral load approaching the naive case.

    At f = 1: very little damage; CD8+ T cells still present at re-inoculation. At f = 0.03125: little added damage, and CD8+ T cells are recruited about 2 days earlier than at f = 0.002. Antibodies were present at the start in all three cases, and their overall level varied by at least two orders of magnitude between them. Authors: clearance with minimal damage despite more than 30-fold waning. · Impact of prior immune responses on subsequent…; Discussion of current model version; Figure legends

  4. SuggestedIn a four-parameter sensitivity analysis of 15-day v5 runs, the ACE2 binding rate constant had the largest influence (toward more surviving cells), followed by the viral diffusion coefficient and the paracrine interferon secretion rate. The T cell recruitment rate had little effect on day-15 live cell count.

    Latin hypercube samples within one order of magnitude of default values. A 4-dimensional linear regression of log10 final cell count on log10 parameters used only runs with more than 0 and fewer than the maximum surviving cells (Fig 5.5). Coefficient values and sample size are not given in the prose. · Global sensitivity analysis; Figure legends

  5. ClaimedSummaries of earlier versions (data in supplementary material, not shown in this text) report that lower ACE2 expression could paradoxically speed tissue-wide spread, and that rapid or pre-existing CD8+ T cell responses are protective.

    v1: for viruses that rapidly produce and exocytose new virions, extra virion release at cell death did not significantly speed spread, and lower cell tolerance to viral load sped tissue loss but did not drastically change spread. v2: lower ACE2 slowed per-cell replication but sped spread. v3: rapid recruitment and pre-existing CD8+ T cells were protective. v4: interferon strongly affected plaque spread. · Discussion; Summary of Versions 1-4

Methods, data and reproducibility

Agent-based model of a single-layer lung epithelium in PhysiCell: epithelial agents run ODE submodels for ACE2 trafficking, viral replication, interferon response and pyroptosis; virus, cytokines, debris and antibody diffuse via BioFVM; macrophages, neutrophils, dendritic cells, CD4+ and CD8+ T cells and fibroblasts are agents; a lymph node ODE model with transit delays supplies T cells and immunoglobulin.

  • Simulated v5 runs with default parameters over 15 days (mean and standard deviation) · n = 12 replicates
  • Simulations of fibrosis dynamics (TGF-β sources, fibroblasts, collagen deposition), 5th to 95th percentile bands · n = 15 iterations
  • Latin hypercube parameter samples (4 parameters) for linear-regression sensitivity analysis of 15-day v5 runs
  • Time-scale data for CD8+ T cell and antibody rates, used to retune v5 lymph node parameters · mouse (healthy mice)
  • Influenza infection data used for preliminary calibration (source not specified)
Methods & tools
PhysiCell, BioFVM, xml2jupyter, nanoHUB, Jupyter, MultiCellDS, Zenodo, Latin hypercube sampling
Software & environment
C++ core model v0.5.0 with default parameters in PhysiCell_settings.xml; nanoHUB app v5.0 with a Jupyter GUI generated by xml2jupyter; v3 releases moved to PhysiCell 1.7.1 and 1.7.2 beta (PhysiCell version for v5 not stated); 3-Clause BSD licence.
Experimental conditions
Hardware and runtime for these simulations are not reported. The paper notes PhysiCell routinely runs 10^4 to 10^6 cells with up to 10 diffusing substrates in 10 mm^3 on desktop workstations; Ig recruitment was capped at 1000 per diffusive step for f = 1 to avoid slowdowns.
Reproducibility gaps
Simulation outputs are not deposited; v1-v4 details are deferred to supplementary materials absent from this text; sensitivity-analysis sample size and coefficients are not given in the prose; the Slack workspace invitation link is not printed.
Limitations (6)
  • The immune model omits natural killer (NK) cells, the complement system, most cytokines and cytokine-receptor binding kinetics. It cannot return to a homeostasis resembling the pre-infection state after clearance, and neutrophils, macrophages and dendritic cells eventually deplete without recruitment signals. · stated by the authors
  • Known SARS-CoV-2 immune evasion (delayed type I interferon, early lymphopenia) is not modeled. Lymph node antigen presentation is a simplified ODE model lacking the Th1 requirement for CD8+ T cell expansion and Th1 inhibition of Th2, so Th2 responses dominate. · stated by the authors
  • Many added parameters make calibration difficult. Interferon is overactive and persists after infection, and fast ACE2 binding can briefly produce locally negative virion concentrations (total viral mass is conserved). Calibration with influenza data is only preliminary. · stated by the authors
  • Virus is seeded randomly over the whole tissue at a multiplicity of infection of 0.1, causing widespread spread before immune responses start. Macrophages can also activate without virus by taking up debris from random cell death. · stated by the authors
  • All results are simulations on a single epithelial layer (a 2D sheet of 3D voxels), with no quantitative comparison to SARS-CoV-2 patient, animal or organoid measurements in this text; the organoid experiments are described as planned. · noted in this summary
  • The sensitivity analysis covers only four parameters within one order of magnitude. It uses a linear fit on log scales, excludes runs where no cells or all cells died, and does not state the number of samples; the delay and waning-immunity scenarios also report no replicate counts. · noted in this summary

Read this if you want an open, modular agent-based template coupling intracellular viral replication, tissue spread and immune responses, or are organizing a consortium-built model with versioned releases and cloud-hosted apps.

Citations
61

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