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Version 0.3.1: draft v3 prototype

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@MathCancer MathCancer released this 03 Jul 17:15
· 122 commits to master since this release
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COVID19 tissue simulator

Version: 0.3.1

Release date: 3 July 2020

Overview

This model simulates replication dynamics of SARS-CoV-2 (coronavirus / COVID19) in a layer of epithelium with an initial immune reaction. It is being rapidly prototyped and refined with community support (see below).

In this model, SARS-CoV-2 (coronavirus / COVID19) infects a single cell, or a solution of virions is administered to the extracellular space. The virus is uncoated to explose viral RNA, which synthesizes viral proteins that are assembled into a virion. Assembled virions are exported to the environment, where they can diffuse and infect other cells. In the extracellular space, virions adhere to ACE2 receptors and get internalized through endocytosis. Internalized ACE2 receptors release their virus cargo and are recycled back to the surface.

Resident macrophages ingest apototic cells and release a pro-inflammatory cytokine that recruits additional macrophages, neutrophils, and CD8+ T cells. CD8+ T cells chemotax towards cytokines released by infected cells and adhere. Cumulative CD8+ T cell contact time can induce apoptosis in infectd cells. Activated macrophages and neutrophils chemotaxis chemotax along chemokine and debris gradients and continue to phagocytose dead cells. Neutrophils also absorb free (extracellular) virus.

The model includes a basic pharmacodynamic response (to assembled virions) to cause cell apoptosis. Apoptosed cells release some or all of their internal contents, notably including virions.

Caveats and disclaimers:

This model is under active development using rapid prototyping:

  • It has not been peer reviewed.
  • It is intended to drive basic scientific research and public education at this stage.
  • It cannot be used for public policy decisions.
  • It cannot be used for individual medical decisions.

This model will be continually refined with input from the community, particularly experts in infectious diseases. The validation state will be updated as this progresses.

Key makefile rules:

make : compiles the project.

make clean : removes all .o files and the executable, so that the next "make" recompiles the entire project

make data-cleanup : clears out all simulation data

More references

Preprint: https://doi.org/10.1101/2020.04.02.019075

Model details: https://github.com/MathCancer/COVID19/wiki/About

Homepage: http://covid19.PhysiCell.org

Support: https://sourceforge.net/p/physicell/tickets/

Latest info: follow @PhysiCell and @MathCancer on Twitter (http://twitter.com/MathCancer)

See changes.md for the full change log.


Release summary:

0.3.1:

This release improves parameter estimates for digestion of phagocytosed material and has an immune model refinement to prevent runaway macrophage death.

0.3.0:

This release incorporates major v2 model feedback and adds the first immune submodel.

NOTE: OSX users must now define PHYSICELL_CPP system variable. See the documentation.

New features and changes:

0.3.1:

  • Refined macrophage and neutrophil models and parameters for phagocytosis. (Upgrade from immune submodel 0.1.0 to 0.1.1.)

0.3.0:

  • Refactored modular design to include refinements from immune model.

  • First integration of new immune submodel.

  • Upgrade to PhysiCell Version 1.7.1, allowing use of XML-based cell definitions to define the behavior of immune cell types.

  • Upgrade to PhysiCell Version 1.7.2beta to improve multithreaded performance, add new cell-cell interaction features, and fix concurrency issues on some platforms.

Bugfixes

0.3.0:

  • None.

Notices for intended changes that may affect backwards compatibility:

  • None.

Planned future improvements:

  • Continue to vet model biology with collaborators.

  • Add lymph node module.

  • Add tissue damage models.

  • Integrate SBML support for submodels.

  • Refine viral replication model.

  • Refine immune model (including more cell types and improved parameter estimates).

  • Add interferon response model.