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COVID19 tissue simulator
This model simulates viral dynamics of SARS-CoV-2 (coronavirus / COVID19) in a layer of epithelium and an immune response. It is being rapidly prototyped and refined with community support (see below).
Please note that this is a stochastic model: for some simulation runs, you may see the immune system fail to respond. Users are encouraged to try the simulation multiple times.
This multiscale simulator combines several model components:
- Tissue: Virus, cell debris, and chemokines diffuse within the extracellular space. They may also “decay” to reflect removal by interstitial flow into nearby blood vessels or airways.
- ACE2 receptor dynamics: Virions bind to ACE2 receptors on the surface, which are internalized (endocytosed) into cells. After virions are released from internalized receptors, they can return to the surface.
- Viral replication: Internalized virus is uncoated to expose viral RNA, which synthesizes viral proteins that are assembled into virions. Assembled virions are transported to the cell surface to be exported to the tissue (exocytosed).
- Single-cell response: Infected cells secrete a chemokine that may attract immune cells. In a simple pharmacodynamics response (to assembled virions), infected cells can undergo apoptosis. Apoptosed cells release some or all of their internal contents, notably including virions.
- Immune response (new in version 3):
- Resident (and recruited) macrophages seek apoptotic cells. They phagocytose (ingest) dead cells upon contact and activate. They also break down ("digest") ingested materials.
- Activated macrophages release a pro-inflammatory cytokine to recruit other immune cells, while seeking both apoptotic and infected cells by chemotaxis. Activated macrophages can “wear out” and apoptose after phagocytosing too much material.
- Neutrophils are recruited by accumulated pro-inflammatory cytokine. They seek apoptotic cells, phagocytose them, and activate. Activated neutrophils seek both apoptotic and infected cells. Neutrophils also capture extracellular virions.
- CD8+ T cells are recruited by accumulated pro-inflammatory cytokine. They seek and adhere to infected cells. After sufficient contact time with one or more CD8+ T cells, infected cells undergo apoptosis.
Please cite this project via the preprint:
Y. Wang et al., Rapid community-driven development of a SARS-CoV-2 tissue simulator. bioRxiv 2020.04.02.019075, 2020 (Preprint). doi: 10.1101/2020.04.02.019075.
- lung epithelium: are colored by their (assembled) virion loads in four colors
- Cells with 0 assembled virions.
- Cells with 1-9 assembled virions.
- Cells with 10-99 assembled virions.
- Cells with 100-999 assembled virions.
- Cells with 1000+ assembled virions.
- Apoptotic (dead from viral load)
- CD8+ T cells: are colored by their cell cycle status
- G0G1, S, G2, M cycle
- macrophage: are colored by their cell cycle status
- G0G1, S, G2, M cycle
- Flag of activated_immune_cell > 0.5
- neutrophil: are colored by their cell cycle status
- G0G1, S, G2, M cycle
- Background: Contour plot of released virus that is diffusing in and above the tissue.
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.
- Config Basics tab: input parameters common to all models (e.g., domain grid, simulation time, choice/frequency of outputs)
- Microenvironment tab: microenvironment parameters that are model-specific
- User Params tab: user parameters that are model-specific
- Cell Types tab: parameters for cell types that are model-specific
- Out: Plots tab: output display of cells and substrates
- Animate tab: generate an animation of cells
Clicking the 'Run' button will use the specified parameters and start a simulation. When clicked, it creates an "Output" widget that can be clicked/expanded to reveal the progress of the simulation. When the simulation generates output files, they can be visualized in the "Out: Plots" tab. The "# cell frames" will be dynamically updated as those output files are generated by the running simulation. When the "Run" button is clicked, it toggles to a "Cancel" button that will terminate (not pause) the simulation.
This model is being rapidly prototyped. Biological and mathematical detail can be found at:
- Project website: http://covid19.physicell.org (opens in new tab)
- Model feedback: Google feedback form (opens in new tab)
- Preprint: Wang et al. (2020) (opens in new tab)
- GitHub codes: pc4covid19 GitHub organization (opens in new tab)
We request community help in estimating parameters and improving model assumptions at the link above.
This model and cloud-hosted demo are part of the education and outreach for the IU Engineered nanoBIO Node and the NCI-funded cancer systems biology grant U01CA232137. The models are built using PhysiCell: a C++ framework for multicellular systems biology.
Modify parameters in the "Config Basics", "Microenvironment", "User Params", or "Cell Types" tabs. Click the "Run" button once you are ready.
To view the output results, click the "Out: Plots" tab, and move the slider bar to advance through simulation frames. Note that as the simulation runs, the "# cell frames" field will increase, so you can view more simulation frames.
If there are multiple substrates defined in the Microenvironment, you can select a different one from the drop-down widget in the Plots tab. You can also fix the colormap range of values.
Note that you can download full simulation data for further exploration in your tools of choice. And you can also generate an animation of the cells to play in the browser and, optionally, download as a video.
About the software:
This model and cloud-hosted demo are part of the education and outreach for the IU Engineered nanoBIO Node and the NCI-funded cancer systems biology grant U01CA232137. The models are built using PhysiCell: a C++ framework for multicellular systems biology  for the core simulation engine and xml2jupyter  to create the graphical user interface (GUI).
- A. Ghaffarizadeh, R. Heiland, S.H. Friedman, S.M. Mumenthaler, and P. Macklin. PhysiCell: an open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput. Biol. 14(2):e1005991, 2018. DOI: 10.1371/journal.pcbi.1005991.
- R. Heiland, D. Mishler, T. Zhang, E. Bower, and P. Macklin. xml2jupyter: Mapping parameters between XML and Jupyter widgets. Journal of Open Source Software 4(39):1408, 2019. DOI: 10.21105/joss.01408.
This software is powered by PhysiCell [1-2], a powerful simulation tool that combines multi-substrate diffusive transport and off-lattice cell models. PhysiCell is BSD-licensed, and available at:
- GitHub releases: https://github.com/MathCancer/PhysiCell/releases
- SourceForge downloads: https://sourceforge.net/projects/physicell/
It is a C++, cross-platform code with minimal software dependencies. It has been tested and deployed in Linux, BSD, OSX, Windows, and other environments, using the standard g++ compiler.
The Jupyter-based GUI was auto-generated by xml2jupyter , a technique to create graphical user interfaces for command-line scientific applications.
 Ghaffarizadeh A, Heiland R, Friedman SH, Mumenthaler SM, Macklin P (2018) PhysiCell: An open source physics-based cell simulator for 3-D multicellular systems. PLoS Comput Biol 14(2): e1005991. https://dx.doi.org/10.1371/journal.pcbi.1005991
 Ghaffarizadeh A, Friedman SH, Macklin P (2016) BioFVM: an efficient, parallelized diffusive transport solver for 3-D biological simulations. Bioinformatics 32(8):1256-8. https://dx.doi.org/10.1093/bioinformatics/btv730
 Heiland R, Mishler D, Zhang T, Bower E, Macklin P (2019) Xml2jupyter: Mapping parameters between XML and Jupyter widgets. J Open Source Software 4(39):1408. https://dx.doi.org/10.21105/joss.01408
 Ozik J, Collier N, Wozniak J, Macal C, Cockrell C, Friedman S, Ghaffarizadeh A, Heiland R, An G, Macklin P (2018). High-throughput cancer hypothesis testing with an integrated PhysiCell-EMEWS workflow. BMC Bioinformatics 19:483. https://dx.doi.org/10.1186/s12859-018-2510-x.
 Ozik J, Collier N, Heiland R, An G, and Macklin P (2019). Learning-accelerated discovery of immune-tumor interactions. Molec. Syst. Design Eng. 4:747-60. https://dx.doi.org/10.1039/c9me00036d
Cite this work
Researchers should cite this work as follows:
Wang Y et al (2020) Rapid community-driven development of a SARS-CoV-2 tissue simulator. bioRxiv preprint 2020.04.02.019075. https://doi.org/10.1101/2020.04.02.019075