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This tool exemplifies the simulation used in our study here https://doi.org/10.1101/2020.04.27.064139. You can select from the parameters used in the paper in the "Steering Panel" window. After the simulation is loaded it will wait for the selection of a parameter in the "Steering Panel" (even if you wish to stay with the default parameters you need to select one -- you can select a value that is already selected -- for the simulation to start). During the simulation you can choose to change the parameters at will.
Development of predictive quantitative models of all aspects of COVID-19 is essential for understanding this complex disease. COVID-19 varies significantly among individuals in both chances of infection and disease severity once infected. There is a critical need to develop mechanistic understanding of all aspects of COVID-19 spread and infection to assist in the development of improved clinical interventions.
We are developing a shared predictive multiscale modeling framework that integrates the wide variety of clinical and research data into a predictive, mechanism-based computational model. As this model is developed it will provide actionable insights on new approaches to treating this deadly disease. Our multiscale, multicellular, spatiotemporal model of viral infection of epithelial tissue includes a basic cellular immune response model, and viral and immune-induced tissue damage submodels. Our model is built of modular components that allow it to be easily modified, extended and adapted in a collaborative fashion to describe specific viral infections, tissue types and immune responses. The model allows us to define three disease state regimes: (1) where viral infection coincides with a massive cytopathic effect, (2) where the immune system rapidly controls the virus and (3) where the immune system controls the virus but extensive tissue damage still occurs. We have applied the model in proof-of-concept application to evaluate a number of drug intervention modalities. We found that (1) inhibition of viral internalization and faster immune-cell recruitment leads to containment of infection. (2) Faster viral internalization and slower immune response lead to uncontrolled spread of infection. (3) Simulation of a drug that reduce production of viral RNAs showed that a relatively small reduction of viral replication at the beginning of infection greatly decreases both the tissue damage and viral load, but (4) treatment that reduces the rate of genomic replication robustly and rapidly loses efficacy as the infection progresses. A number of simulation conditions lead to stochastically variable outcomes, with some replicas clearing or controlling the virus, while others see virus-induced damage sweep the entire simulated lung patch. This result suggests small differences between individual patients, or between different regions of an individual’s lungs, can result in large variations in outcomes. The model is open-source, modular and shared with the research community in a fully functioning form allowing reuse and extension by other researchers.
CompuCell3D is a flexible scriptable modeling environment, which allows the rapid construction of sharable Virtual Tissue in-silico simulations of a wide variety of multi-scale, multi-cellular problems including angiogenesis, bacterial colonies, cancer, developmental biology, evolution, the immune system, tissue engineering, toxicology and even non-cellular soft materials. CompuCell3D models have been used to solve basic biological problems, to develop medical therapies, to assess modes of action of toxicants and to design engineered tissues. CompuCell3D intuitive and make Virtual Tissue modeling accessible to users without extensive software development or programming experience. It uses Cellular Potts Model to model cell behavior.
Part of the nanoBio group https://nanohub.org/groups/nanobio
T.J. Sego, Josua O. Aponte-Serrano, Juliano Ferrari Gianlupi, Samuel R. Heaps, Kira Breithaupt, Lutz Brusch, James M. Osborne, Ellen M. Quardokus, James A. Glazier
JAG, TJS, JFG and JAS acknowledge funding from National Institutes of Health grants U24 EB028887 and R01 GM122424 and National Science Foundation grant, NSF 1720625. LB acknowledges grant 01ZX1707A within the German e:Med initiative (BMBF) and grant 391070520 by the German Research Foundation (DFG).
See https://doi.org/10.1101/2020.04.27.064139 for references.
T.J. Sego, Josua O. Aponte-Serrano, Juliano Ferrari Gianlupi, Samuel R. Heaps, Kira Breithaupt, Lutz Brusch, James M. Osborne, Ellen M. Quardokus, James A. Glazier, "A Modular Framework for Multiscale Spatial Modeling of Viral Infection and Immune Response in Epithelial Tissue", bioRxiv 2020.04.27.064139; doi: https://doi.org/10.1101/2020.04.27.064139
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