"Iterative community-driven development of a SARS-CoV-2 tissue simulator"
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 an ABM model of SARS-CoV-2 dynamics in lung tissue in PhysiCell. This model finds key points on infection dynamics identified for both early timescale (interferon and receptor uptake) and later lymph node interactions (CD4+/CD8+ T Cell and anti-body recruitment). Interestingly, the model also pointed towards the spatial ability of immune cell sensing and initial infection spread with similar MOIs. More broadly, this effort created 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.
Additional authors: Paul Macklin, Intelligent Systems Engineering, Indiana University;