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http://conacyt.repositorioinstitucional.mx/jspui/handle/1000/2333| Rapid community-driven development of a SARS-CoV-2 tissue simulator | |
| Yafei Wang. Randy Heiland. Morgan Craig. Courtney L. Davis. Ashlee N Ford Versypt. Adrianne Jenner. Jonathan Ozik. Nicholson Collier. Chase Cockrell. Andrew Becker. Gary An. James A. Glazier. Aarthi Narayanan. Amber M Smith. Paul Macklin. | |
| Acceso Abierto | |
| Atribución-NoComercial-SinDerivadas | |
| 10.1101/2020.04.02.019075 | |
| The 2019 novel coronavirus, SARS-CoV-2, is an emerging 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 like diabetes. A critical question for treatment and protection is why it appears that the severity of infection may correlate with the initial level of virus exposure. 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 interactions, screen potential therapies, and identify potential biomarkers that differentiate response dynamics. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce a prototype of a multiscale model of SARS-CoV-2 dynamics in lung and intestinal tissue that will be iteratively refined. The first prototype model was built and shared internationally as open source code and interactive, cloud-hosted executables in under 12 hours. In a sustained community effort, this model will integrate 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. NoteThis is a rapid prototyping project. For the very latest, see http://covid19.physicell.org | |
| www.biorxiv.org | |
| 2020 | |
| Artículo | |
| https://www.biorxiv.org/content/10.1101/2020.04.02.019075v1.full.pdf | |
| Inglés | |
| VIRUS RESPIRATORIOS | |
| Aparece en las colecciones: | Artículos científicos |
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