Iterative community-driven development of a SARS-CoV-2 tissue simulator

Abstract

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.

In-host Mathematical Modelling of COVID-19 in Humans

“…While there are many mathematical models developed at epidemiological level for COVID-19 to discuss the transmission of SARS-CoV-2 and de-confinement strategies ( Anderson, Heesterbeek, Klinkenberg, Hollingsworth, 2020 , Ferretti, Wymant, Kendall, Zhao, Nurtay, Abeler-Dörner, Parker, Bonsall, Fraser, 2020 , Lopez, Rodo, 2020 , Mejia-Hernandez, Hernandez-Vargas, 2020 , Peng, Yang, Zhang, Zhuge, & Hong , Ricardo-Azanza, Vargas-Hernandez, 2020 ), there are too few models at within-host level to understand SARS-CoV-2 replication cycle, interactions with the immune system, and drug effects ( Du, Yuan, 2020 , Ejima, Kim, Ito, Iwanami, Ohashi, Koizumi, Watashi, Bento, Aihara, Iwami, 2020 , Gonçalves, Bertrand, Ke, Comets, de Lamballerie, Malvy, Pizzorno, Terrier, Calatrava, Mentré, Smith, Perelson, Guedj, 2020 , Goyal, Cardozo-Ojeda, Schiffer, 2020 , Su, Ejima, Ito, Iwanami, Ohashi, 2020 , Wang, Heiland, Craig, Davis, Ford Versypt, Jenner, Ozik, Collier, Cockrell, Becker, An, Glazier, Narayanan, Smith, Macklin, 2020 ). Among different model structures to represent viral dynamics, the target cell limited model has served to represent several diseases such as HIV Hernandez-Vargas and Middleton (2013) ; Perelson and Ribeiro (2013) ; Pinkevych et al.…”

COVID-19 Disease Map, a computational knowledge repository of SARS-CoV-2 virus-host interaction mechanisms

“…The modelling and simulation of such complex scenarios require a dedicated multiscale computational architecture, where multiple models run in parallel and communicate among them to capture cellular behaviour and intercellular communications. Multiscale agent-based models simulate processes taking place at different time scales, e.g., diffusion, cell mechanics, cell cycle, or signal transduction [294], proposed also for COVID-19 [295]. PhysiBoSS [296] allows such simulation of intracellular processes by combining the computational framework of PhysiCell [297] with MaBoSS [298] tool for stochastic simulation of logical models to study of transient effects and perturbations [299].…”

A modular framework for multiscale, multicellular, spatiotemporal modeling of acute primary viral infection and immune response in epithelial tissues and its application to drug therapy timing and effectiveness

“…Mathematical modeling methods integrate the available host- and pathogen-level data on disease dynamics that are required to understand the complex biology of infection and immune response to optimize therapeutic interventions [3–5]. Mathematical models and computer simulations built on spatial and ODE frameworks have been extensively used to study in-host progression of viral infection [6], with a recent acceleration in the development of spatial COVID-19 viral infection models in response to the global pandemic [7, 8].…”

“…We approximate the discrete process of a cell’s internalization of a virus particle by a stochastic virus internalization event ( E1 ) determined by time elapsed, the local concentration of the virus field, and the number of available cell-surface receptors on the cell. We simplify the complexity of viral replication into four steps: unpacking, viral genome replication, protein synthesis and packaging/assembly ( E2 , Fig 2B) [7,52–54]. The subcellular kinetics of viral replication determine the rate of release of new virus particles into the extracellular environment ( E3 ).…”