COVID-19 recently emerged as a worldwide pandemic, causing untold human suffering and severe economic disruptions. How individual immune systems respond to a novel coronavirus, for example, why some individuals clear infection efficiently while others do not is not known. This project seeks to understand how immune cells, specifically T cells, find cells infected with virus that are dispersed in the lung. It will address how spatial distributions of infected cells and movement patterns of T cells through complex lung structures determine the course of infection. The project will develop the Spatial Immunological Model of Coronavirus (SIM-Cov), a simulation model for studying these effects and improving understanding of how the immune system controls infection by coronaviruses. The model will take computed tomography (CT) scans of an infected human lung as input, as well as biological data on how T cells interact with the virus and infected lung cells. The model will predict the course of infection in the form of visually intuitive movies showing how the infection progresses through time in different individuals. By modeling variability in individuals? infectious rates over time, SIM-Cov will improve our understanding of why the severity of COVID-19 varies so much among individuals. The model and movies will be publicly accessible, and incorporated into educational materials for high school and college students. The project will also train two graduate and one undergraduate students in interdisciplinary research.
One gap in understanding infection dynamics of the novel coronavirus SARS-CoV-2 is why the severity of infection varies so much among individuals. This project addresses that gap by incorporating the role played by spatial-temporal dynamics in within-host infections and immune control, particularly the role of T cells which are required for viral clearance. Most quantitative models of viral infection use differential equations or stochastic models and do not account for the spatial distribution of infected cells or T cell movement patterns. The project addresses this gap by developing a three-dimensional spatial model of the whole lung (SIM-Cov) that tests how spatial interactions between T cells and virus affect viral growth, load and clearance within the lungs. Ultimately, these within-host factors contribute to the rate of clearance within a single host and transmission between hosts. SIM-Cov will be parameterized and validated with empirical imaging data (CT scans of SARS-infected patients) and the emerging literature on SARS-CoV-2 and immune responses. SIM-Cov will model the lung microenvironment, including vasculature and epithelium surrounding the airways and alveolar spaces, the spatial and temporal spread of virus throughout the lung, and the spatial arrangement and movement of T cells. The project will have broad-ranging impacts for understanding coronavirus infection dynamics and educational impacts through dissemination of the model and movies produced in the project, as well as the engagement of three students in interdisciplinary research.
This RAPID award is made by the Physiological Mechanisms and Biomechanics Program and the Symbiosis, Defense, and Self-recognition Program in the BIO Division of Integrative Organismal Systems, and by the Established Program to Stimulate Competitive Research (EPSCoR), using funds from the Coronavirus Aid, Relief, and Economic Security (CARES) Act.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.