Georgia Institute of Technology College of Sciences

Two years after the beginning of the pandemic, we are still working to understand the mechanisms of immunopathology in COVID-19. Immune responses following SARS-CoV-2 infections are heterogeneous, and biomarkers of this variability remain to be elucidated. In collaboration with experimentalists and clinicians, we have deployed various mathematical and computational approaches to understand longitudinal immunological data from patients, and to generate new hypotheses about the factors determining COVID-19 severity and disease dynamics.
To answer foundational questions about immunopathology and heterogeneity in COVID-19, we have developed a multi-scale, mechanistic mathematical model of the immune response to SARS-CoV-2 that includes several innate and adaptive immune cells and their communication via signalling networks. By generating a population of virtual patients, we identified dysregulated rates of monocyte-to-macrophage differentiation that distinguishes disease severity in these in silico patients. Further, our results suggest that maximal IL-6 concentrations can be used as a predictive biomarker of CD8+ T cell lymphopenia. Using the same cohort of virtual patients, we have also studied the influence of variant on immunopathology by combining our model with data of intra-host viral evolution. We predicted that the combined effects of mutations affecting the spike proteins and interferon evasion on the severity of COVID-19 are mostly determined by the innate host immune response. Our approaches can be used to study the factors regulated immunopathology during SARS-CoV-2 infections, and represent a quantitative framework for the study of COVID-19 and other viral diseases.

Recording link: https://bluejeans.com/s/6CmKwHWWc2O