Experimentally Validating Models to Predict Virus-Mediated Pathology and Disease Severity

Influenza viruses infect millions of individuals each year and cause a significant amount of morbidity and mortality. Understanding how the virus spreads within the lung, how efficacious host immune control is, and how different factors influence acute lung injury, inflammation, and disease severity is critical to combat the infection. I'll discuss our integrative model-experiment exchange that allowed us to establish dynamical connections to predict lung pathology and severity. We used the model to predict various features of the infection and CD8-mediated clearance, including a density-dependent rate of infected clearance, and validated the model and its predictions through CD8 depletion and whole lung histomorphometry. Additional analysis illuminated important nonlinear relations between disease severity, inflammation, and lung injury.

Email Veronica Ciocanel (ciocanel@math.duke.edu) for zoom link