Omics-Based Predictive Modeling of Age-Dependent Outcome to Influenza Infection (NIH)

The focus of this project is to integrate multi-scale data collected over the course of influenza infections-including system-wide transcriptomics and meta- transcriptomics, immunological response and physiological markers, along with viral diversity-in order to perform network analyses and develop computational models that predict severe disease outcome. Our goal is to leverage the power of high-dimensional, large-scale Omics data and mathematical modeling to identify risk-stratifying prognostic biomarkers that could be used in the development of point-of-care testing applicable to clinical respiratory samples to identify patients at risk for severe influenza disease. To achieve this goal, we will build predictive models from molecular interaction networks, translated to specific severity outcomes. We propose to use an age-dependent animal model (neonatal, adult and aged ferrets) and clinical human samples to collect biological measurements at multiple scales of host-virus interaction.