Title: Computational Methods to Determine Mechanisms Associated with Respiratory Infection

Abstract: Respiratory viral infections pose a serious threat to public health, with seasonal influenza infection alone causing anywhere between 290,000 and 650,000 deaths each year and SARS-CoV-2 (COVID-19) infection resulting in over 7 million deaths since its emergence in late 2019. Children are uniquely susceptible to severe influenza infection with over 20 million children hospitalized and approximately one million children experiencing severe, life-threatening disease each year.  Unfortunately, many of the underlying host mechanisms that result in severe disease remain unclear, especially in pediatric populations.

Computational methods have emerged as a critical tool for revealing underlying immune response mechanisms of action, identifying key biomarkers and drug targets, and predicting complex longitudinal outcomes of infection. Here, we focus on three methods to elucidate the regulatory processes and host immune factors associated with enhanced pathology during respiratory infection. Machine learning models are applied to patient data to identify novel biomarker combinations that can accurately classify influenza and SARS-CoV-2 infection. Network-based algorithms are developed and applied to whole genome data from juvenile mice infected with influenza to identify new pathways involved in regulating host responses and viral replication. Lastly, a mechanistically derived ordinary differential equation (ODE) model of the innate immune response is constructed to elucidate differential host immune response mechanisms between juveniles and adults. Together, these methods will aid in understanding host immune responses to respiratory infections and aid in the development of improved therapeutics.

Chair:

Dr. Jason E. Shoemaker

Department of Chemical & Petroleum Engineering, University of Pittsburgh

Committee:

Dr. John F. Alcorn, Department of Immunology, Pediatrics, University of Pittsburgh

Dr. Ipsita Banerjee, Department of Chemical & Petroleum Engineering, University of Pittsburgh

Dr. Robert S. Parker, Department of Chemical & Petroleum Engineering, University of Pittsburgh