Speaker
Description
Understanding the mechanisms that govern viral spread within human tissues remains a major challenge, especially for identifying and quantifying key factors that influence viral transmission and innate immune responses. Although mathematical models and experimental advances have provided valuable insights, revealing the complex spatio-temporal interactions of infection and immune processes at the tissue level has remained elusive. Here, we present a novel workflow that combines multimodal experimental data and individual cell-based modeling to allow the inference of viral and immune kinetics within tissues. While standard inference methods typically require custom summary statistics and resourceful re-fitting procedures for individual data sets, our workflow relies on simulation-based inference using BayesFlow, a framework for neural posterior estimation that allows for amortized inference of multimodal data. Validating our approach with synthetic data, we showed that integrating spatial information is essential for reliably inferring viral transmission kinetics and innate immune interactions within human airway epithelium, with subsequent application to experimental data on SARS-CoV-2 infection indicating local transmission as the dominant mode of viral spread. Our method can be readily adapted to various respiratory viral infections, helping to investigate co-infection and treatment scenarios, and presents a general framework for analysing viral infections at tissue level.
