Speaker
Description
Pulmonary tuberculosis (TB) remains a dire concern for countries across the globe, caused by inhaling Mycobacterium tuberculosis (Mtb). Granulomas, tissue-scale nodules that form within lungs of Mtb infected individuals, are hallmarks of human TB and are a central factor that complicates predictions of TB outcomes. Granulomas form in both lungs and lymph nodes (LNs) of pulmonary TB patients, and exhibit heterogeneity within an organ, between organs, and between patients. Experiments implicate both lung and LN granulomas as key drivers of disease outcomes, but the complex interaction of multiple simultaneous infections within an individual makes predicting outcomes challenging. To understand how multiple granulomas within lungs and lymph nodes impact patient outcomes, we have developed two agent-based hybrid models HostSim and LymphSim. HostSim captures formation and maintenance of multiple lung granulomas within a virtual host in the presence of a dynamically-priming immune response including coarse grain blood and LN models. LymphSim is a fine-grained model tracking formation and maintenance of granulomas within lung draining LNs, and the downstream effects to providing adaptive immune response to damage, and hence impairment in that can arise. Using each, we are able to capture heterogeneity of hosts, granulomas and bacterial dynamics observed in experimental studies, and probe the most influential mechanisms governing host outcome using sensitivity analyses.
