Marissa Renardy
Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), kills more individuals worldwide per year than any other infectious agent. As the hallmark of TB, lung granulomas are complex structures composed of immune cells that interact with and surround bacteria, infected cells, and a necrotic core. This interaction leads to diverse granuloma outcomes across time, ranging from bacterial sterilization to uncontrolled bacterial growth, as well as diverse spatial structures. Currently, there are no systematic quantitative methods to classify the formation, function, and spatial characteristics of granulomas. This type of analysis would enable better understanding and prediction of granuloma behaviors that have known associations with poor clinical outcomes for TB patients. Herein, we develop a temporal and spatial analysis framework for TB granulomas using a systems biology approach combining in silico granuloma modeling, geographic information systems, topological data analysis, and machine learning. We apply this framework to simulated granulomas to understand temporal granuloma dynamics, quantify granuloma spatial structure, and predict the relationship between granuloma structure and bacterial growth. As a proof-of-concept, we apply our in silico predictions to in vivo derived data to test our framework for future applications and as a personalized medicine intervention.
