MULTISCALE SPATIOTEMPORAL MODELING FOR HUMAN DISEASE: AGENT BASED MODELS FOR NONTUBERCULOUS MYCOBACTERIUM INFECTIONS AND ALZHEIMER’S DISEASE
Human disease and the corresponding immune response occur in three-dimensional space and time. Many diseases are difficult to study, either in vivo or in vitro, due to the complexity of the system. Despite computational models that can address complexity, many do not capture the spatial aspects of disease. Agent-based models are mechanistic, spatiotemporal computational models that can be integrated with other mathematical models to create multiscale models. Here I detail two models to examine spatiotemporal progression and possible treatment strategies for two diseases with low treatment success: Mycobacterium avium complex (MAC) and Alzheimer’s Disease.
MAC are biofilm-forming environmental microbes capable of residing in human lung nodules, causing MAC pulmonary disease (MAC-PD). Clinical drug susceptibility tests and treatment outcomes are poorly correlated, and nodules are complex and difficult to monitor, leading to low MAC cure rates (45-65%)2. I have developed an informative model of the initial infection events in MAC-PD. This model has been used to probe many different scenarios of infection and to predict the effect of potential interventions.
Alzheimer’s Disease (AD) is the leading cause of dementia, with no disease-altering pharmacological intervention. Microglia are phagocytotic neuroimmune cells, known to form barriers around plaques. There has been increased interest in leveraging microglia to slow the progression of neurodegeneration by manipulating these barriers. I present an agent-based model of microglia barriers at the single plaque level and use knock-out experiments to probe possible targets for immunotherapy and quantify their effects on plaque progression.
- Doctor of Philosophy
- Biomedical Engineering
- West Lafayette