<p>Human disease and the corresponding immune response occur in three-dimensional space and time. Many diseases are difficult to study, either <em>in vivo</em> or <em>in vitro</em>, 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: <em>Mycobacterium  avium complex</em>  (MAC)  and  Alzheimer’s Disease.</p> <p>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.</p> <p>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.</p>