The most prevalent, devastating, and complex diseases of our time, such as diabetes, cardiovascular disease, cancer, and infectious diseases, involve the dynamic interactions of cells with one another and with their changing environment. However, the drugs we typically use to treat diseases target a single protein and disregard the fact that cells within tissues are highly heterogeneous and have individualized responses that contribute to the tissue-level outcomes. To bridge the gap between protein and multi-cell/tissue-levels of spatial scale, my lab develops agent-based computational models and uses them in combination with experiments and machine learning approaches to predict how individual cell behaviors give rise to tissue-level adaptations. We have used agent-based modeling to simulate the structural adaptations of large and small blood vessels, cardiac and skeletal muscle regeneration following injury, and lung tissue remodeling during fibrosis. Our studies have suggested new mechanistic hypotheses and provided guidance for the design of novel therapies that account for the dynamic and heterogeneous interactions between different cell types within diseased and regenerating tissues.