In natural biological systems, self-organization occurs at multiple scales—from molecular self-assembly, organization of cytoskeletal elements, collective migration of cells to flocking of birds. In recent decades, advancements in microscopy, genetic engineering, biochemistry, and computational modeling have enabled us a more quantitative description of these processes. In this talk, I will show how tools from Statistical Mechanics can be used to develop predictive understanding from such quantitative data. First, I will discuss nuclear division in the syncytial fruit fly embryo, which surprisingly preserves structural disorder even as the system densifies. Next, I will introduce a computational framework that integrates cell–cell adhesion, cellular motility, and substrate stiffness to study self-assembly at the cellular scale. Finally, I will describe how concepts from the physics of flexible polymers can be extended to capture the influence of active biological forces and heterogeneity in chain extensibility on biopolymer dynamics.