Most important biological processes involve cell motion;​ breast carcinoma cells metastasize throughout a body, epithelial cells spread to close a wound, T-cells rush to fill their immune response duties. The list of essential phenomena is nearly endless, as is the corresponding number of biochemical signaling pathways and other biological features that mediate cell-cell and cell-environment interactions. Understanding these phenomena through the characterization of genetics and biological signaling is a fruitful, bottom-up approach. A complementary approach uses tools from condensed matter and statistical physics to quantify and make predictions about cells and interactions between them. For example, statistical metrics such as mean-squared displacement or velocity auto-correlation functions help characterize the behavior of cell populations with no knowledge of their specific biochemical interactions. We used these tools to determine the mechanism behind superdiffusivity in mouse fibroblast cells. The work put forth in this thesis shows that a generalized heterogeneous self-propelled particle model captures mouse fibroblast trajectory dynamics by replacing parameters in simulations (speed, rotational diffusion, tumble frequency) with appropriate distributions. Additionally, in order to quantify the intracellular orientation of mouse fibroblast cells, I developed robust imaging software which identifies and tracks Golgi bodies. When paired with the appropriate and already tracked nucleus, this yields a definition of cell orientation. After automating this software, we characterized the mechanoresponse of mouse fibroblast cells on static and active 2D shape memory polymer substrates. While the direction of cell nuclei elongation became more aligned after the shape memory polymer substrates were triggered to form wrinkles, as seen previously, the orientation defined by the Golgi body-nucleus axis aligned with the future wrinkle direction even before visible wrinkles were triggered, suggesting intracellular orientation is more sensitive to the environment than previously thought. In summary, this body of work represents novel investigations into the dynamics of mouse fibroblast cells in 2D as well as software contributions for imaging irregular objects in biological data.