Mathematical modeling is a powerful tool that can be used at both theoretical and data-driven levels to understand the world around us, with different situations calling for different approaches. In both cases, many interesting phenomena emerge as the results of biased movement. In the first part of my talk, I will discuss a model for the establishment of intracellular gradients via rapid diffusion-state switching of proteins in the cytoplasm during cell polarization. I will show a separation of time scales that allows for a simple asymptotic solution for the final gradient. Additionally, we can calculate the accumulation time for the bulk of the local protein concentration analogously to a mean first passage time. For the second part, I will focus on the stability of fruiting bodies formed by Myxococcus xanthus, a prokaryote capable of a diverse set of emergent behaviors. Using cell-state data gathered from experiments, I will show how they can be used in an agent-based model to identify key cellular-level behaviors underlying an emergent phenomenon during fruiting body formation.