I will give an overview of data-driven questions that can be tackled using optimal-transport based algorithms. First, I will show that irregular patterns formed of interacting spots and stripes can be described by pattern statistics, which are probability measures that capture the distribution of certain features, such as the number of connected components or their areas. We use this approach with arclength predictor-corrector continuation to trace out transition and bifurcation curves in parameter space by maximizing the Wasserstein distance of the pattern statistics. Secondly, I will discuss the application of optimal-transport-based algorithms to infer dynamic gene-regulatory networks from time-stamped single-cell gene expression counts.