Categorizing spatiotemporal dynamics of bacterial swarms

Low-dimensional effective models have proved to be an essential tool for analyzing extensive high-dimensional complex biophysical data, enabling computationally efficient characterizations of the dynamics of living systems. Recent advances in automated experimental imaging allow simultaneous measurements of spatiotemporal transcriptomes and spatiotemporal phenotypes during the collective motion of Bacillus subtilis' swarms. Here, we use a spectral dimensionality reduction framework to quantitatively characterize the spatiotemporal patterns that emerge and reveal correlations between cellular and collective properties. Using single-gene knockouts, whose morphology exhibits a rich breadth of macroscopic swarming phenomenology, we further quantify the impact of genotype on swarming behavior, by reducing the complex dynamics of the multicellular system to the time evolution of closed curves by representing the swarms by their moving boundary. The curves provide a three-dimensional spacetime surface representation of each mutant's phenomenology. We model these spacetime surfaces using a simple geometric model, utilizing modern inference techniques for dynamical systems to infer model parameters and cluster the spatiotemporal phenotype of the swarm shape dynamics under varying genotypes.