Quantifying the link between local structure and cellular rearrangements using information in models of biological tissues

Machine learning techniques have been used to quantify the relationship between local structural features and variations in dynamical activity disordered glass-forming materials. To date these methods applied an array of standard (Arrhenius super-Arrhenius) glass formers, where work on "soft spots" indicates a connection linear vibrational response configuration energy barriers non-linear deformations. Here we study Voronoi model, which takes its inspiration from dense epithelial monolayers displays anomalous, sub-Arrhenius scaling relaxation time with decreasing temperature. Despite differences, find that likelihood rearrangements can vary by several orders magnitude within model tissue extract quantity, "softness" accurately predicts temperature-dependence time. We use information-theoretic measure extent softness determines impending topological rearrangements; captures nearly all information about is obtainable structure, this large solid phase decreases rapidly as state variables are varied into fluid phase.