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

Abstract

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

arXiv comment13 pages, 7 figures, questions both answered and unanswered

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Cited by in corpus (9)

• Linear Viscoelastic Properties of the Vertex Model for Epithelial Tissues
• Fragility in Glassy Liquids: A Structural Approach Based on Machine Learning
• Yielding and plasticity in amorphous solids
• Self-generated persistent random forces drive phase separation in growing tumors
• Discovering dynamic laws from observations: the case of self-propelled, interacting colloids
• Correlation of plastic events with local structure in jammed packings across spatial dimensions
• Dead or alive: Distinguishing active from passive particles using supervised learning
• Building a "trap model" of glassy dynamics from a local structural predictor of rearrangements
• Shape matters: Inferring the motility of confluent cells from static images