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

TL;DR

This paper demonstrates that a local structural measure called 'softness' can predict cellular rearrangements in a biological tissue model, revealing a strong link between structure and dynamics across different phases.

Contribution

It introduces the use of an information-theoretic approach to quantify how well softness predicts rearrangements in a Voronoi tissue model, extending insights from glass physics.

Findings

Softness predicts rearrangements with high accuracy.

The information content of softness decreases in the fluid phase.

Rearrangement likelihood varies over several orders of magnitude.

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.