A Mean-Field Model for Active Plastic Flow of Epithelial Tissue

TL;DR

This paper develops a mean-field model that links microscopic cell dynamics to large-scale tissue morphogenesis, explaining active plastic flow and its regulation by external cues in epithelial tissues.

Contribution

It introduces a novel stochastic mean-field framework that quantitatively connects cell-level activity with tissue-scale shape changes, incorporating active stresses and external fields.

Findings

Model explains self-limiting active plastic flow in tissues.

Quantitative predictions match experimental and simulation data.

External fields can direct tissue morphogenesis by influencing cell rearrangements.

Abstract

Animal morphogenesis often involves significant shape changes of epithelial tissue sheets. Great progress has been made in understanding the underlying cellular driving forces and their coordination through biomechanical feedback loops. However, quantitative understanding of how cell-level dynamics translate into large-scale morphogenetic flows remains limited. A key challenge is finding the relevant macroscopic variables (order parameters) that retain the essential information about cell-scale structure. To address this challenge, we combine symmetry arguments with a stochastic mean-field model that accounts for the relevant microscopic dynamics. Complementary to previous work on the passive fluid- and solid-like properties of tissue, we focus on the role of actively generated internal stresses. Centrally, we use the timescale separation between elastic relaxation and morphogenetic dynamics to describe tissue shape change in quasi-static balance of forces within the tissue sheet. The resulting geometric structure - a triangulation in tension space dual to the polygonal cell tiling - proves ideal for developing a mean-field model. All parameters of the coarse-grained model are calculated from the underlying microscopic dynamics. Centrally, the model explains how active plastic flow driven by autonomous active cell rearrangements becomes self-limiting as previously observed in experiments and simulations. Additionally, the model quantitatively predicts tissue behavior when coupled with external fields, such as planar cell polarity and external forces. We show how such fields can sustain oriented active cell rearrangements and thus overcome the self-limited character of purely autonomous active plastic flow. These findings demonstrate how local self-organization and top-down genetic instruction together determine internally-driven tissue dynamics.