Linear stability analysis of morphodynamics during tissue regeneration in plants

TL;DR

This paper introduces a linear stability analysis approach to quantify and model the morphodynamics of tissue regeneration in Arabidopsis thaliana, revealing two distinct temporal scales in the regenerative process.

Contribution

It presents a novel quantitative framework applying linear stability analysis to experimental morphological data during plant tissue regeneration.

Findings

Identification of two distinct temporal scales in regeneration dynamics

Eigenvalues classify the stability and relaxation modes of tissue morphology

Quantitative insights into self-organising properties of regenerating tissue

Abstract

One of the key characteristics of multicellular organisms is the ability to establish and maintain shapes, or morphologies, under a variety of physical and chemical perturbations. A quantitative description of the underlying morphological dynamics is a critical step to fully understand the self-organising properties of multicellular systems. Although many powerful mathematical tools have been developed to analyse stochastic dynamics, rarely these are applied to experimental developmental biology. Here, we take root tip regeneration in the plant model system Arabidopsis thaliana as an example of robust morphogenesis in living tissue and present a novel approach to quantify and model the relaxation of the system to its unperturbed morphology. By generating and analysing time-lapse series of regenerating root tips captured with confocal microscopy, we are able to extract and model the dynamics of key morphological traits at cellular resolution. We present a linear stability analysis of its Markovian dynamics, with the stationary state representing the intact root in the space of morphological traits. We find that the resulting eigenvalues can be classified into two groups, suggesting the co-existence of two distinct temporal scales during the process of regeneration. We discuss the possible biological implications of our specific results and suggest future experiments to further probe the self-organising properties of living tissue.