Dimensionality reduction in bulk-boundary reaction-diffusion systems

TL;DR

This paper introduces a framework for reducing the complexity of cytosolic dynamics in reaction-diffusion systems by projecting them onto lower-dimensional bases, improving analysis of pattern formation in evolving cell geometries.

Contribution

The authors develop a generic method to approximate cytosolic dynamics using dominant concentration profiles, enabling efficient analysis of surface-volume coupled systems in changing geometries.

Findings

The framework accurately captures volume-dependent pattern coarsening.

Optimal basis choices improve approximation accuracy.

Method is effective for analyzing pattern formation in biological systems.

Abstract

Intracellular protein patterns regulate many vital cellular functions, such as the processing of spatiotemporal information or the control of shape deformations. To do so, pattern-forming systems can be sensitive to the cell geometry by means of coupling the protein dynamics on the cell membrane to dynamics in the cytosol. Recent studies demonstrated that modeling the cytosolic dynamics in terms of an averaged protein pool disregards possibly crucial aspects of the pattern formation, most importantly concentration gradients normal to the membrane. At the same time, the coupling of two domains (surface and volume) with different dimensions renders many standard tools for the numerical analysis of self-organizing systems inefficient. Here, we present a generic framework for projecting the cytosolic dynamics onto the lower-dimensional surface that respects the influence of cytosolic concentration gradients in static and evolving geometries. This method uses a priori physical information about the system to approximate the cytosolic dynamics by a small number of dominant characteristic concentration profiles (basis), akin to basis transformations of finite element methods. As a proof of concept, we apply our framework to a toy model for volume-dependent interrupted coarsening, evaluate the accuracy of the results for various basis choices, and discuss the optimal basis choice for biologically relevant systems. Our analysis presents an efficient yet accurate method for analysing pattern formation with surface-volume coupling in evolving geometries.