Mesoscopic model for filament orientation in growing actin networks: the role of obstacle geometry

TL;DR

This study develops continuum models for filament orientation in growing actin networks behind obstacles of various shapes, revealing two stable orientation patterns and providing insights into how obstacle geometry influences network architecture.

Contribution

It introduces a unified continuum modeling framework for filament orientation around different obstacle geometries, validated by simulations and applicable to curved surfaces.

Findings

Two stable filament orientation patterns identified (+35/-35 and +70/0/-70 degrees).

Phase diagrams show how obstacle shape influences orientation stability.

Continuum PDE models agree with stochastic simulations for various obstacle geometries.

Abstract

Propulsion by growing actin networks is a universal mechanism used in many different biological systems. Although the core molecular machinery for actin network growth is well preserved in most cases, the geometry of the propelled obstacle can vary considerably. In recent years, filament orientation distribution has emerged as an important observable characterizing the structure and dynamical state of the growing network. Here we derive several continuum equations for the orientation distribution of filaments growing behind stiff obstacles of various shapes and validate the predicted steady state orientation patterns by stochastic computer simulations based on discrete filaments. We use an ordinary differential equation approach to demonstrate that for flat obstacles of finite size, two fundamentally different orientation patterns peaked at either +35/-35 or +70/0/-70 degrees exhibit mutually exclusive stability, in agreement with earlier results for flat obstacles of very large lateral extension. We calculate and validate phase diagrams as a function of model parameters and show how this approach can be extended to obstacles with piecewise straight contours. For curved obstacles, we arrive at a partial differential equation in the continuum limit, which again is in good agreement with the computer simulations. In all cases, we can identify the same two fundamentally different orientation patterns, but only within an appropriate reference frame, which is adjusted to the local orientation of the obstacle contour. Our results suggest that two fundamentally different network architectures compete with each other in growing actin networks, irrespective of obstacle geometry, and clarify how simulated and electron tomography data have to be analyzed for non-flat obstacle geometries.