Self assembly of model polymers into biological random networks.

Matthew H J Bailey,Mark Wilson

doi:10.1016/j.csbj.2021.02.001

Abstract

The properties of biological networks, such as those found in the ocular lens capsule, are difficult to study without simplified models. Model polymers are developed, inspired by "worm-like" curve models, that are shown to spontaneously self assemble to form networks similar to those observed experimentally in biological systems. These highly simplified coarse-grained models allow the self assembly process to be studied on near-realistic time-scales. Metrics are developed (using a polygon-based framework) which are useful for describing simulated networks and can also be applied to images of real networks. These metrics are used to show the range of control that the computational polymer model has over the networks, including the polygon structure and short range order. The structure of the simulated networks are compared to previous simulation work and microscope images of real networks. The network structure is shown to be a function of the interaction strengths, cooling rates and external pressure. In addition, "pre-tangled" network structures are introduced and shown to significantly influence the subsequent network structure. The network structures obtained fit into a region of the network landscape effectively inaccessible to random (entropically-driven) networks but which are occupied by experimentally-derived configurations.

Highlights

Two-dimensional (2D) networks are critically important in biology — from basement membranes surrounding muscles, to the lens capsule of the eye
The ubiquity of these systems makes understanding the formation and ageing of the 2D networks a key research interest, often with the aim that an understanding of the structure and properties of biological networks can lead to the development of synthetic materials that mimic natural biological networks
The self-assembled networks show polygon edge count distributions which do not resemble the numerical maximum entropy model; this is visible in Fig. 3, which compares polygon edge counts from a polymer simulated and the polygon edge counts from maximum entropy model distribution

Summary

Introduction

Two-dimensional (2D) networks are critically important in biology — from basement membranes surrounding muscles, to the lens capsule of the eye. Some three-dimensional (3D) networks are composed of stacked layers of 2D networks in which the intra-layer interactions are significantly larger than those of the inter-layer. The ubiquity of these systems makes understanding the formation and ageing of the 2D networks a key research interest, often with the aim that an understanding of the structure and properties of biological networks can lead to the development of synthetic materials that mimic natural biological networks. One example of a 2D biological network is the collagen IV network in the ocular lens capsule. Artificial lens capsules can be implanted after cataract surgery, which can improve the ability to accommodate focal depths in elderly people [2]. The loss of focal accommodation in human senescence has been linked to collagen

Methods

Results

Conclusion