Self Assembly of Model Polymers into Biological Random Networks

29 October 2020, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

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.

Keywords

continuous random network
self assemble
polygon statistics
polygon distributions
collagen network
worm-like curve
polymer model

Supplementary materials

Title
Description
Actions
Title
nsa-supplementary-information
Description
Actions

Comments

Comments are not moderated before they are posted, but they can be removed by the site moderators if they are found to be in contravention of our Commenting Policy [opens in a new tab] - please read this policy before you post. Comments should be used for scholarly discussion of the content in question. You can find more information about how to use the commenting feature here [opens in a new tab] .
This site is protected by reCAPTCHA and the Google Privacy Policy [opens in a new tab] and Terms of Service [opens in a new tab] apply.