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.

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2 results in continuous random network in Keywords: continuous random network

Simplified Computational Model for Generating Biological Networks

Self Assembly of Model Polymers into Biological Random Networks

2 results in Keywords: continuous random network