We discuss a Monte Carlo method to simulate biological networks and compare to the underlying networks in experimental images.

No abstract

<p>A method to generate and simulate biological networks is discussed. An expanded Wooten-Winer-Weaire bond switching methods</p><p>is proposed which allows for</p><p>a distribution of node degrees in the network while conserving the mean average node degree.</p><p>The networks are characterised in terms of their polygon structure and assortativities (a measure</p><p>of local ordering). A wide range of experimental images are analysed and the underlying networks</p><p>quantified in an analogous manner. Limitations in obtaining the network structure are discussed.</p><p>A "network landscape" of the experimentally observed and simulated networks is constructed from the underlying metrics.</p><p>The enhanced bond switching algorithm is able to generate networks spanning the full range of experimental observations.</p>

We use a coarse grained polymer model and a simple graph representation to introduce defects into a biopolymer network, then cause them to rupture.

Networks of biopolymers occur often in nature, and are vulnerable to damage over time. In this work, a coarse grained model of collagen IV molecules is applied in a 2D hexagonal network and the mechanisms by which these networks can rupture are explored. The networks are stretched linearly in order to study their structural limits and mechanism of rupture over timescale of up to 100 microseconds. Metrics are developed to track the damage networks suffer over time, and qualitatively analyse ruptures that occur. Further simulations repeatedly stretch the networks sinusoidally to mimic the in vivo strains. Defects of increasing levels of complexity are introduced into an ordered network, and their effect on the rupturing behaviour of the biopolymer networks studied. The effect of introducing holes of varying size in the network, as well as strips of finite width to mimic surgical damage are studied. These demonstrate the importance of the flexibility of the networks to preventing damage.