Generating Random Fiber Network Topologies That Mimic Previously Characterized Networks

Fibrous proteins, such as collagen and elastin, form the underlying structure of many soft tissues. These proteins form micrometer-scale networks of varying topology that play a role in governing the mechanics of tissues at larger length scales [1]. The relationship between a network’s topology and its mechanics, however, are poorly understood. This disconnect presents an important challenge in constructing realistic multiscale models of tissues informed by collagen network micrographs and subsequently reconstructed networks [2]. Accurate multiscale simulations may require thousands to millions of such unique networks. It is imperative that a method be developed to generate random networks that are functionally similar to ones derived experimentally. In the current study, we present a probabilistic method for generating de novo networks that mimic the mechanical properties of previously characterized networks. We chose Delaunay and Voronoi networks as model targets because they have been used successfully to model the mechanics of collagenous tissues [3] and since their topologies are well characterized. Understanding the role of topology in network mechanics is fundamental to building improved models of the mechanics of fibrous soft tissues — models that can aid in the rational design of engineered tissues or that can help assess the mechanical impact of damage or disease on native tissues.