Soft tissues are comprised of underlying fiber networks of collagen and other fibrous proteins and biopolymers. Thus, the ability to model the deformation of fiber networks is critical to understanding the mechanics of tissues in vivo and in vitro [1]. Complicating the issue, protein fiber networks are comprised of a range of different topologies that behave differently under load. There is a clear need for a method to derive network parameters that characterize the network and allow for the prediction of their behavior. In this study, we characterized several different random fiber network types based on their intrinsic mechanical and topological properties. Such characterization would improve our ability to select microscale network topologies that match the mechanical properties we observe in healthy and diseased native tissues [2]. It would also improve our ability to discern the outcome of microstructural changes in tissues (such as from remodeling or injury) on their overall mechanics.

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