TopoGEN: topology-driven microstructure generation for in silico modeling of fiber network mechanics

Abstract: The fields of mechanobiology and biomechanics are expanding our understanding of the complex behavior of soft biological tissues across multiple scales. Given the intricate connection between tissue microstructure and its macroscale mechanical behavior, unraveling this mechanistic relationship remains an ongoing challenge. Reconstituted fiber networks serve as valuable in vitro models to simplify the intricacy of in vivo systems for targeted investigations. Concurrently, advances in imaging enable microstructure visualization and, through generative pipelines, modeling as discrete element networks. These mesoscale models provide insights into macroscale tissue behavior. However, a systematic study of how microstructural variations influence nonlinear tissue mechanics is still lacking. In this work, we develop a novel framework to generate topologically-driven discrete fiber networks. Leveraging these networks, we generate models of interconnected load-bearing fiber components that exhibit softening under compression and are bending-resistant. By virtually replicating microstructural features of reconstituted collagen networks, such as fiber volume fractions and cross-link concentration, we evaluate the robustness of our simulations. Analyzing the nonlinear elastic behavior at varying polymerization temperatures, we find consistency between the in silico results and in vitro data from the literature. We extend our investigation beyond empirically measurable factors to explore microstructural effects at the single fiber level (i.e., fibril morphology and stiffness) that are challenging to investigate experimentally. TopoGEN allows us to mechanistically explore localized microstructural phenomena and relate microstructural changes to the bulk mechanical response of soft biological materials, hence providing an indispensable tool to advance the fields of tissue biomechanics and engineering.