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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 (μm) models provide insights into macroscale (mm) tissue behavior. However, there is still no clear way to systematically incorporate detailed experimentally observed microstructural changes into in silico models of biological networks. In this work, we develop a novel framework to generate topologically-driven discrete fiber networks using high-resolution images that account for how environmental changes during polymerization influence the resulting structure. Leveraging these networks, we generate models of interconnected load-bearing fiber components that exhibit softening under compression and are bending-resistant. The generative topology framework enables control over network-level features, such as fiber volume fraction and cross-link density, along with fiber-level properties, like length distribution, to simulate changes driven by different polymerization conditions. We validate the robustness of our simulations against experimental data in a collagen-specific study case where we examine nonlinear elastic responses of collagen networks across varying conditions. TopoGEN provides a versatile tool for tissue biomechanics and engineering, helping to bridge microstructural insights and bulk mechanical behavior by linking image-derived microstructural topological organization to soft tissue mechanics.
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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 (μm) models provide insights into macroscale (mm) tissue behavior. However, there is still no clear way to systematically incorporate detailed experimentally observed microstructural changes into in silico models of biological networks. In this work, we develop a novel framework to generate topologically-driven discrete fiber networks using high-resolution images that account for how environmental changes during polymerization influence the resulting structure. Leveraging these networks, we generate models of interconnected load-bearing fiber components that exhibit softening under compression and are bending-resistant. The generative topology framework enables control over network-level features, such as fiber volume fraction and cross-link density, along with fiber-level properties, like length distribution, to simulate changes driven by different polymerization conditions. We validate the robustness of our simulations against experimental data in a collagen-specific study case where we examine nonlinear elastic responses of collagen networks across varying conditions. TopoGEN provides a versatile tool for tissue biomechanics and engineering, helping to bridge microstructural insights and bulk mechanical behavior by linking image-derived microstructural topological organization to soft tissue mechanics.
Thrombus composition and microstructure play a critical role in determining the treatment success for thrombus-related diseases such as ischemic stroke and deep vein thrombosis. However, no in vivo diagnostic method can fully capture thrombus microstructure yet, hindering personalized treatment. Photoacoustic imaging is uniquely positioned to provide information on thrombi composition as it relays optical absorption information from diffuse photons at acoustic propagation depths. Computational modeling enables systematic exploration of microstructural effects on imaging signals, offering insights into developing improved in vivo diagnostic techniques. However, no photoacoustic simulation platform can model microstructural features within centimeter-scale phantoms at reasonable computational cost. In this work, we present recursive iterative fibrin network emulation (REFINE), a topology-driven framework for generating in silico thrombi that replicate their key microstructural traits. Unlike existing methods, REFINE enables controlled, recursive optimization of thrombus topology, making it suitable for accurate photoacoustic modeling and potentially powerful for biomechanical analyses beyond this study. These digital thrombi are embedded into a multiscale photoacoustic simulation platform that bridges microscale acoustic modeling with macroscale thrombus geometries, enabling efficient and realistic simulation of photoacoustic signal responses. We created unique representation of thrombi microstructure for various compositions and porosities. Our simulation framework effectively links microstructural features to macroscale imaging outcomes, in agreement with previous empirical studies. Our simulation results demonstrate that thrombus microstructure significantly affects photoacoustic spectral responses and can be reliably modeled in silico. These findings highlight the potential of a multiscale photoacoustic simulation approach as a powerful tool for characterizing tissue microstructure and demonstrate the utility of our computational framework for in silico thrombi analysis and the development of diagnostic imaging strategies.
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Thrombus composition and microstructure play a critical role in determining the treatment success for thrombus-related diseases such as ischemic stroke and deep vein thrombosis. However, no in vivo diagnostic method can fully capture thrombus microstructure yet, hindering personalized treatment. Photoacoustic imaging is uniquely positioned to provide information on thrombi composition as it relays optical absorption information from diffuse photons at acoustic propagation depths. Computational modeling enables systematic exploration of microstructural effects on imaging signals, offering insights into developing improved in vivo diagnostic techniques. However, no photoacoustic simulation platform can model microstructural features within centimeter-scale phantoms at reasonable computational cost. In this work, we present recursive iterative fibrin network emulation (REFINE), a topology-driven framework for generating in silico thrombi that replicate their key microstructural traits. Unlike existing methods, REFINE enables controlled, recursive optimization of thrombus topology, making it suitable for accurate photoacoustic modeling and potentially powerful for biomechanical analyses beyond this study. These digital thrombi are embedded into a multiscale photoacoustic simulation platform that bridges microscale acoustic modeling with macroscale thrombus geometries, enabling efficient and realistic simulation of photoacoustic signal responses. We created unique representation of thrombi microstructure for various compositions and porosities. Our simulation framework effectively links microstructural features to macroscale imaging outcomes, in agreement with previous empirical studies. Our simulation results demonstrate that thrombus microstructure significantly affects photoacoustic spectral responses and can be reliably modeled in silico. These findings highlight the potential of a multiscale photoacoustic simulation approach as a powerful tool for characterizing tissue microstructure and demonstrate the utility of our computational framework for in silico thrombi analysis and the development of diagnostic imaging strategies.
