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Quantification and Modeling of Spatially Heterogeneous Phenomena in Biological Materials

Emma Lejeune, Boston University

Manuel Rausch, University of Texas-Austin

Johannes Weickenmeier, Stevens Institute

Mona Eskandari, University of California-Riverside

Biological materials are spatially heterogeneous on the sub-cellular, cellular, tissue, and organ scales. This spatial heterogeneity can be investigated experimentally through techniques such as microscopy, medical imaging, nanoindentation, and full field measurements of material response. For example, we can visualize spatially dependent fiber directions in cardiac muscle with histology, and we can probe the variable stiffness of regions of brain tissue with nanoindentation. Even in structurally and/or referentially homogeneous tissues, heterogeneous phenomena such as growth and remodeling may induce spatial-dependency. Critically, computational tools, such as constitutive modeling, inverse methods, surrogate modeling, and machine learning, are often required to meaningfully interpret these experimental results. And, advancing methods for capturing spatially heterogeneous (and often uncertain) properties in computational models of biological systems is an area of active research. 

This minisymposia will bring together scientists working on quantifying and modeling spatially heterogeneous phenomena in multiple types of biological tissue. We hope to stimulate discussion surrounding experimental and computational methods, and explore novel techniques for linking experimental characterization to computational models. Topics include (but are certainly not limited to):
- Experimental methods for characterizing tissue microstructure 
- Statistical models of material microstructure
- Microstructure-informed constitutive modeling 
- Material property extraction through inverse modeling
- Inhomogeneous growth and remodeling
- Multi-layered tissue
- Machine learning based approaches to modeling heterogeneous materials 
- Uncertainty quantification and sensitivity analysis