These processes may also be affected by changes in external stimuli that can result from conditions such as injury, disease, or therapeutic treatments. Growth and remodeling of tissues are critical biological processes that can be influenced by biological stimuli such as cell type and density, hormones and other growth factors, and mechanical stimuli. In summary, we illustrate here the importance of the choice of implementation approach for modeling growth, provide a framework for converting models between implementation approaches, and highlight important considerations for comparing results in prior work and developing new models of tissue growth. Node- and element-based approaches matched marginally better when the conversion coefficient to relate the approaches was optimized based on the results of initial simulations, rather than using the theoretically predicted conversion coefficient (median difference in node position 0.042 cm versus 0.052 cm, respectively). The node-based approach was unaffected by modulus. We found that material properties (modulus) affected growth in the element-based approach, with growth completely restricted for high modulus values relative to the growth stimulus, and no restriction for low modulus values. We used a previously reported node-based approach implemented via thermal expansion and an element-based approach implemented via osmotic swelling, and we derived a mathematical relationship to relate the growth resulting from these approaches. This study directly compared node-based and element-based approaches to understand the isolated impact of implementation method on growth predictions by simulating growth of a bone rudiment geometry, and determined what conditions produce similar results between the approaches. Previous work has simulated growth using node-based or element-based approaches, and this implementation choice may influence predicted growth, irrespective of the applied growth model. Finite element analysis is a useful tool to model growth of biological tissues and predict how growth can be impacted by stimuli. Ĝalculated stiffness measurements (.xlsx)Ĭode developed by Ridhi Sahani in publication for the Journal of Applied Physiology.Ĭitation will be updated upon publication, see pre-print.ğiber directions per image window (.xlsx).See below for outline of components of this code: See ‘Test_complete’ for example setup and outputs. Note that the folder structure is set up such that multiple images can be processed at once with input image in \Images, model saved in \Models, and outputs saved in \Outputs. ‘Test_images’ contains example SEM images and ‘Test_complete’ contains the results after running this code. The files here contain all of the required functions and example inputs for this analysis. FEMs are run in FeBio 2.9 and must be in an FeBio compatible format (.xml). A baseline FEM is required, with model geometry matching the size of the SEM images and the boundary conditions and material properties of interest. In this framework, fiber directions are first measured from the SEM images and then used to assign fiber directions in the FEM. This code was developed to generate scanning electron microscopy (SEM) image-based finite-element models (FEM) to explore the influence of collagen fiber-level organization on tissue-level mechanics. Scanning electron microscopy (SEM) image based finite-element models (FEM)
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