Coarse-grained elastic network modelling: A fast and stable numerical tool to characterize mesenchymal stem cells subjected to AFM nanoindentation measurements

The knowledge of the mechanical properties is starting point to study mechanobiology mesenchymal stem cells and understand relationships linking biophysical stimuli cellular differentiation process. In experimental biology, Atomic Force Microscopy (AFM) a common technique for measuring these properties. this paper we present an alternative approach extracting parameters, such as Young's modulus cell components, from AFM nanoindentation measurements conducted on human cells. virtual environment, geometrical model was converted in highly deformable Coarse-Grained Elastic Network Model (CG-ENM) reproduce real experiment retrieve related force-indentation curve. An ad-hoc optimization algorithm perturbed local stiffness values springs, subdivided several functional regions, until computed curve replicated one. After matching, extraction global moduli performed different samples. capable distinguish material subcellular components cortex cytoskeleton. numerical results predicted with elastic network were then compared those obtained hertzian contact theory Finite Element Method (FEM) same case studies, showing optimal agreement reduced computational cost. proposed simulation flow seems be accurate, fast stable method understanding behavior soft biological materials, even levels detail. Moreover, modelling allows shortening times approximately 33% time required by traditional FEM using elements size comparable that springs.