Predicting neurite extension for varying extracellular matrix stiffness and topography

Neurite extension is a dynamic process and is dependent on the microenvironment. The mechanical properties of the extracellular matrix (ECM), such as stiffness and topography influence the microenvironment and affects neurite extension; however, the mechanistic basis for this dynamic response of neurite extension remains elusive. In this study, we develop a computational model that predicts neurite extension dynamics process as the stiffness and patterned topography of ECM changes. The model includes the contribution of receptors integrin and neural cellular adhesion molecule toward the growth of neurite tip. We use non-linear finite element analysis (FEA) to model the neuronal cell, neurite, and the ECM, which is then coupled to the force-deformation receptor properties obtained from molecular dynamics simulations. Using an empirical relation, we develop a neurite extension algorithm that simulates the dynamic process of growth cone induced by growth cone extension, receptor density, and rupture. We investigate the dependence of neurite extension on ECM stiffness using three distinct materials, the effect of width and spacing of continuous (cylindrical) and discontinuous (pillar) patterned topography, as well as the topography steepness and stiffness gradient. We find that an increasing stiffness and width of patterned topography results in increased neurite extension, but the magnitude of the increase differs depending on the growth cone extension and receptor density between them. These findings will aid in vitro studies in determining an ECM with appropriate mechanical properties, such as stiffness and topography that will improve neurite extension, thus resulting in the formation of functional neurons.