Finite element modelling for elucidating surface topography influence on cell-substrate interaction on fatty acid-modified PCL substrate.

Ganguly, Rajdeep; Choudhury, Sandeep; Gupta, Abhisek; Chowdhury, Amit Roy; Barui, Ananya · J Biomech · 2026

biomechanical · Level V

Where this comes from

Abstract

Surface topography acts as a potent determinant of cellular biomechanical behaviour. In this study, poly(ε-caprolactone) (PCL) matrices were chemically modified with oleic acid (OA) to tailor the surface roughness and computationally investigate the cell-substrate interactions. Mechanical characterisation involving nanoindentation study demonstrated a reduction in the Young's modulus from PCL to PCL-OA, confirming the role of OA as a plasticiser. The incorporation of the lipid in the matrix composition enhanced the matrix hydrophilicity, which was confirmed by the ATR-FTIR spectroscopy and surface wettability analysis, respectively. Atomic force microscopy (AFM) was performed to obtain the topographic features and the data are incorporated for developing cell-substrate models for finite element analysis (FEA). FE simulations were conducted using ANSYS Workbench, which involved modelling cell-substrate interaction for three cell models - a hemispherical cell model, a WI-38 fibroblast model, and an A549 (lung adenocarcinoma) model. Each cytotype was idealised as an elastic continuum under physiologically relevant loading. The combination of empirical data with simulated domains correlates the micro- and nanoscale roughness of the scaffolds with the cellular. The distinct deformation patterns and strain distributions are observed for different cell types. The variations in substrate stiffness and roughness distinctly alter the contour of mechanical parameters within cells at the cell-substrate interface. The integration of empirical data on surface topography from AFM and FEA thus facilitates the modelling of a predictive framework to design surface-engineered matrices enable assessment of the biomechanical compatibility for multiple cellular phenotypes.