Bioreactor Optimisation Through Coupled Fluid–Structure Interaction and Data-Driven Analysis: A Biomimetic Framework for Predicting Early Cell Attachment

Early cell attachment sets the baseline for proliferation, matrix deposition and construct maturation in tissue engineering, yet conventional bioreactors deliver simplified, quasi-static stimuli that poorly represent the multi-axial mechanical environment of native tissue. Here we reported a biomimetic computational–experimental framework for a humanoid-robot-driven perfusion bioreactor. The framework coupled a high-fidelity fluid–structure interaction (FSI) model of the culture chamber with a machine-learning model in order to describe the initial 3 h attachment phase, which preceded the application of robotic multi-axial loading. All experimental data reported here were therefore acquired during the static and perfused seeding phase; robotic loading was applied subsequently and was not part of the present dataset. Local mechanical and fluidic fields—wall shear stress, flow velocity, pressure, von Mises stress and scaffold strain—were mapped across ten scaffold subregions and paired with spatially resolved cell numbers obtained by PicoGreen double-stranded DNA quantification. Six regression algorithms were compared under identical conditions; a multilayer perceptron (MLP) gave the lowest mean absolute error (13.2%). Permutation importance ranked flow velocity, wall shear stress, von Mises stress and pressure as the dominant correlates of early adhesion. Parameter sweeps of perfusion rate and scaffold stiffness were model predictions rather than measured optima, and identified 1.4–1.6 mL/min and a moderately compliant scaffold as candidate operating points for experimental testing. The framework provides a transferable route to rational bioreactor and scaffold tuning with reduced empirical trial and error.