Modeling Flow-Induced Deformation of Hydrogel Stromal Compartment in a Microphysiological Device

• Victoria McKeown, Biomedical Engineering, University of Delaware

• Jason Gleghorn, Biomedical Engineering, University of Delaware

Modeling Flow-Induced Deformation of  Hydrogel Stromal Compartment in a Microphysiological Device

Victoria McKeown, Filipa Ribeiro, Katherine M. Nelson, Jason P. Gleghorn

Organ-on-a-chip (OoC) platforms aim to replicate complex physiological environments more accurately than traditional 2D culture models, but many lack a stromal compartment, an essential component in many tissues. Stromal architecture and extracellular matrix (ECM) mechanics critically influence cell function, including shape, signaling, and communication. Our lab developed an OoC device with an integrated ECM-based stromal layer, and to better understand how perfusion-based forces deform this region, we created a finite element model using FEBio Studio 2.8.1. The model simulates a collagen hydrogel (15.5 × 0.5 × 3.0 mm) embedded in the OoC and subjected to vertical perfusion with a top surface pressure of 0.4 kPa and a bottom pressure of 0.2 kPa. Biphasic material properties were applied using a coupled Mooney-Rivlin model, varying the stiffness (c1) and nonlinear response (c2) parameters. Boundary conditions fixed the hydrogel at the sides, and meshing was performed using Hex8 elements. We tested four collagen formulations (A–D) and quantified z-direction displacement after 50 time steps to assess deformation under fluid flow. Simulations showed predictable deformation of the hydrogel toward the lower pressure region, with equilibrium displacement decreasing with increased stiffness: from −0.0691 mm (A) to −0.0254 mm (D). These results validate the model’s ability to predict biomechanical responses to perfusion. Ongoing work involves validating simulations with rheological testing and imaging of collagen hydrogels in the actual OoC device under flow, using embedded fluorescent beads to track displacement. This work provides a digital framework for understanding ECM deformation in microphysiological systems, offering predictive capabilities that enhance OoC design, reduce experimental burden, and enable more tunable, physiologically relevant in vitro models.