2025 AIChE Annual Meeting
(393f) Numerical Methods to Investigate the Morphological Determinants of Glomerular Permeability in Healthy and Diseased Tissue
Authors
2D and 3D numerical models are used to identify the structural determinants of kidney filtration using open-source software FEbio (Finite Elements for Biomechanics). Using the CFD module, we investigated how the viscous fluid interacts between the porous-hydrated biological tissues present in the glomerulus. For the 2D model, we have replaced the complex 3D slit diaphragm with a “simplified” slit diaphragm, modeled as an isotropic porous medium.
Within our simulation, we take recent ultrastructural observation of the “slit” diaphragm to investigate how the structure influences hydraulic permeability and the forces applied along the surface of the slit diaphragm and foot processes. Idealized “subunits” of the kidney filtration barrier are created using parametric CAD models. Furthermore, we include documented ultrastructural parameters from diseased kidney tissue to determine hydrodynamic properties changes as a result of disease morphology [3]. The flow conditions of our numerical simulation reflect the cellular microenvironment of the glomerular capillary wall. These results are compared against previous published numerical models for hydraulic conductivity and experimental results [2,3]. In the future, we will implement the biphasic fluid-solid interactions (BFSI) module to investigate how ultrafiltration forces induce deformation in healthy and diseased glomerular tissue.
[1] Rice, W. L. et al. “High resolution helium ion scanning microscopy of the rat kidney.” PloS One, vol. 8,3 (2013): e57051. doi:10.1371/journal.pone.0057051
[2] Drumond, M. C. and Deen, W. M. “Structural determinants of glomerular hydraulic permeability.” American Journal of Physiology Renal Physiology, vol. 266,1 Pt 2 (1994): F1-12. doi:10.1152/ajprenal.1994.266.1.F1
[3] Remuzzi, A. et al. “Role of ultrastructural determinants of glomerular permeability in ultrafiltration function loss.” JCI Insight, vol. 5,13 (2020): e137249. doi:10.1172/jci.insight.137249