High‐fidelity Modeling of Blood Flow in Physiologically Realistic Microvascular Networks

Abstract

High-fidelity in silico modeling of blood flow in microvascular networks continues to be a challenge. Blood in small vessels behaves as a concentrated suspension of red blood cells (RBC), which are extremely deformable. Computational modeling must resolve the dynamics and deformation of individual RBC while simultaneously extending to a dense suspension. Most cellular-scale in silico models to date have considered blood flow in simple geometries, such as straight tubes or channels, which do not represent physiological conditions, since the architecture of vascular networks in vivo is characterized by bifurcating, merging, and winding vessels. Such geometrical differences result in significant deviations in hemodynamics between that achieved in a straight conduit versus a vascular network. We have recently developed a high-fidelity, multiscale, 3D computational model of flow of deformable RBCs in complex microvascular networks resembling in vivo-like architectures. The vascular networks are designed following in vivo images and data, and are comprised of bifurcating, merging, and tortuous vessels. Our model accurately resolves the large deformation and dynamics of each individual RBC, while simultaneously retaining complex geometric details of the vascular architecture. The model is versatile, and can consider networks irrespective of topological/geometrical complexities. Flow can be driven by either pressure or flow boundary conditions of physiological values. Cell distributions naturally develop in the networks from an initial random distribution, eventually resulting in a heterogeneous cell distribution as observed in vivo. Extreme deformation and a wide range of RBC shapes are observed that are in agreement with in vivo observations. Quantitative comparisons were made with in vivo data using hemodynamic quantities such as flow resistance, wall shear stress, cell-free layer, etc., and good agreement was observed. The model can also consider white blood cells and platelets, and receptor-mediated adhesion between vascular wall and cells. The model provides a full 3D quantification of hemodynamic quantities with unprecedented details that are not available in experimental measurements. For example, the model allows full 3D characterizations of the near-wall cell-free layer (CFL) and wall shear stress (WSS) and gradients (WSSG). It predicts that CFL becomes more asymmetric along vessel lengths with increasing vessel tortuosity. While WSS itself decreases from arterial to venous sides, the model predicts that WSS in presence of RBCs increases more than that without RBCs on the venous sides and at capillary bifurcations. This implies that the effect of RBCs on WSS is more enhanced at specific locations in a microvascular network. WSSG is also predicted to be much higher in presence of the RBCs, and increase at capillary bifurcations. The model is used further to study transport and deposition of nanoparticle drug carriers in the microvasculature. Results show that nanoparticle accumulation is highly nonuniform across the vasculature with the highest accumulation occurring in bifurcations and the lowest in venules. Support or Funding Information Supported by the National Science Foundation In silico model of flow of blood cells in a microvascular network. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.