Complete re-endothelialization of acellular lung scaffolds remains a significant barrier for the clinical application of regenerated lungs. Current methods of delivering endothelial cells have led to insufficient coverage of the pulmonary vasculature. Understanding how reseeding parameters affect cell deposition is necessary to optimize lung re endothelialization. In this thesis, we created a computational fluid dynamics model featuring an inertial particle deposition function to quantify the deposition of cells in mouse lung vasculature for re-endothelialization. Our novel inertial algorithm demonstrated a significant reduction in cell seeding efficiency error compared to two established particle deposition algorithms when validated with experiments. With this newly presented model, cell seeding efficiency and uniformity increased with higher flow rates. Cell seeding efficiency was further improved by utilizing a surrounding parenchymal pressure. Modulating the parenchymal pressure also enabled the targeting of cell deposition locations. This work lays the foundation to computationally optimize lung regeneration towards clinical use.