Cell Inertia: Predicting Cell Distributions in Lung Vasculature to Optimize Re-endothelialization

Jason K.D. Chan, Eric A. Chadwick, Daisuke Taniguchi, Mohammadali Ahmadipour, Takaya Suzuki, David Romero, Cristina Amon, Thomas K. Waddell, Golnaz Karoubi, Aimy Bazylak

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


We created a transient computational fluid dynamics model featuring a particle deposition probability function that incorporates inertia to quantify the transport and deposition of cells in mouse lung vasculature for the re-endothelialization of the acellular organ. Our novel inertial algorithm demonstrated a 73% reduction in cell seeding efficiency error compared to two established particle deposition algorithms when validated with experiments based on common clinical practices. We enhanced the uniformity of cell distributions in the lung vasculature by increasing the injection flow rate from 3.81 ml/min to 9.40 ml/min. As a result, the cell seeding efficiency increased in both the numerical and experimental results by 42 and 66%, respectively.

Original languageEnglish
Article number891407
JournalFrontiers in Bioengineering and Biotechnology
Publication statusPublished - 2022 Apr 27


  • cell seeding
  • computational fluid dynamics
  • inertia
  • lung regeneration
  • lung tissue engineering
  • re-endothelialization


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