Quantifying uncertainties in the microvascular transport of nanoparticles

Tae Rin Lee, M. Steven Greene, Zhen Jiang, Adrian M. Kopacz, Paolo Decuzzi, Wei Chen, Wing Kam Liu

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


The character of nanoparticle dispersion in the microvasculature is a driving factor in nanoparticle-based therapeutics and bio-sensing. It is difficult, with current experimental and engineering capability, to understand dispersion of nanoparticles because their vascular system is more complex than mouse models and because nanoparticle dispersion is so sensitive to in vivo environments. Furthermore, uncertainty cannot be ignored due to the high variation of location-specific vessel characteristics as well as variation across patients. In this paper, a computational method that considers uncertainty is developed to predict nanoparticle dispersion and transport characteristics in the microvasculature with a three step process. First, a computer simulation method is developed to predict blood flow and the dispersion of nanoparticles in the microvessels. Second, experiments for nanoparticle dispersion coefficients are combined with results from the computer model to suggest the true values of its unknown and unmeasurable parameters-red blood cell deformability and red blood cell interaction-using the Bayesian statistical framework. Third, quantitative predictions for nanoparticle transport in the tumor microvasculature are made that consider uncertainty in the vessel diameter, flow velocity, and hematocrit. Our results show that nanoparticle transport is highly sensitive to the microvasculature.

Original languageEnglish (US)
Pages (from-to)515-526
Number of pages12
JournalBiomechanics and Modeling in Mechanobiology
Issue number3
StatePublished - Jun 2014


  • Bayesian updating
  • Blood flow simulation
  • Nanoparticle transport
  • Uncertainty quantification

ASJC Scopus subject areas

  • Biotechnology
  • Mechanical Engineering
  • Modeling and Simulation


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