Quantifying uncertainties in the microvascular transport of nanoparticles
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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.
KeywordsBlood flow simulation Nanoparticle transport Uncertainty quantification Bayesian updating
W.K.L. acknowledges the support of CMMI-0856492 and CMMI-0856333. This research used resources of the QUEST cluster at Northwestern University and the Argonne Leadership Computing Facility at Argonne National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under contract DE-AC02-06CH11357. W.K.L. acknowledges the support of the World Class University Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (R33-10079). M.S.G. warmly thanks the National Science Foundation GRFP program for their support. Z.J. and W.C. are supported by the National Science Foundation Grant CMMI-1233403. P.D. acknowledges the partial support by the Cancer Prevention Research Institute of Texas (CPRIT RP110262) and the USA National Cancer Institute (U54CA143837 and U54CA151668).
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