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dc.contributor.authorFronczyk, Kassandra
Guindani, Michele
Vannucci, Marina
Palange, Annalisa
Decuzzi, Paolo
dc.date.accessioned 2015-03-16T16:56:10Z
dc.date.available 2015-03-16T16:56:10Z
dc.date.issued 2014
dc.identifier.citation Fronczyk, Kassandra, Guindani, Michele, Vannucci, Marina, et al.. "A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles." Computational Mechanics, 53, no. 3 (2014) Springer: 539-547. http://dx.doi.org/10.1007/s00466-013-0957-1.
dc.identifier.urihttps://hdl.handle.net/1911/79353
dc.description.abstract The complex vascular dynamics and wall deposition of systemically injected nanoparticles is regulated by their geometrical properties (size, shape) and biophysical parameters (ligand–receptor bond type and surface density, local shear rates). Although sophisticated computational models have been developed to capture the vascular behavior of nanoparticles, it is increasingly recognized that purely deterministic approaches, where the governing parameters are known a priori and conclusively describe behaviors based on physical characteristics, may be too restrictive to accurately reflect natural processes. Here, a novel computational framework is proposed by coupling the physics dictating the vascular adhesion of nanoparticles with a stochastic model. In particular, two governing parameters (i.e. the ligand–receptor bond length and the ligand surface density on the nanoparticle) are treated as two stochastic quantities, whose values are not fixed a priori but would rather range in defined intervals with a certain probability. This approach is used to predict the deposition of spherical nanoparticles with different radii, ranging from 750 to 6,000 nm, in a parallel plate flow chamber under different flow conditions, with a shear rate ranging from 50 to 90  s−1 . It is demonstrated that the resulting stochastic model can predict the experimental data more accurately than the original deterministic model. This approach allows one to increase the predictive power of mathematical models of any natural process by accounting for the experimental and intrinsic biological uncertainties.
dc.language.iso eng
dc.publisher Springer
dc.rights This is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Springer.
dc.title A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles
dc.type Journal article
dc.contributor.funder National Science Foundation
dc.contributor.funder National Institutes of Health/National Cancer Institute
dc.citation.journalTitle Computational Mechanics
dc.subject.keywordbayesian inference
nanomedicine
vascular adhesion
uncertainty quantification
dc.citation.volumeNumber 53
dc.citation.issueNumber 3
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1007/s00466-013-0957-1
dc.identifier.pmcid PMC4018201
dc.identifier.pmid 24833810
dc.identifier.grantID VIGRE DMS-0739420 (National Science Foundation)
dc.identifier.grantID U54CA15166803 (National Institutes of Health/National Cancer Institute)
dc.type.publication post-print
dc.citation.firstpage 539
dc.citation.lastpage 547


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