A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles

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dc.citation.firstpage539en_US
dc.citation.issueNumber3en_US
dc.citation.journalTitleComputational Mechanicsen_US
dc.citation.lastpage547en_US
dc.citation.volumeNumber53en_US
dc.contributor.authorFronczyk, Kassandraen_US
dc.contributor.authorGuindani, Micheleen_US
dc.contributor.authorVannucci, Marinaen_US
dc.contributor.authorPalange, Annalisaen_US
dc.contributor.authorDecuzzi, Paoloen_US
dc.date.accessioned2015-03-16T16:56:10Zen_US
dc.date.available2015-03-16T16:56:10Zen_US
dc.date.issued2014en_US
dc.description.abstractThe 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.en_US
dc.identifier.citationFronczyk, Kassandra, Guindani, Michele, Vannucci, Marina, et al.. "A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles." <i>Computational Mechanics,</i> 53, no. 3 (2014) Springer: 539-547. http://dx.doi.org/10.1007/s00466-013-0957-1.en_US
dc.identifier.doihttp://dx.doi.org/10.1007/s00466-013-0957-1en_US
dc.identifier.urihttps://hdl.handle.net/1911/79353en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Springer.en_US
dc.subject.keywordbayesian inferenceen_US
dc.subject.keywordnanomedicineen_US
dc.subject.keywordvascular adhesionen_US
dc.subject.keyworduncertainty quantificationen_US
dc.titleA Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticlesen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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