TY - JOUR

T1 - A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles

AU - Fronczyk, Kassandra

AU - Guindani, Michele

AU - Vannucci, Marina

AU - Palange, Annalisa

AU - Decuzzi, Paolo

N1 - Funding Information:
K. Fronczyk partially supported by VIGRE Grant DMS-0739420. K. Fronczyk, M. Guindani, and P. Decuzzi are partially supported by NIH/NCI Grant U54CA15166803.

PY - 2014/3

Y1 - 2014/3

N2 - 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 nanoparticleswith 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.

AB - 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 nanoparticleswith 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.

KW - Bayesian inference

KW - Nanomedicine

KW - Uncertainty quantification

KW - Vascular adhesion

UR - http://www.scopus.com/inward/record.url?scp=84898596899&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84898596899&partnerID=8YFLogxK

U2 - 10.1007/s00466-013-0957-1

DO - 10.1007/s00466-013-0957-1

M3 - Article

AN - SCOPUS:84898596899

SN - 0178-7675

VL - 53

SP - 539

EP - 547

JO - Computational Mechanics

JF - Computational Mechanics

IS - 3

ER -