A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles.
A Bayesian hierarchical model for maximizing the vascular adhesion of nanoparticles.
复制标题
用于最大化纳米颗粒血管粘附的贝叶斯分层模型。
DOI：
10.1007/s00466-013-0957-1
复制
发表时间：
2014
影响因子：
4.1
通讯作者：
Decuzzi,Paolo
中科院分区：
文献类型：
--
作者：
Fronczyk,Kassandra;Guindani,Michele;Vannucci,Marina;Palange,Annalisa;Decuzzi,Paolo
全身注射的纳米颗粒的复杂血管动力学和壁沉积由其几何性质（大小、形状）和生物物理参数（配体-受体键类型和表面密度、局部剪切速率）调节。虽然已经开发了复杂的计算模型来捕获纳米颗粒的血管行为，但人们越来越认识到，纯粹的确定性方法（其中控制参数是先验已知的，并基于物理特性最终描述行为）可能过于限制，无法准确反映自然过程。在这里，提出了一种新的计算框架，通过耦合的物理决定的血管粘附的纳米粒子与随机模型。特别是，两个控制参数（即配体-受体键长和纳米颗粒上的配体表面密度）被视为两个随机量，其值不是先验固定的，而是以一定的概率在定义的区间内变化。该方法用于预测在不同的流动条件下，在平行板流动室中，具有从50至90的剪切速率的具有不同半径（从750至6，000 nm）的球形纳米颗粒的沉积。结果表明，所得到的随机模型可以更准确地预测实验数据比原来的确定性模型。这种方法允许人们通过考虑实验和内在生物学的不确定性来增加任何自然过程的数学模型的预测能力。
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. 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.