Collaborative Research: A Molecular-to-Continuum, Data-Driven Strategy for Mucus Transport Modeling

协作研究：粘液运输建模的分子到连续体、数据驱动策略

• 批准号: 1412844
• 负责人: M Forest
• 金额: $ 10万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1412844&HistoricalAwards=false
• 关键词: Collaborative; Research; Molecular; Continuum; Data

The project develops predictive mathematical theory and computational tools for both the flow of lung airway liquids and the diffusion of inhaled particles (pathogens, particulates, drug carrier particles) that are deposited in human airways. The predictive mathematical modeling and computational tools are developed on the basis of experimental data. The experimental data on particle diffusion in mucus and the physical properties that govern mucus flow transport are collected on human bronchial epithelial mucus from lung cultures and clinical patients. This experimental-theoretical-computational strategy has the promise for direct applications in clinical treatment of humans with diverse lung diseases and disorders, both for assessment of disease and for design of physical therapy and drug treatment strategies. Mathematically, these models and simulation tools promise to provide insight into mean mucus flow properties from physiological forcing (cilia and air drag from breathing and cough) and also to resolve microstructural changes in the mucus molecular network. These tools can offer insight into the biophysical differences in mucus during disease and disease progression, which are keys to a predictive design of physical and drug therapies. When integrated with clinical knowledge, this modeling will provide the capability to infer flow and diffusive transport properties of mucus samples and healthy and disease conditions, and the capability to test therapies to reinstate mucus clearance on an individualized patient basis.This project develops a data-driven strategy for the modeling of lung mucus transport, linking stochastic molecular kinetic processes, evolution equations for microstructure-based stresses, and momentum equations for flow. The experimental data consists of stochastic (entropic fluctuations) and deterministic (controlled forcing) probes of mucus microstructure, together with high-resolution microscopy of mucus transport in cell cultures derived from human lung tissue. These rich data sets provide unprecedented probes of the broad mucus relaxation spectrum arising from single mucin molecules, their entanglement network in mucus gels, transient mucin crosslinking, and chain scission kinetics. Cell cultures afford insights into cilia-driven flow transport of mucus, and provide a laboratory setting to explore molecular-to-macroscopic consequences of imposed physical stresses and chemical dosing. To translate this remarkable data into a predictive understanding of mucus transport, a modeling platform is studied that resolves molecular-to-continuum processes of mucus, both near and far from equilibrium. Our strategy begins with stochastic time series of passive microbead probes in mucus gels of various concentrations, to solve the inverse and direct problems for linear (near equilibrium) viscoelastic characterization. Next, active microbead data for the same set of concentrations is used over a range of controlled magnetic forces to determine nonlinear thresholds and the signatures of non-equilibrium behavior at the microscale. These data directly feed into the main objective of this project, which is the formulation of a new microscopic-macroscopic constitutive law for mucus. This formulation is proposed to interpret the linear and nonlinear data, and to integrate molecular-to-continuum processes into a new mucus transport model. A numerical strategy is under study for direct numerical simulations and comparison with data from each type of experiment.

该项目为肺气道液体的流动和沉积在人体气道中的吸入颗粒（病原体，颗粒，药物载体颗粒）的扩散开发预测数学理论和计算工具。 预测的数学模型和计算工具的基础上开发的实验数据。 从肺培养物和临床患者的人支气管上皮粘液中收集关于粘液中颗粒扩散和支配粘液流动运输的物理性质的实验数据。 这种实验-理论-计算策略有希望直接应用于患有各种肺部疾病和障碍的人类的临床治疗，用于疾病评估以及物理治疗和药物治疗策略的设计。 从数学上讲，这些模型和模拟工具有望提供对生理强迫（纤毛和呼吸和咳嗽的空气阻力）的平均粘液流动特性的洞察，并解决粘液分子网络中的微观结构变化。 这些工具可以深入了解疾病和疾病进展期间粘液的生物物理差异，这是物理和药物治疗预测设计的关键。 当与临床知识相结合时，该建模将提供推断粘液样品和健康和疾病状况的流动和扩散运输特性的能力，以及测试治疗以恢复个体化患者的粘液清除的能力。该项目开发了一种用于肺粘液运输建模的数据驱动策略，将随机分子动力学过程，基于微观结构的应力的演化方程，和动量方程。实验数据包括随机（熵波动）和确定性（受控强迫）的粘液微观结构的探针，连同高分辨率显微镜的粘液运输细胞培养物来自人肺组织。这些丰富的数据集提供了前所未有的探针所产生的广泛的粘液松弛谱从单个粘蛋白分子，它们在粘液凝胶中的缠结网络，瞬时粘蛋白交联，和断链动力学。细胞培养物提供了纤毛驱动的粘液流动运输的见解，并提供了一个实验室环境，探索施加的物理应力和化学剂量的分子到宏观的后果。为了将这一显着的数据转化为对粘液运输的预测性理解，研究了一个建模平台，该平台解决了粘液的分子到连续过程，无论是接近平衡还是远离平衡。我们的策略开始于随机时间序列的被动微珠探针在粘液凝胶中的各种浓度，以解决线性（近平衡）粘弹性表征的逆问题和正问题。接下来，在受控磁力的范围内使用相同浓度组的活性微珠数据来确定非线性阈值和微尺度下的非平衡行为的特征。这些数据直接输入到这个项目的主要目标，这是一个新的粘液的微观-宏观本构关系的制定。这个配方提出解释的线性和非线性数据，并整合到一个新的粘液运输模型的分子连续过程。目前正在研究一种数值战略，以便直接进行数值模拟，并与每一类实验的数据进行比较。