Data-Driven Multiscale Modelling of Asthmatic Airways

炎症、气道高反应性和气道重塑是哮喘的关键特征，但它们之间的相互关系尚不清楚。最近一项全面的哮喘小鼠体内实验研究量化了气道结构变化及其与气道炎症状态的关系，产生了前所未有的数据量，为哮喘bbb的气道重塑机制模型提供了信息。一些参数集是根据实验数据定义好的，但另一些在参数值和模型选择方面提供了很高的不确定性。在这个项目中，我们将使用来自哮喘小鼠模型的大量数据来开发和告知气道组织的计算生物力学模型，并结合调节生化信号来建立稳态状态（即健康气道）的数学描述。这将使我们了解对这种内平衡状态的扰动是如何使气道进入哮喘状态的，并最终了解哪些过程是导致疾病的原因。然后将其扩展为分支气道的网络模型。

Inflammation, airway hyperresponsiveness and airway remodelling are key characteristics of asthma, but it is unclear how they are interconnected. A recent comprehensive experimental in vivo asthma mouse study quantifying structural changes and how they relate to the inflammatory state in the airway, has generated an unprecedented amount of data to inform a mechanistic model airway remodelling in asthma [1]. Some parameter sets are well-defined from experimental data but others provide high levels of uncertainty in parameter value and model selection. In this project we will use extensive data from a mouse model of asthma to develop and inform computational biomechanical models of airway tissue combined with regulatory biochemical signalling to establish a mathematical description for the homoeostatic state (ie for healthy airways). This will then allow us to understand how perturbations to this homoeostatic state could drive airways into an asthmatic state, and ultimately to understand which processes are the cause of the disease. This will then be extended to a network model of the branching airways.