A multi-scale computational model of the extracellular matrix of the lung

细胞创造自己的生化和机械环境，同时又极其敏感 它显示出一种非平衡的稳态，以确保正常的细胞外基质（ECM）功能 在生活中。目前有两种无法治愈的肺部疾病，这种维护失败。气肿 涉及组织的进行性破坏，伴随ECM硬度的丧失，而肺纤维化的特征在于 由于ECM过度沉积而导致组织硬化。这些也是慢性进行性疾病，其中 炎症、机械转导和细胞迁移使ECM的局部稳态控制不稳定。我们 总体假设是ECM机制和细胞内环境之间的稳态反馈异常， 反应是肺气肿和肺纤维化的病理生理学的中心。这个目标 一项提议是开发和测试一个多尺度计算平台，该平台将推动对以下问题的关键性新见解： 稳态细胞-ECM偶联的恶化如何导致特定的疾病形式。目标1：开发多尺度 肺实质的计算模型。实质的肺泡结构将被表现出来 as a 3D network网络of elastic弹性sheets片.自治代理将用于表示各种细胞类型，例如 成纤维细胞和炎症细胞。遵守规则集的代理定义其随机 迁移和他们的分泌炎症和酶的因素，重塑弹性蛋白和胶原蛋白将 在整个网络中。代理移动和活动将在整个时间内确定为网络 呼吸该模型的基于物理和基于代理的组件将通过以下效果联系起来： 对保持局部ECM属性的代理响应施加压力和压力，这反过来又决定了全局 网络的机制。目的2：确定ECM组成的空间和时间分布， 在啮齿类动物模型肺气肿和肺纤维化中的炎症。会诱发肺气肿和纤维化 香烟烟雾提取物和博莱霉素，分别在大鼠。细胞和细胞之间的对应程度 将确定整个组织中的损伤位置并用于推导各种细胞的行为规则 Aim 1的基于代理的模型的类型。目的3：确定宏观结构和实质 在肺气肿和肺纤维化的啮齿动物模型中，力学随时间演变。微型CT的结构 并且随着时间的推移将跟踪动物模型中肺的机械特性， 确定ECM结构-功能如何随着病理进展而演变，并验证基于物理学的 Aim 1中的ECM网络模型。目的4：预测肺气肿演变过程中的组织结构和功能 和肺纤维化。目标1的模型将被初始化远离稳态，允许进化 随着时间的推移，其结构和功能与目标2和3的实验观察结果进行比较。最后， 我们将开发一种适用于任何ECM组织的ECM维护的新的多尺度建模方法， 进而可用于预测疾病进展和了解新的治疗方法。

Cells create their own biochemical and mechanical environment, and at the same time are exquisitely sensitive to it, displaying a non-equilibrium homeostatic state that ensures normal extracellular matrix (ECM) function throughout life. There are two currently incurable lung diseases for which this maintenance fails. Emphysema involves the progressive destruction of tissue with loss of ECM stiffness while pulmonary fibrosis is characterized by tissue stiffening due to excessive ECM deposition. These are also chronic progressive diseases in which inflammation, mechanotransduction and cellular migration destabilize local homeostatic control of the ECM. Our overarching hypothesis is that abnormalities in the homeostatic feedback between ECM mechanics and cellular responses are central to the pathophysiology of both emphysema and pulmonary fibrosis. The goal of this proposal is to develop and test a multi-scale computational platform that will advance critical new insight into how deterioration of homeostatic cell-ECM coupling leads to specific disease forms. Aim 1: Develop a multiscale computational model of the lung parenchyma. The alveolar structure of the parenchyma will be represented as a 3D network of elastic sheets. Autonomous agents will be used to represent various cell types, such as fibroblasts and inflammatory cells, in the tissue. The agents adhering to rule sets defining their stochastic migration and their secretion of inflammatory and enzymatic factors that remodel elastin and collagen will be placed throughout the network. Agent movement and activity will be determined throughout time as the network breathes. The physics-based and agent-based components of the model will be linked through the effects of stress and strain on agent responses that maintain local ECM properties, which in turn determine the global mechanics of the network. Aim 2: Determine the spatial and temporal distribution of ECM composition and inflammation in rodent models of emphysema and pulmonary fibrosis. Emphysema and fibrosis will be induced by cigarette smoke extract and bleomycin, respectively, in rats. The degree of correspondence between cell and injury locations throughout the tissue will be determined and used to derive rules of behavior for the various cell types for the agent-based model of Aim 1. Aim 3: Determine how macroscopic structure and parenchymal mechanics evolve over time in rodent models of emphysema and pulmonary fibrosis. The structure on micro-CT and the mechanical characteristics of the lungs in the animal models will be followed over time in order to determine how ECM structure-function evolves with advancing pathology, and to validate the physics-based ECM network model in Aim 1. Aim 4: Predict tissue structure and function during the evolution of emphysema and pulmonary fibrosis. The model of Aim 1 will be initialized away from the homeostatic state, allowed to evolve over time and its structure and function compared to experimental observations from Aims 2 and 3. Ultimately, we will develop a novel multi-scale modeling approach to ECM maintenance applicable to any ECM tissue, which in turn can be used to predict disease progression and understand novel therapeutic procedures.