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。 对它来说，表现出一种确保正常细胞外基质(ECM)功能的非平衡平衡状态 在一生中。目前有两种无法治愈的肺部疾病，这种维持方法失败了。肺气肿 肺纤维化表现为肺组织进行性破坏，细胞外基质僵硬丧失 由于过度的细胞外基质沉积而导致组织僵硬。这些也是慢性进行性疾病， 炎症、机械转导和细胞迁移破坏了细胞外基质的局部稳态控制。我们的 最重要的假设是ECM机制和细胞之间的稳态反馈异常 反应是肺气肿和肺纤维化病理生理学的中心。这样做的目的是 建议是开发和测试多尺度计算平台，以推动关键的新见解 动态平衡细胞-细胞外基质偶联的恶化如何导致特定的疾病形式。目标1：发展多尺度 肺实质的计算模型。实质的肺泡结构将被表示出来 作为弹性薄片的3D网络。自治代理将用于表示各种细胞类型，例如 组织中的成纤维细胞和炎性细胞。代理遵守定义其随机性的规则集 重组弹性蛋白和胶原蛋白的炎性和酶因子的迁移和分泌将是 放在整个网络中。代理的移动和活动将作为网络在整个时间内确定 呼吸。模型的基于物理的组件和基于代理的组件将通过以下效果联系起来 维持局部ECM属性的试剂响应上的应力和应变，这反过来又决定了全局 网络的机械结构。目标2：确定ECM组成和时间分布 肺气肿和肺纤维化啮齿动物模型中的炎症。会诱发肺气肿和纤维化 香烟烟雾提取物和博莱霉素分别对大鼠产生毒性作用。单元格和单元格的对应程度 整个组织的损伤位置将被确定并用于推导各种细胞的行为规则 基于主体的模型的类型目标1.目标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，机制随着时间的推移而演变。微型CT的结构 并将随着时间的推移观察动物模型中肺的机械特性，以便 确定ECM结构-功能如何随先进的病理演变，并验证基于物理的 目的1.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的模型将从动态平衡状态初始化，允许进化 以及它的结构和功能与目标2和3的实验观察结果相比较。最终， 我们将开发一种适用于任何ECM组织的新的多尺度建模方法来维持ECM，该方法 反过来，可以用来预测疾病的进展和了解新的治疗程序。

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.