Multiscale Modeling of Aortic Homeostasis

项目摘要。机械内稳态是脉管系统通过其来适应体内环境的变化的过程。 血流、血压和其他影响。越来越多的证据表明，妥协或失去 体内平衡是许多血管疾病的原因或结果。因此，迫切需要 增加对血管稳态的理解，这必然来自分子和细胞 过程，但表现在组织水平上通过负反馈，可以用数学描述。 这个项目的目标是利用现有的广泛的数据集在一个独特的小鼠模型的主动脉重塑， 高血压，以告知和验证一个新的多尺度血管稳态模型。一旦实现， 该模型有望帮助描述通过改变组织内环境来促进组织稳态的代偿机制。 细胞信号传导与阻止体内平衡的病理机制。为此，我们将把最近的 细胞信号模型和连续水平生长和重塑模型的进展，以描述和预测 来自高血压的独特小鼠模型的数据，其中主动脉重塑是适应性的，因为保留了 收缩表型和增强的合成表型，具有固有的低炎症。这样，我们将 避免其他高血压和驾驶小鼠模型中存在的典型炎症并发症 这种反应远离了体内平衡。我们将告知我们的机械生物学动机的多尺度模型， 来自RNA测序、定量组织学和双轴生物力学（被动和 活动）数据。重要的是，这种基于数据的模型将使我们能够第一次探索潜在的 促进或阻止有效体内平衡的细胞信号传导拓扑结构的适应性与适应不良的变化， 因此代表了对某些血管疾病的理解和最佳治疗方法的范式转变 他们例如，高血压在这个国家很猖獗，是各种心血管疾病的关键危险因素， 神经血管和肾血管疾病。这项工作具有重要的生物学意义，因为它有可能提供新的 深入了解这一潜在的风险因素。然而，更一般地，组织内稳态对于许多人来说是基本的。 不同的组织和器官，我们的通用计算方法有望普遍适用。 最后，这项工作是高度创新的，因为它将确定一个新的计算框架，整合信息 从差异表达的基因到组织水平的表现，它将能够描绘 对不同基因突变或小分子的潜在稳态与非稳态应答 干预，这可以指导治疗干预。本提案通过R03机制提交， 其重点是“研究方法的发展”和“现有数据的二级分析”，具体而言， 从偶然发现的小鼠模型中获得的独特的多尺度数据集。它还将支持培训 一位前途无量的年轻女性生物数学家，她正在转向血管研究。

PROJECT SUMMARY. Mechanical homeostasis is a process by which the vasculature adapts to changes in blood flow, blood pressure, and other influences. Mounting evidence suggests that compromised or lost homeostasis is a cause or consequence of many vascular diseases. There is, therefore, a pressing need for an increased understanding of vascular homeostasis, which necessarily derives from molecular and cellular processes but manifests at the tissue level via negative feedback that can be described mathematically. The goal of this project is to use an existing extensive data set on aortic remodeling in a unique mouse model of hypertension to inform and validate a new multiscale model of vascular homeostasis. Once achieved, such a model promises to help delineate compensatory mechanisms that promote tissue homeostasis via changes in cell signaling versus pathologic mechanisms that prevent homeostasis. Toward this end, we will meld recent advances in cell signaling models and continuum level growth and remodeling models to describe and predict data from a unique mouse model of hypertension wherein aortic remodeling is adaptive because of a preserved contractile phenotype and augmented synthetic phenotype, with inherently low inflammation. In this way we will avoid the typical complication of inflammation that is present in other mouse models of hypertension and drives the response away from homeostasis. We will inform our mechanobiologically motivated multiscale model using a combination of data from RNA sequencing, quantitative histology, and biaxial biomechanical (passive and active) data. Importantly, this data-informed model will enable us to explore, for the first time, the potentially adaptive versus maladaptive changes in cell signaling topology that promote or prevent effective homeostasis, thus representing a paradigm shift in the way some vascular diseases are understood and how best to treat them. Hypertension, for example, is rampant in this country and is a key risk factor for diverse cardiovascular, neurovascular, and renovascular diseases. This work is significant biologically for it has potential to provide new insight into this insidious risk factor. More generally, however, tissue homeostasis is fundamental to many different tissues and organs and our general computational approach promises to be generally applicable. Finally, this work is highly innovative for it will identify a new computational framework for integrating information across scales from differentially expressed genes to tissue-level manifestations, and it will enable delineation of potentially homeostatic versus non-homeostatic responses to diverse genetic mutations or small molecule interventions, which can guide therapeutic intervention. This proposal is submitted via the R03 mechanism since its focus is “Development of research methodology” and “Secondary analysis of existing data”, specifically, a unique multiscale data set obtained from a fortuitously discovered mouse model. It will also support the training of a promising young female biomathematician as she transitions to vascular research.