Multiscale Modeling of Aortic Homeostasis

项目总结。机械动态平衡是血管系统适应变化的过程。 血液流动、血压和其他影响。越来越多的证据表明，被破坏或丢失的 动态平衡是许多血管疾病的原因或后果。因此，迫切需要一种 增加对血管动态平衡的理解，这必然源于分子和细胞 过程，但表现在组织水平上的负反馈，可以用数学来描述。 该项目的目标是利用现有的关于主动脉重建的广泛数据集，在一种独特的小鼠模型中 高血压提供信息并验证血管内稳态的新的多尺度模型。一旦实现，这样的一个 模型承诺帮助描绘通过改变组织内环境平衡促进组织稳态的代偿机制 细胞信号与阻止动态平衡的病理机制。为此，我们将融合最近的 细胞信号模型及描述和预测连续体水平生长和重塑模型的研究进展 来自一种独特的高血压小鼠模型的数据，在该模型中，由于保存了 收缩表型和增强的合成表型，具有固有的低炎症。这样我们就会 避免其他高血压和驾驶小鼠模型中出现的典型炎症并发症 远离动态平衡的反应。我们将利用我们的机械生物学激励的多尺度模型 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，这种数据信息模型将使我们能够首次探索潜在的 细胞信号拓扑中促进或阻止有效内稳态的适应性与非适应性变化， 从而代表了对一些血管疾病的理解方式以及如何最好地治疗的范式转变 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 洞察这一潜伏的风险因素。然而，更广泛地说，组织动态平衡是许多疾病的基础。 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，这项工作具有很高的创新性，因为它将确定一种新的信息集成计算框架 从差异表达的基因到组织水平的表现，它将使我们能够描绘 对不同基因突变或小分子的潜在自稳与非自稳反应 干预措施，可以指导治疗干预。该提案是通过R03机制提交的，因为 它的重点是“研究方法的发展”和“对现有数据的二次分析”，具体地说， 从一个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 一位有前途的年轻女生物物理学家过渡到血管研究的照片。

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.