Experimental and Computational Studies in Genetic Cardiomyopathies

遗传性心肌病的实验和计算研究 少年派：法里德·穆萨维-哈拉米 摘要 心肌病，包括肥厚型心肌病(HCM)和扩张型心肌病 心肌病(DCM)是实施精准医疗战略的理想场所。这 是因为更常规地使用基因检测，以及大量关于 肌节变异体的潜在生物物理机制，它有助于扩张性心肌病和 胡志明市。虽然肌瘤变异如何导致心肌病的机制是活跃的 在调查领域，显然，它们扰乱了 心肌细胞。许多研究人员使用了各种生物物理和生化分析方法。 研究肌瘤变异体的机制，并将这些研究扩大到细胞、组织和 动物模型。这些方法是信息性的，但是渐进的，无法评估许多 一次产生变种。在这一领域的成功需要强大的高通量分析，具有以下能力 一次分析数千个不同的变种。我们的建议将直接克服限制 通过将数据分析应用于生物物理模拟和实验心脏 抽搐。基本假设是心脏抽动的主要特征概括了 肌瘤变异体的复杂的细丝内和丝间相互作用。此外，我们还可以利用 这些特征对于不同的分类、预测治疗反应和识别新的 治疗靶点。检验这些假说需要1)大数据集的变量，2)模型 这解释了肌节的不同位置和丰度，以及3)开发和验证 数据分析方法。肌节变体的生物物理模拟可以提供这样的 数据集，但需要在实验系统中进行验证。我们将使用空间上明确的 肌节的计算模型，可以模拟肌节中的扰动 机械力化学改变肌细胞力的产生。将会产生模拟的抽搐， 验证并用于预测靶向治疗。

Experimental and Computational Studies in Genetic Cardiomyopathies PI: Farid Moussavi-Harami Abstract Cardiomyopathies, including hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM), are an ideal venue for implementing precision medicine strategies. This is due to more routine use of genetic testing and the vast amount of knowledge regarding underlying biophysical mechanisms of sarcomeric variants, which contribute to both DCM and HCM. While the mechanisms of how sarcomeric variants cause cardiomyopathies is an active area of investigation, it is clear that they disrupt the finely tuned force-generation properties of cardiomyocytes. Many investigators have used a variety of biophysical and biochemical assays to study mechanism of sarcomeric variants and then scale these studies up to cells, tissues and animal models. These approaches are informative, but incremental and unable to asses many variants at once. Success in this area requires robust high-throughput assays with the ability for analysis of thousands of divergent variants at once. Our proposal will directly overcome limitations in the field by applying data analytics to biophysical simulations and experimental cardiac twitches. The fundamental hypothesis is that the principal features of cardiac twitches summarize the complex intra and inter-filament interactions of sarcomeric variants. Moreover, we can utilize these features for variant classification, predicting therapeutic response and identification of new therapeutics targets. Testing these hypotheses requires 1) large datasets of variants, 2) models that account for variant location and abundance in sarcomeres and 3) development and validation of data analytic methods. Biophysical simulations of sarcomeric variants can provide such datasets, but require validation in experimental systems. We will use a spatially explicit computational model of the sarcomere that can simulate how perturbations in sarcomere mechanochemistry change myocyte force generation. Simulated twitches will be generated, validated and used for predicting targeted therapeutics.