Computational methods for mechanistic understanding of inter-sample variability

项目摘要 样本间变异性机械理解的计算方法 这个R21应用程序的总体目标是开发一个计算框架， 预测实验样品之间的生理差异。 单个样本之间的差异 可以在生理学水平上进行分类和描述，根据动作电位等特性， 也可以在分子水平上进行测量，比如基因表达。 将可变性连接到一个 然而，从一个水平到另一个水平的变化，并不是一件简单的事情。 在这里，通过一个 实验研究，数学建模和统计分析的创新组合，我们将 开发方法，允许将样品之间的分子水平差异转化为定量 生理差异的预测。 该多名主要研究者提案利用了两名主要研究者的互补专业知识。 Eric Sobie博士 是心脏生理学、数学建模和计算方法方面的专家， Christoph Schaniel博士是干细胞生物学方面的专家，将多能细胞分化为特定的细胞。 细胞类型和高通量方法。 两个PI的共同努力将产生新的定量 数据，并将产生新的计算方法，可以广泛应用于了解不同的变化， contexts. 为了实现项目的总体目标，我们建议： 1. 收集心脏生理学的测量结果和相关离子通道、泵和 运输机 这些测量值将在逐个样本的基础上进行匹配。 2. 使用动态数学模型进行基于人口的模拟， 关于样本之间重要基因表达差异的机械预测 转化为生理上的差异。 3. 使用基于回归的统计方法来分析实验和模拟结果， 将两组预测相互关联。 这项探索性的研究不仅可能提供对心肌细胞生理学的深入了解， 从干细胞，它也可能证明一种新的计算框架，可用于 在许多生物学背景下对样品之间的变异性进行定量处理。

Project Summary Computational methods for mechanistic understanding of inter-sample variability The overall goal of this R21 application is to develop a computational framework that will allow for the prediction of physiological differences between experimental samples. Differences between individual samples can be catalogued and described at the physiological level, in terms of properties such as action potentials, and also at the molecular level, in terms of measurements such as gene expression. Linking variability at one level to variability at another level in a quantitative manner, however, is not straightforward. Here, through an innovative combination of experimental studies, mathematical modeling, and statistical analyses, we will develop methods that allow for molecular-level differences between samples to be translated into quantitative predictions of physiological differences. This Multiple Principal Investigator proposal utilizes the complementary expertise of the two PIs. Dr. Eric Sobie is expert in cardiac physiology, mathematical modeling, and computational approaches for understanding variability~ Dr. Christoph Schaniel is expert in stem cell biology, differentiation of pluirpotent cells into specific cell types, and high-throughput methods. The combined efforts of the two PIs will generate new quantitative data and will yield new computational methods that can be applied broadly to understand variability in different contexts. To achieve the overall project goals, we propose to: 1. Collect measurements of cardiac physiology and expression of relevant ion channels, pumps, and transporters. These measurements will be matched on a sample-by-sample basis. 2. Perform population-based simulations with dynamical mathematical models to develop quantitative and mechanistic predictions regarding how differences between samples in expression of important genes are translated into physiological differences. 3. Use regression-based statistical methods to analyze the experimental and simulation results, and to relate the two sets of predictions to each other. Not only is this exploratory research likely to provide insight into the physiology of cardiac myocytes derived from stem cells, it is also likely to demonstrate a novel computational framework that can be used for quantitative treatments of variability between samples in many biological contexts.