The Functional Connectome of the Mechanically Loaded Cardiomyocyte

机械负荷心肌细胞的功能连接组

• 批准号: 10322047
• 负责人: Ye Chen-Izu
• 金额: $ 67.05万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-10 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322047
• 关键词: Address; Affect; Area; Behavior; Biological; Cardiac; Cardiac Myocytes; Case Study; Cell physiology; Cells; Cellular biology; Contractile Proteins; Coupling; Data; Dimensions; Exclusion; Feedback; Heart; Heart failure; Homeostasis; Impairment; Ion Channel; Knowledge; Link; Maps; Mathematics; Measures; Mechanical Stress; Methodology; Modeling; Molecular; Muscle Cells; Muscle Contraction; Nitric Oxide; Outcome; Output; Pathway interactions; Pattern; Production; Reactive Oxygen Species; Running; Signal Pathway; Signal Transduction; Stimulus; Stress; Structure; Surface; Testing; Time; Ventricular; Work; connectome; experience; experimental study; heart function; high dimensionality; mathematical analysis; mathematical model; mechanical load; mechanotransduction; models and simulation; novel strategies; predictive modeling; response; sensor; shear stress; uptake

Mechanical load on the heart profoundly affects cardiac excitation-contraction (E-C) coupling that governs heart function. Recent experimental studies have revealed that mechanotransduction mechanisms link to multiple signaling pathways to modulate the activities of many ion channels, Ca2+ handling molecules, and contractile proteins, which work in concert to regulate contractile force to compensate for external load changes. Such autoregulation of contractility requires highly coordinated modulation of many molecules by mechanotransduction. PROBLEM: Current mathematical modeling of cardiomyocytes often uses one-at-a- time parameter changes in model simulations. To understand how multiple parameters and molecules change in a coordinated pattern, however, will require a new mathematical strategy. One-at-a-time parameter changes cannot address how multiple parameters change in a coordinated way. Studying the coordinated changes requires simultaneously changing many model parameters but even this does not, by itself, reveal how the changes are coordinated. INNOVATION: We will develop a new Functional Connectome approach by the following strategy. (a) Randomly change parameters of many subsystems. Because we make few a priori assumptions on what subsystems might be involved, this approach can reduce exclusion of some subsystems, which is important because cellular processes are highly interconnected. (b) From many simulated parameter combinations, we use experimental data to filter out a small number of subsets that fit all the data. Such a subset is called an Acceptable Parameter Set (APS). (c) To determine the coordinated changes of subsystems, we use the Singular Value Decomposition (SVD). SVD factorization of the parameter matrix shows that the APS often lies in a low-dimensional subspace of the entire high-dimension parameter space. The linear structure of this subspace gives both the map of connected subsystems and how the subsystems are modulated coordinately to produce the functional output. We call this connection map the Functional Connectome. Our interdisciplinary team will combine mathematical modeling with state-of-the-art experiments to achieve three specific aims: (1) Extend the cardiomyocyte mathematical model to include mechano-chemo-transduction feedback loop for studying autoregulation of Ca2+ and contractility in response to mechanical load changes. (2) Develop the Functional Connectome modeling platform to find patterns in myriad molecular changes. (3) Experimental test of the Functional Connectome predictions in mechanically loaded cardiomyocytes. SIGNIFICANCE: The outcome of this project will provide a new mathematical platform for studying coordinated changes in biological cells, which enables finding patterns in myriad molecular changes by various stimuli, and piece together many data to form a big picture. We will apply the Functional Connectome to study how mechanical load on cardiomyocyte causes coordinated molecular changes that give rise to the autoregulation of contractility in the heart. 1

心脏上的机械负载深刻影响控制的心脏激发反应（E-C）耦合 心脏功能。最近的实验研究表明，机械转移机制与 多个信号通路，以调节许多离子通道，Ca2+处理分子和 收缩蛋白，协同工作以调节收缩力以补偿外部负载 更改。这种自动调节的收缩性需要高度协调对许多分子的调节 机械转导。问题：心肌细胞的当前数学建模通常使用一个at-a- 时间参数在模型模拟中的变化。了解多个参数和分子如何改变 但是，以协调模式将需要一种新的数学策略。一次性参数更改 无法解决多个参数如何以协调方式变化。研究协调的变化 需要同时更改许多模型参数，但即使这样，也没有揭示 更改是协调的。创新：我们将开发一种新的功能连接方法 以下策略。 （a）随机更改许多子系统的参数。因为我们很少有先验 关于可能涉及哪些子系统的假设，这种方法可以减少某些子系统的排除， 这很重要，因为细胞过程是高度互连的。 （b）来自许多模拟参数 组合，我们使用实验数据来滤除适合所有数据的少数子集。这样的 子集称为可接受的参数集（APS）。 （c）确定协调的更改 子系统，我们使用奇异值分解（SVD）。参数矩阵的SVD分解 表明AP通常位于整个高维参数空间的低维子空间中。 该子空间的线性结构既赋予连接子系统的地图又提供了子系统的方式 进行调制协调以产生功能输出。我们称此连接映射为功能 Connectome。我们的跨学科团队将将数学建模与最先进的 实现三个特定目标的实验：（1）将心肌细胞数学模型扩展到包括 用于研究CA2+自动调节的机械化学转移反馈回路，并响应于收缩力 机械负载变化。 （2）开发功能性连接组建模平台以查找无数的模式 分子变化。 （3）机械加载中功能连接组预测的实验测试 心肌细胞。意义：该项目的结果将为一个新的数学平台提供 研究生物细胞的协调变化，这可以在无数分子变化中找到模式 通过各种刺激，并将许多数据拼凑在一起以形成全局。我们将应用功能 连接组研究心肌细胞上的机械负荷如何引起协调的分子变化 在心脏中自动调节。 1