The Functional Connectome of the Mechanically Loaded Cardiomyocyte

心脏上的机械负荷深刻影响控制心脏兴奋-收缩 (E-C) 耦合 心脏功能。最近的实验研究表明，机械传导机制与 多种信号通路来调节许多离子通道、Ca2 处理分子的活性，以及 收缩蛋白，协同调节收缩力以补偿外部负荷 变化。这种收缩性的自动调节需要通过以下方式高度协调地调节许多分子： 力传导。问题：当前心肌细胞的数学模型通常使用一对一的方法 模型模拟中的时间参数变化。了解多个参数和分子如何变化 然而，在协调模式中，将需要新的数学策略。一次更改一个参数 无法解决多个参数如何以协调的方式变化。研究协调变化 需要同时更改许多模型参数，但即使如此，其本身也无法揭示如何 变化得到协调。创新：我们将开发一种新的功能连接组方法 遵循策略。 (a) 随机改变许多子系统的参数。因为我们先验了一些 对可能涉及哪些子系统的假设，这种方法可以减少对某些子系统的排除， 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，我们使用实验数据过滤掉适合所有数据的少量子集。这样一个 子集称为可接受参数集（APS）。 (c) 确定协调变化 子系统中，我们使用奇异值分解（SVD）。参数矩阵的 SVD 分解 表明 APS 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给出了连接子系统的映射以及子系统如何 协调调制以产生功能输出。我们将此连接图称为功能图 连接体。我们的跨学科团队将数学建模与最先进的技术相结合 实验旨在实现三个具体目标：（1）扩展心肌细胞数学模型以包括 机械化学转导反馈回路，用于研究 Ca2 的自动调节和收缩性响应 机械负载变化。 (2) 开发功能连接组建模平台以发现无数的模式 分子变化。 (3) 机械负载下功能连接组预测的实验测试 心肌细胞。意义：该项目的成果将为以下领域提供一个新的数学平台： 研究生物细胞的协调变化，从而能够发现无数分子变化的模式 通过各种刺激，并将许多数据拼凑起来形成一幅大图。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，从而赋予 引起心脏收缩力的自动调节。 1

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