The Functional Connectome of the Mechanically Loaded Cardiomyocyte

心脏上的机械负荷深刻地影响心脏兴奋-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 多种信号传导途径调节许多离子通道、Ca 2+处理分子的活性， 收缩蛋白，其协同工作以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许多分子的高度协调调节， 机械传导问题：目前心肌细胞的数学建模通常使用一个一个的方法。 模型模拟中的时间参数变化。为了了解多个参数和分子是如何变化的 然而，在一个协调的模式中，将需要一个新的数学策略。一次一次的参数更改 无法解决多个参数如何以协调的方式变化。研究协调变化 需要同时改变许多模型参数，但即使这样，本身也不能揭示 变化是协调的。创新：我们将开发一种新的功能性连接组方法， 遵循战略。(a)随机改变多个子系统的参数。因为我们很少先验地 假设可能涉及哪些子系统，这种方法可以减少对某些子系统的排除， 这一点很重要，因为细胞过程高度相互关联。(b)从多个模拟参数 组合，我们使用实验数据来过滤出适合所有数据的少量子集。这样的 这个子集被称为可接受参数集（APS）。(c)为了确定协调的变化， 子系统，我们使用奇异值分解（SVD）。参数矩阵的SVD分解 表明APS往往位于整个高维参数空间的低维子空间中。 这个子空间的线性结构给出了连通子系统的映射以及子系统如何 被协调地调制以产生功能输出。我们将此连接映射称为函数映射 连接体我们的跨学科团队将联合收割机数学建模与最先进的 实验达到三个具体目标：（1）扩展心肌细胞的数学模型，包括 机械-化学-转导反馈回路，用于研究响应于 机械负荷变化。(2)开发功能性连接组建模平台，以在无数 分子变化(3)机械负载中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方面提供新的数学平台： 研究生物细胞中的协调变化，这使得能够在无数分子变化中找到模式 通过各种各样的刺激，把很多数据拼凑起来，形成一个大的画面。我们将应用函数 Connectome研究心肌细胞上的机械负荷如何引起协调的分子变化， 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