The Functional Connectome of the Mechanically Loaded Cardiomyocyte

心脏上的机械负荷深刻地影响着支配心脏的兴奋-收缩(E-C)偶联 心脏功能。最近的实验研究表明，机械转导机制与 多条信号通路调节许多离子通道、钙调节分子和 收缩蛋白，它们协同工作调节收缩力量以补偿外部负荷 改变。这种收缩能力的自动调节需要许多分子的高度协调调节，通过 机械转导。问题：目前对心肌细胞的数学建模通常使用一次一个- 模型模拟中的时间参数更改。要了解多个参数和分子如何变化 然而，在一个协调的模式下，将需要一个新的数学策略。一次更改一个参数 无法解决多个参数如何协调更改的问题。研究协调变化 需要同时更改许多模型参数，但即使这样，本身也不能揭示 变化是协调一致的。创新：我们将开发一种新的功能性Connectome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 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，因为细胞过程是高度相互联系的。(B)从多个模拟参数中 组合，我们使用实验数据来筛选出适合所有数据的少量子集。这样的一个 子集称为可接受参数集(APS)。(C)确定协调的变化 子系统，我们使用奇异值分解(SVD)。参数矩阵的奇异值分解 表明APS往往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给出了相连子系统的映射，也给出了子系统如何 被协调调制以产生功能输出。我们称此连接图为功能图 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 实现三个具体目标的实验：(1)将心肌细胞数学模型扩展到包括 机械力-化学-转导反馈环用于研究钙离子和肌张力的自动调节 机械载荷会发生变化。(2)开发功能性Connectome建模平台，发现万千模式 分子变化。(3)机械载荷下功能连接组预测的实验验证 心肌细胞。意义：该项目的成果将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，并将许多数据拼凑成一幅大图。我们将应用泛函 连接组研究心肌细胞机械负荷如何引起协调的分子变化，从而使 上升到心脏收缩能力的自动调节。 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