Collaborative Research: A Predictive Theory of Muscle Energy Consumption
合作研究:肌肉能量消耗的预测理论
基本信息
- 批准号:9902713
- 负责人:
- 金额:$ 35.69万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-09-23 至 2023-08-31
- 项目状态:已结题
- 来源:
- 关键词:BindingCellsChemicalsConsumptionCoupledDataData SetDifferential EquationFunctional disorderGeneticHeart DiseasesHumanKinesiologyLocomotionMeasurementMeasuresModelingMolecularMovementMuscleMuscle ContractionMuscle FibersMuscle functionMuscular AtrophyOrganProsthesisResearchRunningSelf-Help DevicesTestingWalkingWorkbasecostdesignexperimental studyhuman subjectimprovedin vivo evaluationmathematical theorymolecular scalenovelsimulationsingle moleculetheories
项目摘要
A predictive theory of muscle contraction and chemical energy consumption can transform human
movement science, e.g., helping us better understand movements such as walking and running and
informing the design of effort-reducing assistive and prosthetic devices. Such a theory can also inform a
quantitative understanding of the genetic basis of heart disease and other muscular dysfunction. An
accurate theory of muscle contraction and energy consumption does not exist. While a clear picture of
muscle contraction, including energy consumption, has emerged at the single molecule scale, the simplified
conditions of these experiments limit their application to larger scales. We propose to produce a multi-
scale mathematical theory of muscle contraction, based on molecular and cellular measurements, to
understand muscle function in vivo and test such a bottom-up theory's accuracy at the whole muscle or
whole body level, as will be relevant in applications. The proposed research builds on previous work,
where we developed a theory, described by linear ordinary differential equations, coupled to integro-partial
differential equations, that quantitatively describes experiments from single molecules to large ensembles.
Because our theory is described by differential equations, unlike other models, it can be inverted to predict
muscle energy consumption from muscle force. Such simulations predict a cost for the rate of muscle force
production, which is thought to be critical to understanding the energetics of human walking. Despite this
promising result, the theory lacks components necessary to quantitatively describe muscle at larger (i.e.
cellular, organ, etc.) scales. We will therefore perform experiments, motivated by the theory, to identify and
quantify the missing components. In Aim 1 we will extend the theory to the cellular scale by generating a
self-consistent data set from the single molecule to cellular (muscle fiber) scale. These experiments will
characterize the transient interactions (weak binding) between molecules involved in muscle contraction
that are too rapid to measure at the molecular scale, and so must be characterized via multi-scale
measurements interpreted with the theory. In Aim 2 we will extend the theory to conditions relevant to
locomotion by performing novel experiments on muscle molecules and cells under conditions that replicate
forcibly lengthened muscle (eccentric contraction), a situation that frequently occurs during locomotion. We
will test hypotheses, motivated by the model, that 1) molecular bonds are forcibly broken when muscle is
lengthened, and 2) this bond breaking leads to transient instabilities that cause catastrophic loss of muscle
force. In Aim 3, we will collect data for muscle energy consumption from human subjects. These
experiments will allow us to test and refine candidate muscle energy cost models. The theory already
makes testable predictions; our measurements will allow us to test these predictions, refine the model, and
improve on current muscle energy cost models.
肌肉收缩和化学能量消耗的预测理论可以改变人类
运动科学,例如,帮助我们更好地理解行走和跑步等运动
为减轻劳力的辅助和假肢装置的设计提供信息。这样的理论也可以告诉我们
定量了解心脏病和其他肌肉功能障碍的遗传基础。一个
关于肌肉收缩和能量消耗的准确理论并不存在。虽然一幅清晰的画面
肌肉收缩,包括能量消耗,已经出现在单分子尺度上,简化了
这些实验的条件限制了它们在更大范围内的应用。我们建议生产一种多-
基于分子和细胞测量的肌肉收缩的尺度数学理论,以
了解肌肉在活体中的功能,并测试这种自下而上理论在整个肌肉或
全身水平,这将在应用程序中相关。这项拟议的研究建立在以前工作的基础上,
在那里我们发展了一种理论,用线性常微分方程组描述,耦合到积分-偏微分方程组
微分方程,它定量地描述了从单分子到大型系综的实验。
因为我们的理论是用微分方程式来描述的,不像其他模型,它可以反转来预测
来自肌肉力量的肌肉能量消耗。这样的模拟预测了肌肉力量的速度的成本
生产,这被认为是理解人类行走的能量学的关键。尽管如此
令人振奋的结果是,该理论缺乏定量描述较大肌肉(即肌肉)的必要成分。
细胞、器官等)比例。因此,我们将在理论的推动下进行实验,以确定和
量化缺失的组件。在目标1中,我们将通过生成一个
从单分子到细胞(肌肉纤维)尺度的自洽数据集。这些实验将
描述参与肌肉收缩的分子之间的瞬时相互作用(弱结合)
它们太快,无法在分子尺度上测量,因此必须通过多尺度来表征
用该理论解释的测量结果。在目标2中,我们将把该理论扩展到与以下条件相关的条件
通过在复制条件下对肌肉分子和细胞进行新颖的实验来实现运动
强迫性拉长肌肉(偏心收缩),这是运动中经常出现的一种情况。我们
将检验由该模型激励的假设,即1)当肌肉被破坏时,分子键被强制断开
拉长,以及2)这种纽带断裂导致瞬间的不稳定,导致灾难性的肌肉损失
武力。在目标3中,我们将收集人体肌肉能量消耗的数据。这些
实验将允许我们测试和改进候选肌肉能量成本模型。理论上已经有了
做出可测试的预测;我们的测量将允许我们测试这些预测,改进模型,以及
对现有的肌肉能量成本模型进行改进。
项目成果
期刊论文数量(0)
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科研奖励数量(0)
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samuel walcott其他文献
samuel walcott的其他文献
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{{ truncateString('samuel walcott', 18)}}的其他基金
Collaborative Research: A Predictive Theory of Muscle Energy Consumption
合作研究:肌肉能量消耗的预测理论
- 批准号:
10493166 - 财政年份:2019
- 资助金额:
$ 35.69万 - 项目类别:
Collaborative Research: A Predictive Theory of Muscle Energy Consumption
合作研究:肌肉能量消耗的预测理论
- 批准号:
10378797 - 财政年份:2019
- 资助金额:
$ 35.69万 - 项目类别:
Collaborative Research: A Predictive Theory of Muscle Energy Consumption
合作研究:肌肉能量消耗的预测理论
- 批准号:
10267169 - 财政年份:2019
- 资助金额:
$ 35.69万 - 项目类别:
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