Kim Hojeong, Sandercock Thomas G, Heckman C J
Division of Robotics Research, Daegu Gyeongbuk Institute of Science and Technology, Daegu 711-873, Korea. Department of Physiology, Northwestern University Feinberg School of Medicine, Chicago 60611, USA.
J Neural Eng. 2015 Aug;12(4):046025. doi: 10.1088/1741-2560/12/4/046025. Epub 2015 Jun 18.
The goal of this study was to develop a physiologically plausible, computationally robust model for muscle activation dynamics (A(t)) under physiologically relevant excitation and movement.
The interaction of excitation and movement on A(t) was investigated comparing the force production between a cat soleus muscle and its Hill-type model. For capturing A(t) under excitation and movement variation, a modular modeling framework was proposed comprising of three compartments: (1) spikes-to-[Ca(2+)]; (2) [Ca(2+)]-to-A; and (3) A-to-force transformation. The individual signal transformations were modeled based on physiological factors so that the parameter values could be separately determined for individual modules directly based on experimental data.
The strong dependency of A(t) on excitation frequency and muscle length was found during both isometric and dynamically-moving contractions. The identified dependencies of A(t) under the static and dynamic conditions could be incorporated in the modular modeling framework by modulating the model parameters as a function of movement input. The new modeling approach was also applicable to cat soleus muscles producing waveforms independent of those used to set the model parameters.
This study provides a modeling framework for spike-driven muscle responses during movement, that is suitable not only for insights into molecular mechanisms underlying muscle behaviors but also for large scale simulations.
本研究的目标是开发一个在生理相关的刺激和运动下，具有生理合理性且计算稳健的肌肉激活动力学（A(t)）模型。
通过比较猫比目鱼肌与其希尔型模型之间的力产生情况，研究刺激和运动对A(t)的相互作用。为了捕捉在刺激和运动变化下的A(t)，提出了一个模块化建模框架，该框架由三个部分组成：（1）动作电位到[Ca(2+)]；（2）[Ca(2+)]到A；以及（3）A到力的转换。基于生理因素对各个信号转换进行建模，以便可以直接根据实验数据分别确定各个模块的参数值。
在等长收缩和动态运动收缩过程中，均发现A(t)对刺激频率和肌肉长度有很强的依赖性。通过根据运动输入调节模型参数，可以将在静态和动态条件下确定的A(t)的依赖性纳入模块化建模框架。这种新的建模方法也适用于产生与用于设置模型参数的波形无关的波形的猫比目鱼肌。
本研究提供了一个用于运动过程中动作电位驱动的肌肉反应的建模框架，该框架不仅适用于深入了解肌肉行为的分子机制，也适用于大规模模拟。
