Wu J Z, Herzog W, Cole G K
Human Performance Laboratory, University of Calgary, Alberta, Canada.
Math Biosci. 1997 Jan 1;139(1):69-78. doi: 10.1016/s0025-5564(96)00115-0.
During normal, voluntary movements, skeletal muscles typically contract in a highly dynamic manner; the length of the muscle and the speed of contraction change continuously. In this study, we present an approach to predict the accurate behavior of muscles for such dynamic contractions using Huxley's cross-bridge model. A numerical procedure is proposed to solye, without any assumptions, the partial differential equation that governs the attachment distribution function in Huxley's cross-bridge model. The predicted attachment distribution functions, and the corresponding force responses for shortening and stretching, were compared with those obtained using Zahalak's analytical solution and those obtained using the so-called "distribution moment model" in transient and steady-state contractions. Compared to the distribution moment model, the solutions obtained using our model are exact rather than approximate. The solutions obtained using the analytical approach and the present approach were virtually identical; however, in terms of CPU times, the present approach was 250-300 times faster than Zahalak's. From the results of this study, we concluded that the proposed solution is an exact and efficient way for solving the partial differential equation governing the cross-bridge model.
在正常的自主运动过程中,骨骼肌通常以高度动态的方式收缩;肌肉的长度和收缩速度会持续变化。在本研究中,我们提出了一种使用赫胥黎横桥模型来预测此类动态收缩时肌肉准确行为的方法。我们提出了一种数值程序,无需任何假设即可求解赫胥黎横桥模型中控制附着分布函数的偏微分方程。将预测的附着分布函数以及相应的缩短和拉伸力响应,与使用扎哈拉克解析解得到的结果以及在瞬态和稳态收缩中使用所谓“分布矩模型”得到的结果进行了比较。与分布矩模型相比,使用我们模型得到的解是精确的而非近似的。使用解析方法和本方法得到的解几乎相同;然而,在计算时间方面,本方法比扎哈拉克的方法快250 - 300倍。从本研究结果来看,我们得出结论,所提出的解是求解控制横桥模型的偏微分方程的一种精确且高效的方法。