Blümel Marcus, Hooper Scott L, Guschlbauerc Christoph, White William E, Büschges Ansgar
Zoologisches Institut, Universität zu Köln, Köln, Germany.
Biol Cybern. 2012 Nov;106(10):543-58. doi: 10.1007/s00422-012-0531-5. Epub 2012 Nov 7.
Characterizing muscle requires measuring such properties as force-length, force-activation, and force-velocity curves. These characterizations require large numbers of data points because both what type of function (e.g., linear, exponential, hyperbolic) best represents each property, and the values of the parameters in the relevant equations, need to be determined. Only a few properties are therefore generally measured in experiments on any one muscle, and complete characterizations are obtained by averaging data across a large number of muscles. Such averaging approaches can work well for muscles that are similar across individuals. However, considerable evidence indicates that large inter-individual variation exists, at least for some muscles. This variation poses difficulties for across-animal averaging approaches. Methods to fully describe all muscle's characteristics in experiments on individual muscles would therefore be useful. Prior work in stick insect extensor muscle has identified what functions describe each of this muscle's properties and shown that these equations apply across animals. Characterizing these muscles on an individual-by-individual basis therefore requires determining only the values of the parameters in these equations, not equation form. We present here techniques that allow determining all these parameter values in experiments on single muscles. This technique will allow us to compare parameter variation across individuals and to model muscles individually. Similar experiments can likely be performed on single muscles in other systems. This approach may thus provide a widely applicable method for characterizing and modeling muscles from single experiments.
对肌肉进行特征描述需要测量诸如力-长度、力-激活和力-速度曲线等属性。这些特征描述需要大量的数据点,因为既要确定哪种类型的函数(例如,线性、指数、双曲线)最能代表每个属性,又要确定相关方程中参数的值。因此,在对任何一块肌肉进行的实验中,通常只测量少数几个属性,通过对大量肌肉的数据进行平均来获得完整的特征描述。这种平均方法对于个体间相似的肌肉效果良好。然而,大量证据表明,至少对于某些肌肉来说,个体间存在很大的差异。这种差异给跨动物平均方法带来了困难。因此,在对单个肌肉进行的实验中,能够全面描述所有肌肉特征的方法将很有用。此前对竹节虫伸肌的研究已经确定了描述该肌肉每种属性的函数,并表明这些方程适用于所有动物。因此,在个体基础上对这些肌肉进行特征描述只需要确定这些方程中参数的值,而不需要确定方程形式。我们在此展示了在单个肌肉实验中确定所有这些参数值的技术。这项技术将使我们能够比较个体间的参数差异,并对肌肉进行个体建模。类似的实验很可能也可以在其他系统的单个肌肉上进行。因此,这种方法可能为从单个实验中对肌肉进行特征描述和建模提供一种广泛适用的方法。