Measured and modeled properties of mammalian skeletal muscle. II. The effects of stimulus frequency on force-length and force-velocity relationships
- PMID: 10672511
- DOI: 10.1023/a:1005585030764
Measured and modeled properties of mammalian skeletal muscle. II. The effects of stimulus frequency on force-length and force-velocity relationships
Abstract
Interactions between physiological stimulus frequencies, fascicle lengths and velocities were analyzed in feline caudofemoralis (CF), a hindlimb skeletal muscle composed exclusively of fast-twitch fibers. Split ventral roots were stimulated asynchronously to produce smooth contractions at sub-tetanic stimulus frequencies. As described previously, the peak of the sub-tetanic force-length relationship was found to shift to longer lengths with decreases in stimulus frequency, indicating a length dependence for activation that is independent of filament overlap. The sub-tetanic force-velocity (FV) relationship was affected strongly both by stimulus frequency and by length; decreases in either decreased the slope of the FV relationship around isometric. The shapes of the force transients following stretch or shortening revealed that these effects were not due to a change in the instantaneous FV relationship; the relative shape of the force transients following stretch or shortening was independent of stimulus frequency and hardly affected by length. The effects of stimulus frequency and length on the sub-tetanic FV relationship instead appear to be caused by a time delay in the length-dependent changes of activation. In contrast to feline soleus muscle, which is composed exclusively of slow-twitch fibers, CF did not yield at sub-tetanic stimulus frequencies for the range of stretch velocities tested (up to 2 L0/s). The data presented here were used to build a model of muscle that accounted well for all of the effects described. We extended our model to account for slow twitch muscle by comparing our fast-twitch model with previously published data and then changing the necessary parameters to fit the data. Our slow-twitch model accounts well for all previous findings including that of yielding.
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