Implications of quantal motor action in biological systems

Invertebrate muscles: thin and thick filament structure; molecular basis of contraction and its regulation, catch and asynchronous muscle

This is the second in a series of canonical reviews on invertebrate muscle. We cover here thin and thick filament structure, the molecular basis of force generation and its regulation, and two special properties of some invertebrate muscle, catch and asynchronous muscle. Invertebrate thin filaments resemble vertebrate thin filaments, although helix structure and tropomyosin arrangement show small differences. Invertebrate thick filaments, alternatively, are very different from vertebrate striated thick filaments and show great variation within invertebrates. Part of this diversity stems from variation in paramyosin content, which is greatly increased in very large diameter invertebrate thick filaments. Other of it arises from relatively small changes in filament backbone structure, which results in filaments with grossly similar myosin head placements (rotating crowns of heads every 14.5 nm) but large changes in detail (distances between heads in azimuthal registration varying from three to thousands of crowns). The lever arm basis of force generation is common to both vertebrates and invertebrates, and in some invertebrates this process is understood on the near atomic level. Invertebrate actomyosin is both thin (tropomyosin:troponin) and thick (primarily via direct Ca(++) binding to myosin) filament regulated, and most invertebrate muscles are dually regulated. These mechanisms are well understood on the molecular level, but the behavioral utility of dual regulation is less so. The phosphorylation state of the thick filament associated giant protein, twitchin, has been recently shown to be the molecular basis of catch. The molecular basis of the stretch activation underlying asynchronous muscle activity, however, remains unresolved.

Automatic step-detection algorithm for analysis of sarcomere dynamics

Motile systems exhibit a stepwise nature, seen most prominently in muscle contraction. A novel algorithm has been developed that detects steps automatically in sarcomere-length change data and computes their size. The method is based on a nonlinear filter and a step detection protocol that detects local slope values in comparison to a threshold. The algorithm was first evaluated using artificial data with various degrees of Gaussian noise. Steps were faithfully detected even with significant noise. With actual data, the algorithm detected 2.7 nm steps and integer multiples thereof. The results confirm a quantal 2.7 nm step-size reported earlier. As stepwise phenomena become increasingly evident, automatic step-detection algorithms become increasingly useful since the limiting factor is almost always noise. The algorithm presented here offers a versatile and accurate tool that should be useful not only within muscle contraction and motility fields, but in fields which quantal phenomena play a role.