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Axial and Radial Forces of Cross-Bridges Depend on Lattice Spacing

Overview of attention for article published in PLoS Computational Biology, December 2010
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Title
Axial and Radial Forces of Cross-Bridges Depend on Lattice Spacing
Published in
PLoS Computational Biology, December 2010
DOI 10.1371/journal.pcbi.1001018
Pubmed ID
Authors

C. David Williams, Michael Regnier, Thomas L. Daniel

Abstract

Nearly all mechanochemical models of the cross-bridge treat myosin as a simple linear spring arranged parallel to the contractile filaments. These single-spring models cannot account for the radial force that muscle generates (orthogonal to the long axis of the myofilaments) or the effects of changes in filament lattice spacing. We describe a more complex myosin cross-bridge model that uses multiple springs to replicate myosin's force-generating power stroke and account for the effects of lattice spacing and radial force. The four springs which comprise this model (the 4sXB) correspond to the mechanically relevant portions of myosin's structure. As occurs in vivo, the 4sXB's state-transition kinetics and force-production dynamics vary with lattice spacing. Additionally, we describe a simpler two-spring cross-bridge (2sXB) model which produces results similar to those of the 4sXB model. Unlike the 4sXB model, the 2sXB model requires no iterative techniques, making it more computationally efficient. The rate at which both multi-spring cross-bridges bind and generate force decreases as lattice spacing grows. The axial force generated by each cross-bridge as it undergoes a power stroke increases as lattice spacing grows. The radial force that a cross-bridge produces as it undergoes a power stroke varies from expansive to compressive as lattice spacing increases. Importantly, these results mirror those for intact, contracting muscle force production.

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Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 31%
Researcher 6 12%
Student > Bachelor 5 10%
Student > Doctoral Student 4 8%
Other 4 8%
Other 12 23%
Unknown 5 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 11 21%
Engineering 10 19%
Physics and Astronomy 5 10%
Biochemistry, Genetics and Molecular Biology 4 8%
Sports and Recreations 3 6%
Other 10 19%
Unknown 9 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 February 2014.
All research outputs
#17,286,645
of 25,374,917 outputs
Outputs from PLoS Computational Biology
#7,480
of 8,960 outputs
Outputs of similar age
#152,358
of 190,671 outputs
Outputs of similar age from PLoS Computational Biology
#40
of 55 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 11th percentile – i.e., 11% of its peers scored the same or lower than it.
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