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Self-Organization of Muscle Cell Structure and Function

Overview of attention for article published in PLoS Computational Biology, February 2011
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Title
Self-Organization of Muscle Cell Structure and Function
Published in
PLoS Computational Biology, February 2011
DOI 10.1371/journal.pcbi.1001088
Pubmed ID
Authors

Anna Grosberg, Po-Ling Kuo, Chin-Lin Guo, Nicholas A. Geisse, Mark-Anthony Bray, William J. Adams, Sean P. Sheehy, Kevin Kit Parker

Abstract

The organization of muscle is the product of functional adaptation over several length scales spanning from the sarcomere to the muscle bundle. One possible strategy for solving this multiscale coupling problem is to physically constrain the muscle cells in microenvironments that potentiate the organization of their intracellular space. We hypothesized that boundary conditions in the extracellular space potentiate the organization of cytoskeletal scaffolds for directed sarcomeregenesis. We developed a quantitative model of how the cytoskeleton of neonatal rat ventricular myocytes organizes with respect to geometric cues in the extracellular matrix. Numerical results and in vitro assays to control myocyte shape indicated that distinct cytoskeletal architectures arise from two temporally-ordered, organizational processes: the interaction between actin fibers, premyofibrils and focal adhesions, as well as cooperative alignment and parallel bundling of nascent myofibrils. Our results suggest that a hierarchy of mechanisms regulate the self-organization of the contractile cytoskeleton and that a positive feedback loop is responsible for initiating the break in symmetry, potentiated by extracellular boundary conditions, is required to polarize the contractile cytoskeleton.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 157 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 5 3%
Germany 2 1%
Brazil 1 <1%
Chile 1 <1%
Japan 1 <1%
United Kingdom 1 <1%
Unknown 146 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 55 35%
Researcher 23 15%
Student > Master 18 11%
Student > Bachelor 10 6%
Student > Doctoral Student 8 5%
Other 21 13%
Unknown 22 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 27%
Engineering 41 26%
Biochemistry, Genetics and Molecular Biology 14 9%
Physics and Astronomy 7 4%
Materials Science 5 3%
Other 24 15%
Unknown 24 15%