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Beyond Turing: mechanochemical pattern formation in biological tissues

Overview of attention for article published in Biology Direct, May 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (81st percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

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11 X users
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2 Wikipedia pages

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30 Dimensions

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83 Mendeley
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Title
Beyond Turing: mechanochemical pattern formation in biological tissues
Published in
Biology Direct, May 2016
DOI 10.1186/s13062-016-0124-7
Pubmed ID
Authors

Moritz Mercker, Felix Brinkmann, Anna Marciniak-Czochra, Thomas Richter

Abstract

During embryogenesis, chemical (morphogen) and mechanical patterns develop within tissues in a self-organized way. More than 60 years ago, Turing proposed his famous reaction-diffusion model for such processes, assuming chemical interactions as the main driving force in tissue patterning. However, experimental identification of corresponding molecular candidates is still incomplete. Recent results suggest that beside morphogens, also tissue mechanics play a significant role in these patterning processes. Combining continuous finite strain with discrete cellular tissue models, we present and numerically investigate mechanochemical processes, in which morphogen dynamics and tissue mechanics are coupled by feedback loops. We consider three different mechanical cues involved in such feedbacks: strain, stress, and compression. Based on experimental results, for each case, we present a feedback loop spontaneously creating robust mechanochemical patterns. In contrast to Turing-type models, simple mechanochemical interaction terms are sufficient to create de novo patterns. Our results emphasize mechanochemical processes as possible candidates controlling different steps of embryogenesis. To motivate further experimental research discovering related mechanisms in living tissues, we also present predictive in silicio experiments. Reviewer 1 - Marek Kimmel; Reviewer 2 - Konstantin Doubrovinski (nominated by Ned Wingreen); Reviewer 3 - Jun Allard (nominated by William Hlavacek).

X Demographics

X Demographics

The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
United States 1 1%
Sweden 1 1%
Germany 1 1%
Unknown 79 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 25%
Researcher 19 23%
Student > Master 8 10%
Professor > Associate Professor 6 7%
Professor 5 6%
Other 12 14%
Unknown 12 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 27%
Biochemistry, Genetics and Molecular Biology 21 25%
Engineering 8 10%
Mathematics 6 7%
Physics and Astronomy 6 7%
Other 9 11%
Unknown 11 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 11 October 2021.
All research outputs
#3,557,166
of 22,867,327 outputs
Outputs from Biology Direct
#142
of 487 outputs
Outputs of similar age
#56,426
of 298,976 outputs
Outputs of similar age from Biology Direct
#3
of 15 outputs
Altmetric has tracked 22,867,327 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 487 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has gotten more attention than average, scoring higher than 70% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 298,976 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 81% of its contemporaries.
We're also able to compare this research output to 15 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.