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Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network

Overview of attention for article published in PLoS Computational Biology, August 2017
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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 (90th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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37 X users
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1 patent
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1 Facebook page

Citations

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37 Dimensions

Readers on

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83 Mendeley
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1 CiteULike
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Title
Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network
Published in
PLoS Computational Biology, August 2017
DOI 10.1371/journal.pcbi.1005677
Pubmed ID
Authors

Jinyuan Yan, Maxime Deforet, Kerry E. Boyle, Rayees Rahman, Raymond Liang, Chinweike Okegbe, Lars E. P. Dietrich, Weigang Qiu, Joao B. Xavier

Abstract

Bacteria of many species rely on a simple molecule, the intracellular secondary messenger c-di-GMP (Bis-(3'-5')-cyclic dimeric guanosine monophosphate), to make a vital choice: whether to stay in one place and form a biofilm, or to leave it in search of better conditions. The c-di-GMP network has a bow-tie shaped architecture that integrates many signals from the outside world-the input stimuli-into intracellular c-di-GMP levels that then regulate genes for biofilm formation or for swarming motility-the output phenotypes. How does the 'uninformed' process of evolution produce a network with the right input/output association and enable bacteria to make the right choice? Inspired by new data from 28 clinical isolates of Pseudomonas aeruginosa and strains evolved in laboratory experiments we propose a mathematical model where the c-di-GMP network is analogous to a machine learning classifier. The analogy immediately suggests a mechanism for learning through evolution: adaptation though incremental changes in c-di-GMP network proteins acquires knowledge from past experiences and enables bacteria to use it to direct future behaviors. Our model clarifies the elusive function of the ubiquitous c-di-GMP network, a key regulator of bacterial social traits associated with virulence. More broadly, the link between evolution and machine learning can help explain how natural selection across fluctuating environments produces networks that enable living organisms to make sophisticated decisions.

X Demographics

X Demographics

The data shown below were collected from the profiles of 37 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 %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 24%
Researcher 17 20%
Student > Bachelor 8 10%
Student > Master 8 10%
Student > Postgraduate 5 6%
Other 14 17%
Unknown 11 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 25 30%
Agricultural and Biological Sciences 19 23%
Immunology and Microbiology 8 10%
Physics and Astronomy 7 8%
Unspecified 3 4%
Other 10 12%
Unknown 11 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 24. 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 14 November 2023.
All research outputs
#1,598,210
of 25,382,440 outputs
Outputs from PLoS Computational Biology
#1,368
of 8,960 outputs
Outputs of similar age
#31,058
of 327,230 outputs
Outputs of similar age from PLoS Computational Biology
#31
of 141 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
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 has done well, scoring higher than 84% 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 327,230 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 141 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.