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Algorithms for detecting and analysing autocatalytic sets

Overview of attention for article published in Algorithms for Molecular Biology, April 2015
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  • Above-average Attention Score compared to outputs of the same age (56th percentile)

Mentioned by

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2 tweeters

Citations

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14 Dimensions

Readers on

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19 Mendeley
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Title
Algorithms for detecting and analysing autocatalytic sets
Published in
Algorithms for Molecular Biology, April 2015
DOI 10.1186/s13015-015-0042-8
Pubmed ID
Authors

Wim Hordijk, Joshua I Smith, Mike Steel

Abstract

Autocatalytic sets are considered to be fundamental to the origin of life. Prior theoretical and computational work on the existence and properties of these sets has relied on a fast algorithm for detectingself-sustaining autocatalytic sets in chemical reaction systems. Here, we introduce and apply a modified version and several extensions of the basic algorithm: (i) a modification aimed at reducing the number of calls to the computationally most expensive part of the algorithm, (ii) the application of a previously introduced extension of the basic algorithm to sample the smallest possible autocatalytic sets within a reaction network, and the application of a statistical test which provides a probable lower bound on the number of such smallest sets, (iii) the introduction and application of another extension of the basic algorithm to detect autocatalytic sets in a reaction system where molecules can also inhibit (as well as catalyse) reactions, (iv) a further, more abstract, extension of the theory behind searching for autocatalytic sets. (i) The modified algorithm outperforms the original one in the number of calls to the computationally most expensive procedure, which, in some cases also leads to a significant improvement in overall running time, (ii) our statistical test provides strong support for the existence of very large numbers (even millions) of minimal autocatalytic sets in a well-studied polymer model, where these minimal sets share about half of their reactions on average, (iii) "uninhibited" autocatalytic sets can be found in reaction systems that allow inhibition, but their number and sizes depend on the level of inhibition relative to the level of catalysis. (i) Improvements in the overall running time when searching for autocatalytic sets can potentially be obtained by using a modified version of the algorithm, (ii) the existence of large numbers of minimal autocatalytic sets can have important consequences for the possible evolvability of autocatalytic sets, (iii) inhibition can be efficiently dealt with as long as the total number of inhibitors is small.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 5%
Italy 1 5%
Switzerland 1 5%
Unknown 16 84%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 21%
Researcher 3 16%
Student > Bachelor 2 11%
Student > Postgraduate 1 5%
Student > Doctoral Student 1 5%
Other 4 21%
Unknown 4 21%
Readers by discipline Count As %
Mathematics 3 16%
Biochemistry, Genetics and Molecular Biology 3 16%
Agricultural and Biological Sciences 2 11%
Chemistry 2 11%
Physics and Astronomy 2 11%
Other 3 16%
Unknown 4 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 19 May 2015.
All research outputs
#6,902,675
of 12,434,754 outputs
Outputs from Algorithms for Molecular Biology
#76
of 182 outputs
Outputs of similar age
#95,987
of 228,783 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 3 outputs
Altmetric has tracked 12,434,754 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 182 research outputs from this source. They receive a mean Attention Score of 2.9. This one has gotten more attention than average, scoring higher than 58% 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 228,783 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 56% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one.