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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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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.

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Mendeley readers

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Geographical breakdown

Country Count As %
United States 1 4%
Italy 1 4%
Switzerland 1 4%
Unknown 21 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 21%
Researcher 4 17%
Student > Bachelor 2 8%
Professor 2 8%
Student > Doctoral Student 1 4%
Other 4 17%
Unknown 6 25%
Readers by discipline Count As %
Computer Science 3 13%
Mathematics 3 13%
Chemistry 3 13%
Biochemistry, Genetics and Molecular Biology 2 8%
Agricultural and Biological Sciences 2 8%
Other 5 21%
Unknown 6 25%
Attention Score in Context

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 23 November 2022.
All research outputs
#15,693,374
of 24,862,067 outputs
Outputs from Algorithms for Molecular Biology
#114
of 257 outputs
Outputs of similar age
#144,837
of 269,787 outputs
Outputs of similar age from Algorithms for Molecular Biology
#3
of 6 outputs
Altmetric has tracked 24,862,067 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 257 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 49th percentile – i.e., 49% of its peers scored the same or lower than it.
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 269,787 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.