↓ Skip to main content

Autocatalytic sets in E. coli metabolism

Overview of attention for article published in Journal of Systems Chemistry, April 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)

Mentioned by

2 blogs
4 tweeters


52 Dimensions

Readers on

69 Mendeley
Autocatalytic sets in E. coli metabolism
Published in
Journal of Systems Chemistry, April 2015
DOI 10.1186/s13322-015-0009-7
Pubmed ID

Filipa L Sousa, Wim Hordijk, Mike Steel, William F Martin


A central unsolved problem in early evolution concerns self-organization towards higher complexity in chemical reaction networks. In theory, autocatalytic sets have useful properties to help model such transitions. Autocatalytic sets are chemical reaction systems in which molecules belonging to the set catalyze the synthesis of other members of the set. Given an external supply of starting molecules - the food set - and the conditions that (i) all reactions are catalyzed by at least one molecule, and (ii) each molecule can be constructed from the food set by a sequence of reactions, the system becomes a reflexively autocatalytic food-generated network (RAF set). Autocatalytic networks and RAFs have been studied extensively as mathematical models for understanding the properties and parameters that influence self-organizational tendencies. However, despite their appeal, the relevance of RAFs for real biochemical networks that exist in nature has, so far, remained virtually unexplored. Here we investigate the best-studied metabolic network, that of Escherichia coli, for the existence of RAFs. We find that the largest RAF encompasses almost the entire E. coli cytosolic reaction network. We systematically study its structure by considering the impact of removing catalysts or reactions. We show that, without biological knowledge, finding the minimum food set that maintains a given RAF is NP-complete. We apply a randomized algorithm to find (approximately) smallest subsets of the food set that suffice to sustain the original RAF. The existence of RAF sets within a microbial metabolic network indicates that RAFs capture properties germane to biological organization at the level of single cells. Moreover, the interdependency between the different metabolic modules, especially concerning cofactor biosynthesis, points to the important role of spontaneous (non-enzymatic) reactions in the context of early evolution. Graphical AbstractE. coli metabolic network in the context of autocatalytic sets.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Japan 1 1%
Unknown 66 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 22%
Student > Master 13 19%
Researcher 12 17%
Student > Bachelor 5 7%
Professor 4 6%
Other 9 13%
Unknown 11 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 22%
Biochemistry, Genetics and Molecular Biology 10 14%
Physics and Astronomy 6 9%
Mathematics 6 9%
Computer Science 6 9%
Other 13 19%
Unknown 13 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 17 September 2021.
All research outputs
of 20,501,648 outputs
Outputs from Journal of Systems Chemistry
of 24 outputs
Outputs of similar age
of 238,378 outputs
Outputs of similar age from Journal of Systems Chemistry
of 1 outputs
Altmetric has tracked 20,501,648 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 24 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.1. This one scored the same or higher as 20 of them.
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 238,378 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 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them