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Emergent Biosynthetic Capacity in Simple Microbial Communities

Overview of attention for article published in PLoS Computational Biology, July 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (68th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

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7 X users

Citations

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90 Dimensions

Readers on

mendeley
273 Mendeley
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4 CiteULike
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Title
Emergent Biosynthetic Capacity in Simple Microbial Communities
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003695
Pubmed ID
Authors

Hsuan-Chao Chiu, Roie Levy, Elhanan Borenstein

Abstract

Microbes have an astonishing capacity to transform their environments. Yet, the metabolic capacity of a single species is limited and the vast majority of microorganisms form complex communities and join forces to exhibit capabilities far exceeding those achieved by any single species. Such enhanced metabolic capacities represent a promising route to many medical, environmental, and industrial applications and call for the development of a predictive, systems-level understanding of synergistic microbial capacity. Here we present a comprehensive computational framework, integrating high-quality metabolic models of multiple species, temporal dynamics, and flux variability analysis, to study the metabolic capacity and dynamics of simple two-species microbial ecosystems. We specifically focus on detecting emergent biosynthetic capacity - instances in which a community growing on some medium produces and secretes metabolites that are not secreted by any member species when growing in isolation on that same medium. Using this framework to model a large collection of two-species communities on multiple media, we demonstrate that emergent biosynthetic capacity is highly prevalent. We identify commonly observed emergent metabolites and metabolic reprogramming patterns, characterizing typical mechanisms of emergent capacity. We further find that emergent secretion tends to occur in two waves, the first as soon as the two organisms are introduced, and the second when the medium is depleted and nutrients become limited. Finally, aiming to identify global community determinants of emergent capacity, we find a marked association between the level of emergent biosynthetic capacity and the functional/phylogenetic distance between community members. Specifically, we demonstrate a "Goldilocks" principle, where high levels of emergent capacity are observed when the species comprising the community are functionally neither too close, nor too distant. Taken together, our results demonstrate the potential to design and engineer synthetic communities capable of novel metabolic activities and point to promising future directions in environmental and clinical bioengineering.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 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 273 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 3%
Colombia 1 <1%
Switzerland 1 <1%
France 1 <1%
Saudi Arabia 1 <1%
Mexico 1 <1%
Chile 1 <1%
Belgium 1 <1%
Argentina 1 <1%
Other 2 <1%
Unknown 254 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 69 25%
Researcher 58 21%
Student > Master 26 10%
Student > Bachelor 21 8%
Student > Doctoral Student 18 7%
Other 49 18%
Unknown 32 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 90 33%
Biochemistry, Genetics and Molecular Biology 44 16%
Engineering 20 7%
Environmental Science 17 6%
Computer Science 14 5%
Other 48 18%
Unknown 40 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 13 February 2015.
All research outputs
#8,045,790
of 25,604,262 outputs
Outputs from PLoS Computational Biology
#5,328
of 9,014 outputs
Outputs of similar age
#72,513
of 242,613 outputs
Outputs of similar age from PLoS Computational Biology
#74
of 162 outputs
Altmetric has tracked 25,604,262 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 9,014 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 39th percentile – i.e., 39% 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 242,613 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 68% of its contemporaries.
We're also able to compare this research output to 162 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.