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Interaction networks for identifying coupled molecular processes in microbial communities

Overview of attention for article published in BioData Mining, July 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (78th percentile)
  • Good Attention Score compared to outputs of the same age and source (70th percentile)

Mentioned by

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6 X users
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2 Wikipedia pages
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1 Google+ user

Readers on

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49 Mendeley
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Title
Interaction networks for identifying coupled molecular processes in microbial communities
Published in
BioData Mining, July 2015
DOI 10.1186/s13040-015-0054-4
Pubmed ID
Authors

Magnus Bosse, Alexander Heuwieser, Andreas Heinzel, Ivan Nancucheo, Hivana Melo Barbosa Dall’Agnol, Arno Lukas, George Tzotzos, Bernd Mayer

Abstract

Microbial communities adapt to environmental conditions for optimizing metabolic flux. Such adaption may include cooperative mechanisms eventually resulting in phenotypic observables as emergent properties that cannot be attributed to an individual species alone. Understanding the molecular basis of cross-species cooperation adds to utilization of microbial communities in industrial applications including metal bioleaching and bioremediation processes. With significant advancements in metagenomics the composition of microbial communities became amenable for integrative analysis on the level of entangled molecular processes involving more than one species, in turn offering a data matrix for analyzing the molecular basis of cooperative phenomena. We present an analysis framework aligned with a dynamical hierarchies concept for unraveling emergent properties in microbial communities, and exemplify this approach for a co-culture setting of At. ferrooxidans and At. thiooxidans. This minimum microbial community demonstrates a significant increase in bioleaching efficiency compared to the activity of individual species, involving mechanisms of the thiosulfate, the polysulfide and the iron oxidation pathway. Populating gene-centric data structures holding rich functional annotation and interaction information allows deriving network models at the functional level coupling energy production and transport processes of both microbial species. Applying a network segmentation approach on the interaction network of ortholog genes covering energy production and transport proposes a set of specific molecular processes of relevance in bioleaching. The resulting molecular process model essentially involves functionalities such as iron oxidation, nitrogen metabolism and proton transport, complemented by sulfur oxidation and nitrogen metabolism, as well as a set of ion transporter functionalities. At. ferrooxidans-specific genes embedded in the molecular model representation hold gene functions supportive for ammonia utilization as well as for biofilm formation, resembling key elements for effective chalcopyrite bioleaching as emergent property in the co-culture situation. Analyzing the entangled molecular processes of a microbial community on the level of segmented, gene-centric interaction networks allows identification of core molecular processes and functionalities adding to our mechanistic understanding of emergent properties of microbial consortia.

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Brazil 2 4%
Germany 1 2%
Portugal 1 2%
Spain 1 2%
United States 1 2%
Unknown 43 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 35%
Student > Ph. D. Student 9 18%
Student > Master 8 16%
Student > Bachelor 6 12%
Student > Postgraduate 3 6%
Other 4 8%
Unknown 2 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 33%
Environmental Science 10 20%
Biochemistry, Genetics and Molecular Biology 6 12%
Immunology and Microbiology 6 12%
Computer Science 4 8%
Other 2 4%
Unknown 5 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 December 2023.
All research outputs
#4,702,527
of 24,885,505 outputs
Outputs from BioData Mining
#98
of 320 outputs
Outputs of similar age
#54,571
of 268,074 outputs
Outputs of similar age from BioData Mining
#4
of 10 outputs
Altmetric has tracked 24,885,505 research outputs across all sources so far. Compared to these this one has done well and is in the 80th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 320 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.5. This one has gotten more attention than average, scoring higher than 68% 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 268,074 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 78% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 6 of them.