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An algorithm for the reduction of genome-scale metabolic network models to meaningful core models

Overview of attention for article published in BMC Systems Biology, August 2015
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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 (85th percentile)

Mentioned by

blogs
1 blog
twitter
6 tweeters

Citations

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53 Dimensions

Readers on

mendeley
207 Mendeley
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2 CiteULike
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Title
An algorithm for the reduction of genome-scale metabolic network models to meaningful core models
Published in
BMC Systems Biology, August 2015
DOI 10.1186/s12918-015-0191-x
Pubmed ID
Authors

Philipp Erdrich, Ralf Steuer, Steffen Klamt

Abstract

Constraint-based analysis of genome-scale metabolic models has become a key methodology to gain insights into functions, capabilities, and properties of cellular metabolism. Since their inception, the size and complexity of genome-scale metabolic reconstructions has significantly increased, with a concomitant increase in computational effort required for their analysis. Many stoichiometric methods cannot be applied to large networks comprising several thousand reactions. Furthermore, basic principles of an organism's metabolism can sometimes be easier studied in smaller models focusing on central metabolism. Therefore, an automated and unbiased reduction procedure delivering meaningful core networks from well-curated genome-scale reconstructions is highly desirable. Here we present NetworkReducer, a new algorithm for an automated reduction of metabolic reconstructions to obtain smaller models capturing the central metabolism or other metabolic modules of interest. The algorithm takes as input a network model and a list of protected elements and functions (phenotypes) and applies a pruning step followed by an optional compression step. Network pruning removes elements of the network that are dispensable for the protected functions and delivers a subnetwork of the full system. Loss-free network compression further reduces the network size but not the complexity (dimension) of the solution space. As a proof of concept, we applied NetworkReducer to the iAF1260 genome-scale model of Escherichia coli (2384 reactions, 1669 internal metabolites) to obtain a reduced model that (i) allows the same maximal growth rates under aerobic and anaerobic conditions as in the full model, and (ii) preserves a protected set of reactions representing the central carbon metabolism. The reduced representation comprises 85 metabolites and 105 reactions which we compare to a manually derived E. coli core model. As one particular strength of our approach, NetworkReducer derives a condensed biomass synthesis reaction that is consistent with the full genome-scale model. In a second case study, we reduced a genome-scale model of the cyanobacterium Synechocystis sp. PCC 6803 to obtain a small metabolic module comprising photosynthetic core reactions and the Calvin-Benson cycle allowing synthesis of both biomass and a biofuel (ethanol). Although only genome-scale models provide a complete description of an organism's metabolic capabilities, an unbiased stoichiometric reduction of large-scale metabolic models is highly useful. We are confident that the NetworkReducer algorithm provides a valuable tool for the application of computationally expensive analyses, for educational purposes, as well to identify core models for kinetic modeling and isotopic tracer experiments.

Twitter Demographics

The data shown below were collected from the profiles of 6 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 207 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Sweden 1 <1%
South Africa 1 <1%
Singapore 1 <1%
Colombia 1 <1%
Mexico 1 <1%
Iran, Islamic Republic of 1 <1%
Russia 1 <1%
China 1 <1%
Other 0 0%
Unknown 197 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 50 24%
Researcher 45 22%
Student > Master 36 17%
Student > Bachelor 18 9%
Student > Doctoral Student 13 6%
Other 21 10%
Unknown 24 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 60 29%
Biochemistry, Genetics and Molecular Biology 44 21%
Engineering 23 11%
Computer Science 15 7%
Chemical Engineering 9 4%
Other 27 13%
Unknown 29 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 28 March 2016.
All research outputs
#1,947,090
of 16,122,996 outputs
Outputs from BMC Systems Biology
#75
of 1,107 outputs
Outputs of similar age
#34,852
of 240,813 outputs
Outputs of similar age from BMC Systems Biology
#1
of 3 outputs
Altmetric has tracked 16,122,996 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,107 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done particularly well, scoring higher than 93% 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 240,813 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 85% 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. This one has scored higher than all of them