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MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

Overview of attention for article published in PLOS ONE, December 2012
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Article details
Title
MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models
Published in
PLOS ONE, December 2012
DOI 10.1371/journal.pone.0051511
Pubmed ID
Authors
Abstract

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads.

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X Demographics

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United Kingdom 3 2%
Thailand 1 <1%
Slovenia 1 <1%
Singapore 1 <1%
Japan 1 <1%
Iran, Islamic Republic of 1 <1%
Denmark 1 <1%
Brazil 1 <1%
Argentina 1 <1%
Other 0 0%
Unknown 112 91%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 36 29%
Researcher 31 25%
Student > Master 15 12%
Student > Bachelor 7 6%
Professor > Associate Professor 6 5%
Other 15 12%
Unknown 13 11%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 48 39%
Biochemistry, Genetics and Molecular Biology 16 13%
Computer Science 13 11%
Engineering 6 5%
Immunology and Microbiology 4 3%
Other 19 15%
Unknown 17 14%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 16 January 2013.
All research outputs
#21,399,274
of 34,358,242 outputs
Outputs from PLOS ONE
#126,561
of 224,513 outputs
Outputs of similar age
#232,871
of 344,437 outputs
Outputs of similar age from PLOS ONE
#3,018
of 5,144 outputs
Altmetric has tracked 34,358,242 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 224,513 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 17.2. This one is in the 43rd percentile – i.e., 43% 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 344,437 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 32nd percentile – i.e., 32% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 5,144 others from the same source and published within six weeks on either side of this one. This one is in the 40th percentile – i.e., 40% of its contemporaries scored the same or lower than it.