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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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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

Piotr Zakrzewski, Marnix H. Medema, Albert Gevorgyan, Andrzej M. Kierzek, Rainer Breitling, Eriko Takano

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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The data shown below were compiled from readership statistics for 120 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 3%
Netherlands 1 <1%
Brazil 1 <1%
Singapore 1 <1%
Iran, Islamic Republic of 1 <1%
Slovenia 1 <1%
Argentina 1 <1%
Denmark 1 <1%
Thailand 1 <1%
Other 1 <1%
Unknown 108 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 30%
Researcher 30 25%
Student > Master 16 13%
Student > Bachelor 7 6%
Professor > Associate Professor 6 5%
Other 13 11%
Unknown 12 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 49 41%
Biochemistry, Genetics and Molecular Biology 15 13%
Computer Science 13 11%
Engineering 6 5%
Environmental Science 4 3%
Other 17 14%
Unknown 16 13%