FASTGAPFILL: Efficient gap filling in metabolic networks

Ines Thiele; Nikos Vlassis; Ronan M.T. Fleming

doi:10.1093/bioinformatics/btu321

FASTGAPFILL: Efficient gap filling in metabolic networks

University of Luxembourg

Research output: Contribution to a Journal (Peer & Non Peer) › Article › peer-review

98 Citations (Scopus)

Abstract

Motivation: Genome-scale metabolic reconstructions summarize current knowledge about a target organism in a structured manner and as such highlight missing information. Such gaps can be filled algorithmically. Scalability limitations of available algorithms for gap filling hinder their application to compartmentalized reconstructions. Results: We present FASTGAPFILL, a computationally efficient tractable extension to the COBRA toolbox that permits the identification of candidate missing knowledge from a universal biochemical reaction database (e.g. Kyoto Encyclopedia of Genes and Genomes) for a given (compartmentalized) metabolic reconstruction. The stoichiometric consistency of the universal reaction database and of the metabolic reconstruction can be tested for permitting the computation of biologically more relevant solutions. We demonstrate the efficiency and scalability of FASTGAPFILL on a range of metabolic reconstructions.

Original language	English
Pages (from-to)	2529-2531
Number of pages	3
Journal	Bioinformatics
Volume	30
Issue number	17
DOIs	https://doi.org/10.1093/bioinformatics/btu321
Publication status	Published - 1 Sep 2014
Externally published	Yes

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10.1093/bioinformatics/btu321

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year = "2014",

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AU - Thiele, Ines

AU - Vlassis, Nikos

AU - Fleming, Ronan M.T.

PY - 2014/9/1

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N2 - Motivation: Genome-scale metabolic reconstructions summarize current knowledge about a target organism in a structured manner and as such highlight missing information. Such gaps can be filled algorithmically. Scalability limitations of available algorithms for gap filling hinder their application to compartmentalized reconstructions. Results: We present FASTGAPFILL, a computationally efficient tractable extension to the COBRA toolbox that permits the identification of candidate missing knowledge from a universal biochemical reaction database (e.g. Kyoto Encyclopedia of Genes and Genomes) for a given (compartmentalized) metabolic reconstruction. The stoichiometric consistency of the universal reaction database and of the metabolic reconstruction can be tested for permitting the computation of biologically more relevant solutions. We demonstrate the efficiency and scalability of FASTGAPFILL on a range of metabolic reconstructions.

AB - Motivation: Genome-scale metabolic reconstructions summarize current knowledge about a target organism in a structured manner and as such highlight missing information. Such gaps can be filled algorithmically. Scalability limitations of available algorithms for gap filling hinder their application to compartmentalized reconstructions. Results: We present FASTGAPFILL, a computationally efficient tractable extension to the COBRA toolbox that permits the identification of candidate missing knowledge from a universal biochemical reaction database (e.g. Kyoto Encyclopedia of Genes and Genomes) for a given (compartmentalized) metabolic reconstruction. The stoichiometric consistency of the universal reaction database and of the metabolic reconstruction can be tested for permitting the computation of biologically more relevant solutions. We demonstrate the efficiency and scalability of FASTGAPFILL on a range of metabolic reconstructions.

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DO - 10.1093/bioinformatics/btu321

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SP - 2529

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JO - Bioinformatics

JF - Bioinformatics

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ER -

FASTGAPFILL: Efficient gap filling in metabolic networks

Abstract

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