Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms

BMC Bioinformatics
Rainer KönigRoland Eils

Abstract

Microarray technology produces gene expression data on a genomic scale for an endless variety of organisms and conditions. However, this vast amount of information needs to be extracted in a reasonable way and funneled into manageable and functionally meaningful patterns. Genes may be reasonably combined using knowledge about their interaction behaviour. On a proteomic level, biochemical research has elucidated an increasingly complete image of the metabolic architecture, especially for less complex organisms like the well studied bacterium Escherichia coli. We sought to discover central components of the metabolic network, regulated by the expression of associated genes under changing conditions. We mapped gene expression data from E. coli under aerobic and anaerobic conditions onto the enzymatic reaction nodes of its metabolic network. An adjacency matrix of the metabolites was created from this graph. A consecutive ones clustering method was used to obtain network clusters in the matrix. The wavelet method was applied on the adjacency matrices of these clusters to collect features for the classifier. With a feature extraction method the most discriminating features were selected. We yielded network sub-graphs from these top ...Continue Reading

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Citations

Aug 8, 2014·PLoS Computational Biology·Cheemeng TanLingchong You
Jul 26, 2008·BMC Systems Biology·Kitiporn PlaimasRainer König
Jan 1, 2012·Metabolites·Andreas Hoppe
May 20, 2020·Revista Da Sociedade Brasileira De Medicina Tropical·Leila Maria FerreiraJuliano Lino Ferreira

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Methods Mentioned

BETA
feature extraction

Software Mentioned

MathType
EcoCyc
R MCRestimate package

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