Principal elementary mode analysis (PEMA)

Molecular BioSystems
Abel Folch-FortunyAlberto Ferrer

Abstract

Principal component analysis (PCA) has been widely applied in fluxomics to compress data into a few latent structures in order to simplify the identification of metabolic patterns. These latent structures lack a direct biological interpretation due to the intrinsic constraints associated with a PCA model. Here we introduce a new method that significantly improves the interpretability of the principal components with a direct link to metabolic pathways. This method, called principal elementary mode analysis (PEMA), establishes a bridge between a PCA-like model, aimed at explaining the maximum variance in flux data, and the set of elementary modes (EMs) of a metabolic network. It provides an easy way to identify metabolic patterns in large fluxomics datasets in terms of the simplest pathways of the organism metabolism. The results using a real metabolic model of Escherichia coli show the ability of PEMA to identify the EMs that generated the different simulated flux distributions. Actual flux data of E. coli and Pichia pastoris cultures confirm the results observed in the simulated study, providing a biologically meaningful model to explain flux data of both organisms in terms of the EM activation. The PEMA toolbox is freely avai...Continue Reading

References

Sep 21, 2002·Molecular Biology Reports·Steffen Klamt, Jörg Stelling
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Jan 6, 2004·Biotechnology and Bioengineering·Ross Carlson, Friedrich Srienc
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Aug 19, 2010·BMC Systems Biology·Marta TortajadaJesús Picó
Oct 26, 2010·BMC Systems Biology·Kristin BaumannPau Ferrer
Jul 31, 2014·BMC Systems Biology·Lake-Ee Quek, Lars K Nielsen

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Citations

Feb 9, 2018·Bioinformatics·Sahely BhadraJuho Rousu
Jul 12, 2019·PLoS Computational Biology·Guido ZampieriClaudio Angione
Jun 20, 2018·BMC Systems Biology·Abel Folch-FortunyAlberto Ferrer
Nov 27, 2020·Computational and Structural Biotechnology Journal·Athanasios AntonakoudisCleo Kontoravdi

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