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Titre du document / Document title

Multi-way analysis of flux distributions across multiple conditions

Auteur(s) / Author(s)

VEROUDEN Maikel P. H. (1) ; NOTEBAART Richard A. (2) ; WESTERHUIS Johan A. (1) ; VAN DER WERF Mariët J. (3) ; TEUSINK Bas (4) ; SMILDE Age K. (1) ;

Affiliation(s) du ou des auteurs / Author(s) Affiliation(s)

(1) Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 wvAmsterdam, PAYS-BAS
(2) Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen, P.O.Box 9010, 6500 GL Nijmegen, PAYS-BAS
(3) TNO Quality of Life, P.O.Box 360, 3700AJZeist, PAYS-BAS
(4) Systems Bioinformatics, Centre for Integrative Bioinformatics, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, PAYS-BAS

Résumé / Abstract

With the availability of genome sequences of many organisms and information about gene-protein-reaction (GPR) associations with respect to these organisms genome-scale metabolic networks can be reconstructed. In cellular systems biology these networks are used to model the behavior of metabolism in context of cell growth in terms of fluxes (reaction rates) through reactions in the network. Because the flux through each reaction can generally vary within a range, many flux distributions of the entire network are possible. However, since reactions are connected by common metabolites, reactions that are functionally coherent, are expected to highly correlate in terms of their flux value over different flux distributions. In this paper the genome-scale network of a lactic acid bacterium, named Lactococcus lactis MG1363, is used to generate flux distributions for multiple in silico environmental conditions, mimicking laboratory growth conditions. The flux distributions per condition are used to calculate a correlation matrix for each condition. Subsequently the correlations between the reactions are analyzed in a multivariate approach across the in silico environmental conditions in order to identify correlations that are invariant (i.e. independent of the environment) and correlations that are variant across conditions (i.e. dependent of the environment). The applied multivariate methods are Parallel Factor Analysis (PARAFAC) and Principal Component Analysis (PCA). The discussion of the results of both methods leads to the question whether latent variable models are suitable analyzing this type of data.

Revue / Journal Title

Journal of chemometrics    ISSN  0886-9383   CODEN JOCHEU 

Source / Source

2009, vol. 23, no7-8, pp. 406-420 [15 page(s) (article)] (40 ref.)

Langue / Language

Anglais

Editeur / Publisher

Wiley, Bognor Regis, ROYAUME-UNI  (1987) (Revue)

Mots-clés anglais / English Keywords

Genome

;

Principal component analysis

;

Factor analysis

;

Environment

;

Laboratory

;

Lactic acid

;

Reaction rate

;

Growth

;

Association

;

Protein

;

Chemometrics

;

Correlation

;

Distribution

;

Mots-clés français / French Keywords

Modèle PARAFAC

;

Génome

;

Analyse composante principale

;

Analyse factorielle

;

Environnement

;

Laboratoire

;

Acide lactique

;

Vitesse réaction

;

Croissance

;

Association

;

Protéine

;

Chimiométrie

;

Corrélation

;

Distribution

;

Mots-clés espagnols / Spanish Keywords

Genoma

;

Análisis componente principal

;

Análisis factorial

;

Medio ambiente

;

Laboratorio

;

Láctico ácido

;

Velocidad reacción

;

Crecimiento

;

Asociación

;

Proteína

;

Quimiometría

;

Correlación

;

Distribución

;

Mots-clés d'auteur / Author Keywords

PARAFAC

;

PCA

;

correlation

;

flux distributions

;

genome-scale network

;

Localisation / Location

INIST-CNRS, Cote INIST : 21197, 35400018757303.0090

Nº notice refdoc (ud4) : 21820017



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