Special Issue Article

Multi-way analysis of flux distributions across multiple conditions

  1. Maikel P. H. Verouden1,
  2. Richard A. Notebaart2,
  3. Johan A. Westerhuis2,*,
  4. Mariët J. van der Werf3,
  5. Bas Teusink4,
  6. Age K. Smilde1

Article first published online: 18 MAY 2009

DOI: 10.1002/cem.1238

Issue

Journal of Chemometrics

Journal of Chemometrics

Special Issue: In Honor of Professor Richard A. Harshman

Volume 23, Issue 7-8, pages 406–420, July - August 2009

Additional Information

How to Cite

Verouden, M. P. H., Notebaart, R. A., Westerhuis, J. A., van der Werf, M. J., Teusink, B. and Smilde, A. K. (2009), Multi-way analysis of flux distributions across multiple conditions. J. Chemometrics, 23: 406–420. doi: 10.1002/cem.1238

Author Information

  1. 1

    Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands

  2. 2

    Centre for Molecular and Biomolecular Informatics, Radboud University Nijmegen, P.O.Box 9010, 6500 GL Nijmegen, The Netherlands

  3. 3

    TNO Quality of Life, P.O.Box 360, 3700 AJ Zeist, The Netherlands

  4. 4

    Systems Bioinformatics, Centre for Integrative Bioinformatics, VU University Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands

*Biosystems Data Analysis, Swammerdam Institute for Life Sciences, University of Amsterdam, Nieuwe Achtergracht 166, 1018 WV Amsterdam, The Netherlands.

Publication History

  1. Issue published online: 11 AUG 2009
  2. Article first published online: 18 MAY 2009
  3. Manuscript Accepted: 6 MAR 2009
  4. Manuscript Revised: 23 FEB 2009
  5. Manuscript Received: 12 OCT 2008

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Keywords:

  • PARAFAC;
  • PCA;
  • correlation;
  • flux distributions;
  • genome-scale network

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. Copyright © 2009 John Wiley & Sons, Ltd.

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