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Discrimination of metabolic flux profiles using a hybrid evolutionary algorithm

Published: 07 July 2007 Publication History

Abstract

Studying metabolic fluxes is a crucial aspect of understanding biological phenotypes. However, it is often not possible to measure these fluxes directly. As an alternative, fluxome profiling provides indirect information about fluxes in a high-throughput setting. In this paper, we consider a scenario where fluxome profiling is used to investigate characteristic differences between a number of bacterial mutant strains. The goal is to identify groups of mutants that show maximally different fluxome profiles. We propose an evolutionary algorithm for this optimization problem and demonstrate that it outperforms alternative methods based on principle component analysis and independent component analysis on both real and synthetic data sets.

References

[1]
S. Bleuler, A. Prelić, and E. Zitzler. An EA framework for biclustering of gene expression data. In Congress on Evolutionary Computation (CEC-2004), pages 166--173, Piscataway, NJ, 2004. IEEE.
[2]
M. Dauner, J. E. Bailey, and U. Sauer. Metabolic flux analysis with a comprehensive isotopomer model in bacillus subtilis. Biotechnol Bioeng, 76:144--156, 2001.
[3]
E. Falkenauer. Genetic Algorithms and Grouping Problems. John Wiley & Sons, 1998.
[4]
E. Fischer and U. Sauer. Metabolic flux profinig of Escherichia coli mutants in central carbon metabolism using gc-ms. Eur. J. Biochem., 270:880--891, 2003.
[5]
J. Handl and J. Knowles. Evolutionary multiobjective clustering. In Parallel Problem Solving from Nature (PPSN VIII), volume 3242 of LNCS, pages 1081--1091. Springer, 2004.
[6]
J. Handl, J. Knowles, and D. B. Kell. Computational Cluster Validation in Post-Genomic Data Analysis. Bioinformatics, 21(15):3201--3212, 2005.
[7]
A. Hyvärinen. Survey on independent component analysis. Neural Computing Serveys, 2:94--128, 1999.
[8]
P. J. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20:53--65, 1987.
[9]
U. Sauer. High-throughput phenomics: experimental methods for mapping fluxomes. Curr. Opin. Biotechnol., 15(1):58--63, 2004.
[10]
M. Scholz, S. Gatznek, A. Sterling, O. Fiehn, and J. Selbig. Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics, 20(15):2447--2454, 2004.
[11]
W. Wiechert, M. Möllney, N. Isermann, M. Wurzel, and A. A. de Graaf. Bidirectional reaction steps in metabolic networks: {III.} explicit solution and analysis of isotopomer labeling systems. Biotechnol Bioeng, 66:69--85, 1999.
[12]
N. Zamboni and U. Sauer. Model-independent fluxome profiling from 2H and 13C experiments for metabolic variant discrimination. Genome Biology, 5(R99), 2004.

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  1. Discrimination of metabolic flux profiles using a hybrid evolutionary algorithm

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    cover image ACM Conferences
    GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
    July 2007
    2313 pages
    ISBN:9781595936974
    DOI:10.1145/1276958
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    Published: 07 July 2007

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    Author Tags

    1. biological application
    2. evolutionary algorithm
    3. fluxome analysis

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    GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
    Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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