YANA - a software tool for analyzing flux modes, gene-expression and enzyme activities

被引:55
作者
Schwarz, R
Musch, P
von Kamp, A
Engels, B
Schirmer, H
Schuster, S
Dandekar, T [1 ]
机构
[1] Univ Wurzburg, Bioctr, Dept Bioinformat, D-97070 Wurzburg, Germany
[2] Univ Wurzburg, Organikum, Dept Theoret Chem, D-97070 Wurzburg, Germany
[3] Univ Jena, Dept Bioinformat, D-6900 Jena, Germany
[4] Heidelberg Univ, Ctr Biochem BZH, D-6900 Heidelberg, Germany
[5] EMBL, Heidelberg, Germany
关键词
D O I
10.1186/1471-2105-6-135
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Background: A number of algorithms for steady state analysis of metabolic networks have been developed over the years. Of these, Elementary Mode Analysis (EMA) has proven especially useful. Despite its low user-friendliness, METATOOL as a reliable high-performance implementation of the algorithm has been the instrument of choice up to now. As reported here, the analysis of metabolic networks has been improved by an editor and analyzer of metabolic flux modes. Analysis routines for expression levels and the most central, well connected metabolites and their metabolic connections are of particular interest. Results: YANA features a platform-independent, dedicated toolbox for metabolic networks with a graphical user interface to calculate (integrating METATOOL), edit (including support for the SBML format), visualize, centralize, and compare elementary flux modes. Further, YANA calculates expected flux distributions for a given Elementary Mode (EM) activity pattern and vice versa. Moreover, a dissection algorithm, a centralization algorithm, and an average diameter routine can be used to simplify and analyze complex networks. Proteomics or gene expression data give a rough indication of some individual enzyme activities, whereas the complete flux distribution in the network is often not known. As such data are noisy, YANA features a fast evolutionary algorithm (EA) for the prediction of EM activities with minimum error, including alerts for inconsistent experimental data. We offer the possibility to include further known constraints (e. g. growth constraints) in the EA calculation process. The redox metabolism around glutathione reductase serves as an illustration example. All software and documentation are available for download at http://yana.bioapps.biozentrum.uni-wuerzburg.de. Conclusion: A graphical toolbox and an editor for METATOOL as well as a series of additional routines for metabolic network analyses constitute a new user-friendly software for such efforts.
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页数:12
相关论文
共 38 条
[1]   Antioxidant defense in Plasmodium falciparum -: data mining of the transcriptome -: art. no. 23 [J].
Bozdech, Z ;
Ginsburg, H .
MALARIA JOURNAL, 2004, 3 (1)
[2]  
*BSD, BSD OPENSOURCE LIC
[3]   Fundamental Escherichia coli biochemical pathways for biomass and energy production:: Creation of overall flux states [J].
Carlson, R ;
Srienc, F .
BIOTECHNOLOGY AND BIOENGINEERING, 2004, 86 (02) :149-162
[4]   Fundamental Escherichia coli biochemical pathways for biomass and energy production:: Identification of reactions [J].
Carlson, R ;
Srienc, F .
BIOTECHNOLOGY AND BIOENGINEERING, 2004, 85 (01) :1-19
[5]   Comparative genome analysis and pathway reconstruction [J].
Dandekar, T ;
Sauerborn, R .
PHARMACOGENOMICS, 2002, 3 (02) :245-256
[6]   A method for classifying metabolites in topological pathway analyses based on minimization of pathway number [J].
Dandekar, T ;
Moldenhauer, F ;
Bulik, S ;
Bertram, H ;
Schuster, S .
BIOSYSTEMS, 2003, 70 (03) :255-270
[7]  
Finney A, 2003, BIOCHEM SOC T, V31, P1472
[8]   Computation of elementary modes: a unifying framework and the new binary approach [J].
Gagneur, J ;
Klamt, S .
BMC BIOINFORMATICS, 2004, 5 (1)
[9]   Hierarchical analysis of dependency in metabolic networks [J].
Gagneur, J ;
Jackson, DB ;
Casari, G .
BIOINFORMATICS, 2003, 19 (08) :1027-1034
[10]   Global analysis of protein expression in yeast [J].
Ghaemmaghami, S ;
Huh, W ;
Bower, K ;
Howson, RW ;
Belle, A ;
Dephoure, N ;
O'Shea, EK ;
Weissman, JS .
NATURE, 2003, 425 (6959) :737-741