Software

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

Roland Schwarz¹ , Patrick Musch² , Axel von Kamp³ , Bernd Engels² , Heiner Schirmer⁴ , Stefan Schuster³ and Thomas Dandekar^1,5

¹  Dept of Bioinformatics, Biocenter, University of Würzburg; Germany

²  Dept of Theoretical Chemistry, Organikum, University of Würzburg, Germany

³  Dept of Bioinformatics, University of Jena, Germany

⁴  Center for Biochemistry (BZH), University of Heidelberg, Germany

⁵  Structural and Computational Biology, EMBL, Heidelberg, Germany

author email corresponding author email

BMC Bioinformatics 2005, 6:135doi:10.1186/1471-2105-6-135

Published:	1 June 2005

Abstract

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 webcite.

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.