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Software LS-MIDA for efficient mass isotopomer distribution analysis in metabolic modelling

Zeeshan Ahmed17, Saman Zeeshan18, Claudia Huber5, Michael Hensel2, Dietmar Schomburg3, Richard Münch4, Wolfgang Eisenreich5 and Thomas Dandekar16*

Author Affiliations

1 Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany

2 Department of Microbiology, University of Osnabrück, Osnabrück, Germany

3 Department of Bioinformatics and Biochemistry, Technical University Braunschweig, Braunschweig, Germany

4 Institute for Microbiology, Technical University Braunschweig, Braunschweig, Germany

5 Lehrstuhl für Biochemie, Technische Universität München, München, Germany

6 EMBL, Structural and Computational Biology Unit, Heidelberg, Germany

7 Department of Neurobiology and Genetics, Biocenter, University of Würzburg, Würzburg, Germany

8 Institute of Molecular and Translational Therapeutic Strategies, Hannover Medical School, Hanover, Germany

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BMC Bioinformatics 2013, 14:218  doi:10.1186/1471-2105-14-218

Published: 9 July 2013



The knowledge of metabolic pathways and fluxes is important to understand the adaptation of organisms to their biotic and abiotic environment. The specific distribution of stable isotope labelled precursors into metabolic products can be taken as fingerprints of the metabolic events and dynamics through the metabolic networks. An open-source software is required that easily and rapidly calculates from mass spectra of labelled metabolites, derivatives and their fragments global isotope excess and isotopomer distribution.


The open-source software “Least Square Mass Isotopomer Analyzer” (LS-MIDA) is presented that processes experimental mass spectrometry (MS) data on the basis of metabolite information such as the number of atoms in the compound, mass to charge ratio (m/e or m/z) values of the compounds and fragments under study, and the experimental relative MS intensities reflecting the enrichments of isotopomers in 13C- or 15 N-labelled compounds, in comparison to the natural abundances in the unlabelled molecules. The software uses Brauman’s least square method of linear regression. As a result, global isotope enrichments of the metabolite or fragment under study and the molar abundances of each isotopomer are obtained and displayed.


The new software provides an open-source platform that easily and rapidly converts experimental MS patterns of labelled metabolites into isotopomer enrichments that are the basis for subsequent observation-driven analysis of pathways and fluxes, as well as for model-driven metabolic flux calculations.