Software Supporting a Workflow of Quantitative Dynamic Flux Maps Estimation in Central Metabolism from SIRM Experimental Data

  • Vitaly A. SelivanovEmail author
  • Silvia Marin
  • Josep Tarragó-Celada
  • Andrew N. Lane
  • Richard M. Higashi
  • Teresa W.-M. Fan
  • Pedro de Atauri
  • Marta CascanteEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2088)


Stable isotope-resolved metabolomics (SIRM), based on the analysis of biological samples from living cells incubated with artificial isotope enriched substrates, enables mapping the rates of biochemical reactions (metabolic fluxes). We developed software supporting a workflow of analysis of SIRM data obtained with mass spectrometry (MS). The evaluation of fluxes starting from raw MS recordings requires at least three steps of computer support: first, extraction of mass spectra of metabolites of interest, then correction of the spectra for natural isotope abundance, and finally, evaluation of fluxes by simulation of the corrected spectra using a corresponding mathematical model. A kinetic model based on ordinary differential equations (ODEs) for isotopomers of metabolites of the corresponding biochemical network supports the final part of the analysis, which provides a dynamic flux map.

Key words

Mass spectrometry Stable isotope tracing Isotopolog distribution Central energy metabolism Metabolic fluxes Computational analysis Kinetic models of metabolism Stable isotope-resolved metabolomics 











Flux balance analysis


Fructose bisphosphate






Gas chromatography–mass spectrometry














Mass spectrometry


Nuclear magnetic resonance




Ordinary differential equations


Pentose phosphate pathways








Sedoheptulose-7- phosphate




Selected ion monitoring


Stable isotope resolved metabolomics




Ultrahigh resolution Fourier transformed mass spectrometry



This work was supported by the European Commission (PhenoMeNal EC-654241), MINECO-European Commission FEDER funds—“Una manera de hacer Europa” (SAF2017-89673-R and SAF2015-70270-REDT), Instituto de Salud Carlos III and Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD, CB17/04/00023 and INB-Bioinformatics Platform, of the ISCIII, PT17/0009/0018), Agència de Gestió d’Ajuts Universitaris i de Recerca—Generalitat de Catalunya (2017SGR-1033), and Ministerio de Educación y Formación Profesional (FPU14-05992); and by the Redox Metabolism Shared Resource of the University of Kentucky Markey Cancer Center (P30CA177558). M.C. also acknowledges the prize “ICREA Academia” for the excellence in research, funded by ICREA foundation—Generalitat de Catalunya.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Vitaly A. Selivanov
    • 1
    • 2
    • 3
    • 4
    Email author
  • Silvia Marin
    • 1
    • 2
    • 3
  • Josep Tarragó-Celada
    • 1
    • 2
  • Andrew N. Lane
    • 5
    • 6
    • 7
  • Richard M. Higashi
    • 5
    • 6
    • 7
  • Teresa W.-M. Fan
    • 5
    • 6
    • 7
  • Pedro de Atauri
    • 1
    • 2
    • 3
    • 4
  • Marta Cascante
    • 1
    • 2
    • 3
    • 4
    Email author
  1. 1.Department of Biochemistry and Molecular Biomedicine, Faculty of BiologyUniversitat de BarcelonaBarcelonaSpain
  2. 2.Institute of Biomedicine of Universitat de Barcelona (IBUB)BarcelonaSpain
  3. 3.Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD)Instituto de Salud Carlos III (ISCIII)MadridSpain
  4. 4.INB-Bioinformatics Platform Metabolomics NodeInstituto de Salud Carlos III (ISCIII)MadridSpain
  5. 5.Markey Cancer CenterUniversity of KentuckyLexingtonUSA
  6. 6.Center for Environment and Systems Biochemistry and the Resource Center for Stable Isotope Resolved MetabolomicsUniversity of KentuckyLexingtonUSA
  7. 7.Department of Toxicology and Cancer BiologyUniversity of KentuckyLexingtonUSA

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