Software supporting a workflow of quantitative dynamic flux maps estimation in central metabolism from SIRM experimental data

Vitaly A. Selivanov; Silvia Marin; Josep Tarragó-Celada; Andrew N. Lane; Richard M. Higashi; Teresa W.M. Fan; Pedro de Atauri; Marta Cascante

doi:10.1007/978-1-0716-0159-4_12

Software supporting a workflow of quantitative dynamic flux maps estimation in central metabolism from SIRM experimental data

Vitaly A. Selivanov, Silvia Marin, Josep Tarragó-Celada, Andrew N. Lane, Richard M. Higashi, Teresa W.M. Fan, Pedro de Atauri, Marta Cascante

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

4 Scopus citations

Abstract

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.

Original language	English
Title of host publication	Methods in Molecular Biology
Pages	271-298
Number of pages	28
DOIs	https://doi.org/10.1007/978-1-0716-0159-4_12
State	Published - 2020

Publication series

Name	Methods in Molecular Biology
Volume	2088
ISSN (Print)	1064-3745
ISSN (Electronic)	1940-6029

Bibliographical note

Publisher Copyright:
© Springer Science+Business Media, LLC, part of Springer Nature 2020.

Funding

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-Bioinfor-matics 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 Pro-fesional (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. This work was supported by the European Commission (PhenoM-eNal 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-Bioinfor-matics 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 Aca-demia” for the excellence in research, funded by ICREA founda-tion—Generalitat de Catalunya.

Funders	Funder number
ICREA Foundation-Generalitat de Catalunya
MINECO-European
Ministerio de Educación y Formación Pro-fesional	FPU14-05992
University of Kentucky Markey Cancer Center	P30CA177558
European Commission	EC-654241
ICREA Foundation-Generalitat de Catalunya	2017SGR-1033
Federación Española de Enfermedades Raras	SAF2017-89673-R, SAF2015-70270-REDT
Agència de Gestió d'Ajuts Universitaris i de Recerca
Institució Catalana de Recerca i Estudis Avançats
Instituto de Salud Carlos III	PT17/0009/0018
Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas	CB17/04/00023
Ministerio de Educación y Formación Profesional

Keywords

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

ASJC Scopus subject areas

Molecular Biology
Genetics

Access to Document

10.1007/978-1-0716-0159-4_12

Cite this

Selivanov, V. A., Marin, S., Tarragó-Celada, J., Lane, A. N., Higashi, R. M., Fan, T. W. M., de Atauri, P., & Cascante, M. (2020). Software supporting a workflow of quantitative dynamic flux maps estimation in central metabolism from SIRM experimental data. In Methods in Molecular Biology (pp. 271-298). (Methods in Molecular Biology; Vol. 2088). https://doi.org/10.1007/978-1-0716-0159-4_12

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abstract = "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.",

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year = "2020",

doi = "10.1007/978-1-0716-0159-4_12",

language = "English",

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Software supporting a workflow of quantitative dynamic flux maps estimation in central metabolism from SIRM experimental data. / Selivanov, Vitaly A.; Marin, Silvia; Tarragó-Celada, Josep et al.
Methods in Molecular Biology. 2020. p. 271-298 (Methods in Molecular Biology; Vol. 2088).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AU - Marin, Silvia

AU - Tarragó-Celada, Josep

AU - Lane, Andrew N.

AU - Higashi, Richard M.

AU - Fan, Teresa W.M.

AU - de Atauri, Pedro

AU - Cascante, Marta

N1 - Publisher Copyright: © Springer Science+Business Media, LLC, part of Springer Nature 2020.

PY - 2020

Y1 - 2020

N2 - 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.

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KW - Computational analysis

KW - Isotopolog distribution

KW - Kinetic models of metabolism

KW - Mass spectrometry

KW - Metabolic fluxes

KW - Stable isotope tracing

KW - Stable isotope-resolved metabolomics

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Software supporting a workflow of quantitative dynamic flux maps estimation in central metabolism from SIRM experimental data

Abstract

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Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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