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An analytic and systematic framework for estimating metabolic flux ratios from (13)C tracer experiments
BACKGROUND: Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from (13)C isotopomer measurement data rely either on manual derivation of analytic equati...
Autores principales: | , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2008
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2430715/ https://www.ncbi.nlm.nih.gov/pubmed/18534038 http://dx.doi.org/10.1186/1471-2105-9-266 |
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author | Rantanen, Ari Rousu, Juho Jouhten, Paula Zamboni, Nicola Maaheimo, Hannu Ukkonen, Esko |
author_facet | Rantanen, Ari Rousu, Juho Jouhten, Paula Zamboni, Nicola Maaheimo, Hannu Ukkonen, Esko |
author_sort | Rantanen, Ari |
collection | PubMed |
description | BACKGROUND: Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from (13)C isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the (13)C isotopomer data are typically needed. RESULTS: We present a novel analytic framework for estimating metabolic flux ratios in the cell from (13)C isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, (13)C isotopomer measurement techniques, substrates and substrate labelling patterns. By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms Bacillus subtilis and Saccharomyces cerevisiae we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by in silico calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose. CONCLUSION: The core of (13)C metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations. |
format | Text |
id | pubmed-2430715 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-24307152008-06-19 An analytic and systematic framework for estimating metabolic flux ratios from (13)C tracer experiments Rantanen, Ari Rousu, Juho Jouhten, Paula Zamboni, Nicola Maaheimo, Hannu Ukkonen, Esko BMC Bioinformatics Methodology Article BACKGROUND: Metabolic fluxes provide invaluable insight on the integrated response of a cell to environmental stimuli or genetic modifications. Current computational methods for estimating the metabolic fluxes from (13)C isotopomer measurement data rely either on manual derivation of analytic equations constraining the fluxes or on the numerical solution of a highly nonlinear system of isotopomer balance equations. In the first approach, analytic equations have to be tediously derived for each organism, substrate or labelling pattern, while in the second approach, the global nature of an optimum solution is difficult to prove and comprehensive measurements of external fluxes to augment the (13)C isotopomer data are typically needed. RESULTS: We present a novel analytic framework for estimating metabolic flux ratios in the cell from (13)C isotopomer measurement data. In the presented framework, equation systems constraining the fluxes are derived automatically from the model of the metabolism of an organism. The framework is designed to be applicable with all metabolic network topologies, (13)C isotopomer measurement techniques, substrates and substrate labelling patterns. By analyzing nuclear magnetic resonance (NMR) and mass spectrometry (MS) measurement data obtained from the experiments on glucose with the model micro-organisms Bacillus subtilis and Saccharomyces cerevisiae we show that our framework is able to automatically produce the flux ratios discovered so far by the domain experts with tedious manual analysis. Furthermore, we show by in silico calculability analysis that our framework can rapidly produce flux ratio equations – as well as predict when the flux ratios are unobtainable by linear means – also for substrates not related to glucose. CONCLUSION: The core of (13)C metabolic flux analysis framework introduced in this article constitutes of flow and independence analysis of metabolic fragments and techniques for manipulating isotopomer measurements with vector space techniques. These methods facilitate efficient, analytic computation of the ratios between the fluxes of pathways that converge to a common junction metabolite. The framework can been seen as a generalization and formalization of existing tradition for computing metabolic flux ratios where equations constraining flux ratios are manually derived, usually without explicitly showing the formal proofs of the validity of the equations. BioMed Central 2008-06-06 /pmc/articles/PMC2430715/ /pubmed/18534038 http://dx.doi.org/10.1186/1471-2105-9-266 Text en Copyright © 2008 Rantanen et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Rantanen, Ari Rousu, Juho Jouhten, Paula Zamboni, Nicola Maaheimo, Hannu Ukkonen, Esko An analytic and systematic framework for estimating metabolic flux ratios from (13)C tracer experiments |
title | An analytic and systematic framework for estimating metabolic flux ratios from (13)C tracer experiments |
title_full | An analytic and systematic framework for estimating metabolic flux ratios from (13)C tracer experiments |
title_fullStr | An analytic and systematic framework for estimating metabolic flux ratios from (13)C tracer experiments |
title_full_unstemmed | An analytic and systematic framework for estimating metabolic flux ratios from (13)C tracer experiments |
title_short | An analytic and systematic framework for estimating metabolic flux ratios from (13)C tracer experiments |
title_sort | analytic and systematic framework for estimating metabolic flux ratios from (13)c tracer experiments |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2430715/ https://www.ncbi.nlm.nih.gov/pubmed/18534038 http://dx.doi.org/10.1186/1471-2105-9-266 |
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