An analytic and systematic framework for estimating metabolic flux ratios from ¹³C tracer experiments
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| 008 | 180924e200806 xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.3929/ethz-a-005752017 |2 doi |
| 024 | 7 | 0 | |a 10.1186/1471-2105-9-266 |2 doi |
| 035 | |a (ETHRESEARCH)oai:www.research-collecti.ethz.ch:20.500.11850/287441 | ||
| 245 | 0 | 3 | |a An analytic and systematic framework for estimating metabolic flux ratios from ¹³C tracer experiments |h [Elektronische Daten] |c [Ari Rantanen, Juho Rousu, Paula Jouhten, Nicola; id_orcid 0000-0003-1271-1021 Zamboni, Hannu Maaheimo, Esko Ukkonen] |
| 506 | |a Open access |2 ethresearch | ||
| 520 | 3 | |a 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 13C 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 13C isotopomer data are typically needed. Results We present a novel analytic framework for estimating metabolic flux ratios in the cell from 13C 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, 13C 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 13C 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. | |
| 540 | |a Creative Commons Attribution 2.0 Generic |u http://creativecommons.org/licenses/by/2.0 |2 ethresearch | ||
| 690 | 7 | |a STOFFWECHSELWEGE UND STOFFWECHSELZYKLEN (BIOCHEMIE) |2 ethresearch | |
| 690 | 7 | |a ZELLMETABOLISMUS + ZELLERNÄHRUNG (CYTOLOGIE) |2 ethresearch | |
| 690 | 7 | |a KOHLENSTOFF-13-ISOTOP |2 ethresearch | |
| 690 | 7 | |a GLUKOSE (BIOCHEMIE) |2 ethresearch | |
| 690 | 7 | |a BACILLUS SUBTILIS (MIKROBIOLOGIE) |2 ethresearch | |
| 690 | 7 | |a METABOLIC PATHWAYS + METABOLIC CYCLES (BIOCHEMISTRY) |2 ethresearch | |
| 690 | 7 | |a CELL NUTRITION + CELL METABOLISM (CYTOLOGY) |2 ethresearch | |
| 690 | 7 | |a CARBON-13 ISOTOPE |2 ethresearch | |
| 690 | 7 | |a GLUCOSE (BIOCHEMISTRY) |2 ethresearch | |
| 690 | 7 | |a BACILLUS SUBTILIS (MICROBIOLOGY) |2 ethresearch | |
| 690 | 7 | |a SACCHAROMYCES (MYKOLOGIE) |2 ethresearch | |
| 690 | 7 | |a SACCHAROMYCES (MYCOLOGY) |2 ethresearch | |
| 690 | 7 | |a Metabolic Network |2 ethresearch | |
| 690 | 7 | |a Metabolic Flux |2 ethresearch | |
| 690 | 7 | |a Flux Distribution |2 ethresearch | |
| 690 | 7 | |a Flux Ratio |2 ethresearch | |
| 690 | 7 | |a Flux Balance Analysis |2 ethresearch | |
| 690 | 7 | |a Life sciences |2 ethresearch | |
| 700 | 1 | |a Rantanen |D Ari |e joint author | |
| 700 | 1 | |a Rousu |D Juho |e joint author | |
| 700 | 1 | |a Jouhten |D Paula |e joint author | |
| 700 | 1 | |a Zamboni |D Nicola; id_orcid 0000-0003-1271-1021 |e joint author | |
| 700 | 1 | |a Maaheimo |D Hannu |e joint author | |
| 700 | 1 | |a Ukkonen |D Esko |e joint author | |
| 773 | 0 | |t BMC Bioinformatics |d London : BioMed Central |g 9, p. 266 |x 1471-2105 | |
| 856 | 4 | 0 | |u http://hdl.handle.net/20.500.11850/287441 |q text/html |z WWW-Backlink auf das Repository (Open access) |
| 908 | |D 1 |a Journal Article |2 ethresearch | ||
| 950 | |B ETHRESEARCH |P 856 |E 40 |u http://hdl.handle.net/20.500.11850/287441 |q text/html |z WWW-Backlink auf das Repository (Open access) | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Rantanen |D Ari |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Rousu |D Juho |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Jouhten |D Paula |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Zamboni |D Nicola; id_orcid 0000-0003-1271-1021 |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Maaheimo |D Hannu |e joint author | ||
| 950 | |B ETHRESEARCH |P 700 |E 1- |a Ukkonen |D Esko |e joint author | ||
| 950 | |B ETHRESEARCH |P 773 |E 0- |t BMC Bioinformatics |d London : BioMed Central |g 9, p. 266 |x 1471-2105 | ||
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 949 | |B ETHRESEARCH |F ETHRESEARCH |b ETHRESEARCH |j Journal Article |c Open access | ||