13CFLUX2—high-performance software suite for 13C-metabolic flux analysis

Weitzel, Michael; Nöh, Katharina; Dalman, Tolga; Niedenführ, Sebastian; Stute, Birgit; Wiechert, Wolfgang

doi:10.1093/bioinformatics/bts646

Cited by 186 publications

(162 citation statements)

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“…In each experiment, sugar uptake rates were measured using NMR analysis on growth media, and growth rate was used to calculate biomass effluxes from upper glycolysis and the PPP (39,45). We applied the 13CFLUX2 (http://www.13cflux.net) package (40) to obtain initial flux results as well as to generate cumulative isotopomer networks for further optimization followed by 95% confidence interval estimation as previously described (41). Fluxes were fit to networks, including or lacking one or both of the reactions of the PKP.…”

Section: Methodsmentioning

confidence: 99%

Hierarchy in Pentose Sugar Metabolism in Clostridium acetobutylicum

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Lewis

Park

et al. 2015

Appl Environ Microbiol

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Bacterial metabolism of polysaccharides from plant detritus into acids and solvents is an essential component of the terrestrial carbon cycle. Understanding the underlying metabolic pathways can also contribute to improved production of biofuels. Using a metabolomics approach involving liquid chromatography-mass spectrometry, we investigated the metabolism of mixtures of the cellulosic hexose sugar (glucose) and hemicellulosic pentose sugars (xylose and arabinose) in the anaerobic soil bacterium Clostridium acetobutylicum. Simultaneous feeding of stable isotope-labeled glucose and unlabeled xylose or arabinose revealed that, as expected, glucose was preferentially used as the carbon source. Assimilated pentose sugars accumulated in pentose phosphate pathway (PPP) intermediates with minimal flux into glycolysis. Simultaneous feeding of xylose and arabinose revealed an unexpected hierarchy among the pentose sugars, with arabinose utilized preferentially over xylose. The phosphoketolase pathway (PKP) provides an alternative route of pentose catabolism in C. acetobutylicum that directly converts xylulose-5-phosphate into acetyl-phosphate and glyceraldehyde-3-phosphate, bypassing most of the PPP. When feeding the mixture of pentose sugars, the labeling patterns of lower glycolytic intermediates indicated more flux through the PKP than through the PPP and upper glycolysis, and this was confirmed by quantitative flux modeling. Consistent with direct acetyl-phosphate production from the PKP, growth on the pentose mixture resulted in enhanced acetate excretion. Taken collectively, these findings reveal two hierarchies in clostridial pentose metabolism: xylose is subordinate to arabinose, and the PPP is used less than the PKP.

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Section: Methodsmentioning

confidence: 99%

Hierarchy in Pentose Sugar Metabolism in Clostridium acetobutylicum

Aristilde

Lewis

Park

et al. 2015

Appl Environ Microbiol

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“…Experiments using labeled substrates were done in four replicates to determine metabolic fluxes. Flux analysis was performed using the 13CFLUX2 toolbox (Weitzel et al, 2013) as described in Supplemental Methods S1. The 13 C-based MFA model is defined by 14 free net fluxes as well as 21 biomass effluxes (Supplemental Table S3B), which are derived from the biomass compositions of the different genotypes (Supplemental Table S2) and growth rates (Supplemental Table S3D).…”

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confidence: 99%

Quantitative Multilevel Analysis of Central Metabolism in Developing Oilseeds of Oilseed Rape during in Vitro Culture

et al. 2015

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Seeds provide the basis for many food, feed, and fuel products. Continued increases in seed yield, composition, and quality require an improved understanding of how the developing seed converts carbon and nitrogen supplies into storage. Current knowledge of this process is often based on the premise that transcriptional regulation directly translates via enzyme concentration into flux. In an attempt to highlight metabolic control, we explore genotypic differences in carbon partitioning for in vitro cultured developing embryos of oilseed rape (Brassica napus). We determined biomass composition as well as 79 net fluxes, the levels of 77 metabolites, and 26 enzyme activities with specific focus on central metabolism in nine selected germplasm accessions. Overall, we observed a tradeoff between the biomass component fractions of lipid and starch. With increasing lipid content over the spectrum of genotypes, plastidic fatty acid synthesis and glycolytic flux increased concomitantly, while glycolytic intermediates decreased. The lipid/starch tradeoff was not reflected at the proteome level, pointing to the significance of (posttranslational) metabolic control. Enzyme activity/flux and metabolite/flux correlations suggest that plastidic pyruvate kinase exerts flux control and that the lipid/starch tradeoff is most likely mediated by allosteric feedback regulation of phosphofructokinase and ADP-glucose pyrophosphorylase. Quantitative data were also used to calculate in vivo mass action ratios, reaction equilibria, and metabolite turnover times. Compounds like cyclic 39,59-AMP and sucrose-6-phosphate were identified to potentially be involved in so far unknown mechanisms of metabolic control. This study provides a rich source of quantitative data for those studying central metabolism.Seeds develop by absorbing nutrients from their mother plant and using these to synthesize a combination of starch, protein, and lipid. The size and number of seeds that finally develop determine the crop's yield, while their composition determines the end-use quality of the crop. The conversion of nutrients into storage products involves a complex network of metabolic reactions, many of which are subject to transcriptional, translational, and posttranslational regulation. Attempting to engineer seed composition clearly requires a firm understanding of these regulatory networks.The seed's central metabolism differs markedly from those of both a photosynthesizing leaf and a root. In most species, the immature seed is green for a period during its development, so during this phase it is regarded as being photoheterotrophic. A further level of complexity arises as a result of spatial heterogeneity within the seed (Rolletschek et al., 2011; Borisjuk et al.,

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“…In contrast to metabolite balances, the labeling balances lead to non-linear differential equation systems that require advanced computation, even at metabolic steady-state. 58,59 The parameter identification, and especially obtaining the global optimum are challenging already for medium sized networks (order of 100 metabolites). In general dynamic 13 C metabolic flux analysis is experimentally and computationally more intensive than MetDFBA.…”

Section: Discussionmentioning

confidence: 99%

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis

et al. 2015

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. (2015). MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis. Molecular BioSystems, 11(1), 137-145. DOI: 10.1039/c4mb00510d General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: http://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters.Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13 C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.

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13CFLUX2—high-performance software suite for 13C-metabolic flux analysis

Cited by 186 publications

References 16 publications

Hierarchy in Pentose Sugar Metabolism in Clostridium acetobutylicum

Hierarchy in Pentose Sugar Metabolism in Clostridium acetobutylicum

Quantitative Multilevel Analysis of Central Metabolism in Developing Oilseeds of Oilseed Rape during in Vitro Culture

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis

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