Academic literature on the topic '13C-MFA'
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Journal articles on the topic "13C-MFA"
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Kajihata, Shuichi, Chikara Furusawa, Fumio Matsuda, and Hiroshi Shimizu. "OpenMebius: An Open Source Software for Isotopically Nonstationary13C-Based Metabolic Flux Analysis." BioMed Research International 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/627014.
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Thein vivomeasurement of metabolic flux by13C-based metabolic flux analysis (13C-MFA) provides valuable information regarding cell physiology. Bioinformatics tools have been developed to estimate metabolic flux distributions from the results of tracer isotopic labeling experiments using a13C-labeled carbon source. Metabolic flux is determined by nonlinear fitting of a metabolic model to the isotopic labeling enrichment of intracellular metabolites measured by mass spectrometry. Whereas13C-MFA is conventionally performed under isotopically constant conditions, isotopically nonstationary13C metabolic flux analysis (INST-13C-MFA) has recently been developed for flux analysis of cells with photosynthetic activity and cells at a quasi-steady metabolic state (e.g., primary cells or microorganisms under stationary phase). Here, the development of a novel open source software for INST-13C-MFA on the Windows platform is reported. OpenMebius (Open source software for Metabolic flux analysis) provides the function of autogenerating metabolic models for simulating isotopic labeling enrichment from a user-defined configuration worksheet. Analysis using simulated data demonstrated the applicability of OpenMebius for INST-13C-MFA. Confidence intervals determined by INST-13C-MFA were less than those determined by conventional methods, indicating the potential of INST-13C-MFA for precise metabolic flux analysis. OpenMebius is the open source software for the general application of INST-13C-MFA.
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Desai, Trunil S., and Shireesh Srivastava. "FluxPyt: a Python-based free and open-source software for 13C-metabolic flux analyses." PeerJ 6 (April 27, 2018): e4716. http://dx.doi.org/10.7717/peerj.4716.
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13C-Metabolic flux analysis (MFA) is a powerful approach to estimate intracellular reaction rates which could be used in strain analysis and design. Processing and analysis of labeling data for calculation of fluxes and associated statistics is an essential part of MFA. However, various software currently available for data analysis employ proprietary platforms and thus limit accessibility. We developed FluxPyt, a Python-based truly open-source software package for conducting stationary 13C-MFA data analysis. The software is based on the efficient elementary metabolite unit framework. The standard deviations in the calculated fluxes are estimated using the Monte-Carlo analysis. FluxPyt also automatically creates flux maps based on a template for visualization of the MFA results. The flux distributions calculated by FluxPyt for two separate models: a small tricarboxylic acid cycle model and a larger Corynebacterium glutamicum model, were found to be in good agreement with those calculated by a previously published software. FluxPyt was tested in Microsoft™ Windows 7 and 10, as well as in Linux Mint 18.2. The availability of a free and open 13C-MFA software that works in various operating systems will enable more researchers to perform 13C-MFA and to further modify and develop the package.
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Moiz, Bilal, Jonathan Garcia, Sarah Basehore, et al. "13C Metabolic Flux Analysis Indicates Endothelial Cells Attenuate Metabolic Perturbations by Modulating TCA Activity." Metabolites 11, no. 4 (2021): 226. http://dx.doi.org/10.3390/metabo11040226.
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Disrupted endothelial metabolism is linked to endothelial dysfunction and cardiovascular disease. Targeted metabolic inhibitors are potential therapeutics; however, their systemic impact on endothelial metabolism remains unknown. In this study, we combined stable isotope labeling with 13C metabolic flux analysis (13C MFA) to determine how targeted inhibition of the polyol (fidarestat), pentose phosphate (DHEA), and hexosamine biosynthetic (azaserine) pathways alters endothelial metabolism. Glucose, glutamine, and a four-carbon input to the malate shuttle were important carbon sources in the baseline human umbilical vein endothelial cell (HUVEC) 13C MFA model. We observed two to three times higher glutamine uptake in fidarestat and azaserine-treated cells. Fidarestat and DHEA-treated HUVEC showed decreased 13C enrichment of glycolytic and TCA metabolites and amino acids. Azaserine-treated HUVEC primarily showed 13C enrichment differences in UDP-GlcNAc. 13C MFA estimated decreased pentose phosphate pathway flux and increased TCA activity with reversed malate shuttle direction in fidarestat and DHEA-treated HUVEC. In contrast, 13C MFA estimated increases in both pentose phosphate pathway and TCA activity in azaserine-treated cells. These data show the potential importance of endothelial malate shuttle activity and suggest that inhibiting glycolytic side branch pathways can change the metabolic network, highlighting the need to study systemic metabolic therapeutic effects.
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Kleijn, Roelco J., Wouter A. van Winden, Cor Ras, Walter M. van Gulik, Dick Schipper, and Joseph J. Heijnen. "13C-Labeled Gluconate Tracing as a Direct and Accurate Method for Determining the Pentose Phosphate Pathway Split Ratio in Penicillium chrysogenum." Applied and Environmental Microbiology 72, no. 7 (2006): 4743–54. http://dx.doi.org/10.1128/aem.02955-05.
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ABSTRACT In this study we developed a new method for accurately determining the pentose phosphate pathway (PPP) split ratio, an important metabolic parameter in the primary metabolism of a cell. This method is based on simultaneous feeding of unlabeled glucose and trace amounts of [U-13C]gluconate, followed by measurement of the mass isotopomers of the intracellular metabolites surrounding the 6-phosphogluconate node. The gluconate tracer method was used with a penicillin G-producing chemostat culture of the filamentous fungus Penicillium chrysogenum. For comparison, a 13C-labeling-based metabolic flux analysis (MFA) was performed for glycolysis and the PPP of P. chrysogenum. For the first time mass isotopomer measurements of 13C-labeled primary metabolites are reported for P. chrysogenum and used for a 13C-based MFA. Estimation of the PPP split ratio of P. chrysogenum at a growth rate of 0.02 h−1 yielded comparable values for the gluconate tracer method and the 13C-based MFA method, 51.8% and 51.1%, respectively. A sensitivity analysis of the estimated PPP split ratios showed that the 95% confidence interval was almost threefold smaller for the gluconate tracer method than for the 13C-based MFA method (40.0 to 63.5% and 46.0 to 56.5%, respectively). From these results we concluded that the gluconate tracer method permits accurate determination of the PPP split ratio but provides no information about the remaining cellular metabolism, while the 13C-based MFA method permits estimation of multiple fluxes but provides a less accurate estimate of the PPP split ratio.
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Crown, Scott B., Joanne K. Kelleher, Rosanne Rouf, Deborah M. Muoio, and Maciek R. Antoniewicz. "Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts." American Journal of Physiology-Heart and Circulatory Physiology 311, no. 4 (2016): H881—H891. http://dx.doi.org/10.1152/ajpheart.00428.2016.
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In many forms of cardiomyopathy, alterations in energy substrate metabolism play a key role in disease pathogenesis. Stable isotope tracing in rodent heart perfusion systems can be used to determine cardiac metabolic fluxes, namely those relative fluxes that contribute to pyruvate, the acetyl-CoA pool, and pyruvate anaplerosis, which are critical to cardiac homeostasis. Methods have previously been developed to interrogate these relative fluxes using isotopomer enrichments of measured metabolites and algebraic equations to determine a predefined metabolic flux model. However, this approach is exquisitely sensitive to measurement error, thus precluding accurate relative flux parameter determination. In this study, we applied a novel mathematical approach to determine relative cardiac metabolic fluxes using 13C-metabolic flux analysis (13C-MFA) aided by multiple tracer experiments and integrated data analysis. Using 13C-MFA, we validated a metabolic network model to explain myocardial energy substrate metabolism. Four different 13C-labeled substrates were queried (i.e., glucose, lactate, pyruvate, and oleate) based on a previously published study. We integrated the analysis of the complete set of isotopomer data gathered from these mouse heart perfusion experiments into a single comprehensive network model that delineates substrate contributions to both pyruvate and acetyl-CoA pools at a greater resolution than that offered by traditional methods using algebraic equations. To our knowledge, this is the first rigorous application of 13C-MFA to interrogate data from multiple tracer experiments in the perfused heart. We anticipate that this approach can be used widely to study energy substrate metabolism in this and other similar biological systems.
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Shupletsov, M. S., L. I. Golubeva, and S. V. Mashko. "Metabolic Flux Analysis using 13C isotopes. II. Mathematical Basis for the Method." Biotekhnologiya 32, no. 6 (2016): 9–34. http://dx.doi.org/10.21519/0234-2758-2016-32-6-9-34.
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Tomi-Andrino, Claudio, Rupert Norman, Thomas Millat, et al. "Physicochemical and metabolic constraints for thermodynamics-based stoichiometric modelling under mesophilic growth conditions." PLOS Computational Biology 17, no. 1 (2021): e1007694. http://dx.doi.org/10.1371/journal.pcbi.1007694.
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Metabolic engineering in the post-genomic era is characterised by the development of new methods for metabolomics and fluxomics, supported by the integration of genetic engineering tools and mathematical modelling. Particularly, constraint-based stoichiometric models have been widely studied: (i) flux balance analysis (FBA) (in silico), and (ii) metabolic flux analysis (MFA) (in vivo). Recent studies have enabled the incorporation of thermodynamics and metabolomics data to improve the predictive capabilities of these approaches. However, an in-depth comparison and evaluation of these methods is lacking. This study presents a thorough analysis of two different in silico methods tested against experimental data (metabolomics and 13C-MFA) for the mesophile Escherichia coli. In particular, a modified version of the recently published matTFA toolbox was created, providing a broader range of physicochemical parameters. Validating against experimental data allowed the determination of the best physicochemical parameters to perform the TFA (Thermodynamics-based Flux Analysis). An analysis of flux pattern changes in the central carbon metabolism between 13C-MFA and TFA highlighted the limited capabilities of both approaches for elucidating the anaplerotic fluxes. In addition, a method based on centrality measures was suggested to identify important metabolites that (if quantified) would allow to further constrain the TFA. Finally, this study emphasised the need for standardisation in the fluxomics community: novel approaches are frequently released but a thorough comparison with currently accepted methods is not always performed.
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Noack, Stephan, Katharina Nöh, Matthias Moch, Marco Oldiges, and Wolfgang Wiechert. "Stationary versus non-stationary 13C-MFA: A comparison using a consistent dataset." Journal of Biotechnology 154, no. 2-3 (2011): 179–90. http://dx.doi.org/10.1016/j.jbiotec.2010.07.008.
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Hanke, Tanja, Katharina Nöh, Stephan Noack, et al. "Combined Fluxomics and Transcriptomics Analysis of Glucose Catabolism via a Partially Cyclic Pentose Phosphate Pathway in Gluconobacter oxydans 621H." Applied and Environmental Microbiology 79, no. 7 (2013): 2336–48. http://dx.doi.org/10.1128/aem.03414-12.
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ABSTRACTIn this study, the distribution and regulation of periplasmic and cytoplasmic carbon fluxes inGluconobacter oxydans621H with glucose were studied by13C-based metabolic flux analysis (13C-MFA) in combination with transcriptomics and enzyme assays. For13C-MFA, cells were cultivated with specifically13C-labeled glucose, and intracellular metabolites were analyzed for their labeling pattern by liquid chromatography-mass spectrometry (LC-MS). In growth phase I, 90% of the glucose was oxidized periplasmically to gluconate and partially further oxidized to 2-ketogluconate. Of the glucose taken up by the cells, 9% was phosphorylated to glucose 6-phosphate, whereas 91% was oxidized by cytoplasmic glucose dehydrogenase to gluconate. Additional gluconate was taken up into the cells by transport. Of the cytoplasmic gluconate, 70% was oxidized to 5-ketogluconate and 30% was phosphorylated to 6-phosphogluconate. In growth phase II, 87% of gluconate was oxidized to 2-ketogluconate in the periplasm and 13% was taken up by the cells and almost completely converted to 6-phosphogluconate. SinceG. oxydanslacks phosphofructokinase, glucose 6-phosphate can be metabolized only via the oxidative pentose phosphate pathway (PPP) or the Entner-Doudoroff pathway (EDP).13C-MFA showed that 6-phosphogluconate is catabolized primarily via the oxidative PPP in both phases I and II (62% and 93%) and demonstrated a cyclic carbon flux through the oxidative PPP. The transcriptome comparison revealed an increased expression of PPP genes in growth phase II, which was supported by enzyme activity measurements and correlated with the increased PPP flux in phase II. Moreover, genes possibly related to a general stress response displayed increased expression in growth phase II.
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Theorell, Axel, and Katharina Nöh. "Reversible jump MCMC for multi-model inference in Metabolic Flux Analysis." Bioinformatics 36, no. 1 (2019): 232–40. http://dx.doi.org/10.1093/bioinformatics/btz500.
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Abstract Motivation The validity of model based inference, as used in systems biology, depends on the underlying model formulation. Often, a vast number of competing models is available, that are built on different assumptions, all consistent with the existing knowledge about the studied biological phenomenon. As a remedy for this, Bayesian Model Averaging (BMA) facilitates parameter and structural inferences based on multiple models simultaneously. However, in fields where a vast number of alternative, high-dimensional and non-linear models are involved, the BMA-based inference task is computationally very challenging. Results Here we use BMA in the complex setting of Metabolic Flux Analysis (MFA) to infer whether potentially reversible reactions proceed uni- or bidirectionally, using 13C labeling data and metabolic networks. BMA is applied on a large set of candidate models with differing directionality settings, using a tailored multi-model Markov Chain Monte Carlo (MCMC) approach. The applicability of our algorithm is shown by inferring the in vivo probability of reaction bidirectionalities in a realistic network setup, thereby extending the scope of 13C MFA from parameter to structural inference. Supplementary information Supplementary data are available at Bioinformatics online.
Dissertations / Theses on the topic "13C-MFA"
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Sundqvist, Nicolas. "Can you trust your model? A showcase study of validation in 13C metabolic flux analysis." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156328.
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Cellular metabolism is one of the most fundamental systems for any living organisms, involving thousands of metabolites and reactions that forms large interconnected metabolic networks. Proper and comprehensive understanding of the metabolism in human cells has been a field of research for a long time. One of the key parameters in understanding the metabolism are the metabolic fluxes, which are the rates of conversion of metabolic intermediates. Currently, one of the main approaches for determining these fluxes is metabolic flux analysis (MFA), in which isotope-labelled compounds are introduced into the system and measured. Mathematical models are then used to calculate a prediction of the systems flux configuration. However, the current paradigm of MFA lack established methods for validating that a model can accurately predict quantities for which there are no experimental data. In this study, a model for the central human metabolism was created and evaluated with regards to the model’s ability to predict a validation dataset. Further, an uncertainty analysis of these predictions were performed with a prediction profile likelihood analysis. This study has conclusively shown that MFA models can be validated against experimental data that the model has never seen before. Additionally, such model predictions were shown to be observable with a well determined prediction uncertainty. These results shows that a systematic validation of MFA models is possible. This in turn allows for a greater trust to be placed in the models, and in any conclusions that are based on such models.
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Bland, Katherine Elizabeth. "Lignocellulosic fermentation of Saccharomyces cerevisiae to produce medium chain fatty alcohols." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/82720.
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The effects of climate change have made the need to develop sustainable production practices for biofuels and other chemicals imminent. The development of the green economy has also led to many industries voluntarily improving the sustainability of the products they produce. The microbial production of fatty acid-derived chemicals allows for the opportunity to reduce petroleum-based chemicals in the marketplace. However, for microbial produced chemicals to be industrially competitive, significant work is needed to improve the production capacity of industrial strains. There are a number of bottlenecks and challenges related to the production of various fatty acid derivatives that need to be addressed.
One of these key challenges relates to the source of the fermentation feedstock. While sources such as corn or sugar cane are currently common, these feedstocks compete with food supply and require nutrient-rich soils. The use of lignocellulosic feedstocks is preferred to combat this issue, however these feedstocks present their own unique challenges. Pretreatment is required to release fermentable sugars, and this process also results in various fermentation inhibitors released into the solution. A better understanding of how engineered strains utilize these fermentable sugars as well as improving resistance to the inhibitors will help to improve the chemical production capacity of these chemical products. This work will focus on describing key bottlenecks related to fatty acid-derived products, while also evaluating proposed solutions to these bottlenecks.<br>Master of Science
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Fatarova, Maria. "The metabolic consequences of gene knockout to pathway flux in trypanosomes." Thesis, Toulouse, INSA, 2017. http://www.theses.fr/2017ISAT0025/document.
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Le contexte de ce projet de thèse était d’approfondir la compréhension du métabolisme de Trypanosoma brucei. Les trypanosomes utilisent différents types de sources de carbone, des hydrates de carbone ainsi que des acides aminés pour alimenter leurs besoins énergétiques et biosynthétiques (conditions imitant réellement l'environnement dans la mouche tse-tse). Les différences de thioesters d'acyl-CoA sont encore inconnues dans ces conditions. Une telle élucidation est essentielle pour comprendre les adaptations métaboliques de l'organisme au cours de son cycle de vie. Cet objectif pourrait être complété par une combinaison d'analyses sensibles de divers groupes de métabolites, de délétions dirigées de gènes ou de régulations négatives. Ces derniers développements intègrent un flux de travail complet d'analyse des flux métaboliques par 13C à l’état-instationnaire. Ce flux de travail combine les méthodes existantes pour la collecte d'échantillons, la métabolomique quantitative basée sur MS et l'analyse isotopique d'acides organiques, d'acides aminés, de composés phosphorylés en plus des thioesters d'acyl Coenzyme A (acyl-CoAs), qui représentent un point central entre le métabolisme central du carbone et les voies anaboliques. Ce flux de travail a d'abord été évalué et validé sur l'organisme modèle Escherichia coli et a fourni de nouvelles idées sur son fonctionnement métabolique. Par la suite, ce flux de travail a ensuite été exploité pour étudier le métabolisme de T. brucei, pour lequel les résultats préliminaires sont décrits et discutés dans cette thèse<br>Unusual metabolism of protozoan parasite causing deadly sleeping sickness, Trypanosoma brucei, has been enigmatic for many years. In the past decades, targeted genetic perturbations combined with metabolic analysis have advanced the view on complex compartmentalized metabolism of this organism, but acyl-CoA metabolism on the crossroad between catabolic and anabolic pathways, remains largely uncharacterized. Present work aims at clarifying mitochondrial operation and topology of acyl-CoA network of T. brucei, as well as its interconnections with the rest of metabolism. This has required the development of a complete framework for investigation of acyl-CoA metabolism in T. brucei integrating isotope labeling experiments with metabolite quantification. Sensitive LC-MS method for identification and quantification of acyl-CoAs based on high-resolution mass spectrometry (HRMS) with LTQ-OrbiTrap has been established and applied to investigate acyl-CoA metabolism in the protozoan parasite, as well as in the model organism in systems and synthetic biology, Escherichia coli. Complete workflow from cell cultivation, measurement of extracellular fluxes and analysis of isotopic profile which is result of enzyme-specific incorporation of isotopic tracer allowed modelling of metabolic network and calculation of metabolic fluxes. The entire workflow has been biologically validated and has clarified the link between acyl-CoA and central carbon metabolism in E. coli. The proposed framework has been adapted to T. brucei, for which several sample collection methods have been evaluated thoroughly. It was possible to extract, identify and quantify main acyl-CoA species produced from glucose catabolism. This optimised setup for acyl-CoA analysis will allow collection of data for NMR-based analysis of metabolic end products as well as collection of intracellular metabolites from same sample
Book chapters on the topic "13C-MFA"
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Ebert, Birgitta E., and Lars M. Blank. "Successful Downsizing for High-Throughput 13C-MFA Applications." In Methods in Molecular Biology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1170-7_8.
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Jouhten, Paula, and Hannu Maaheimo. "Labelling Analysis for 13C MFA Using NMR Spectroscopy." In Methods in Molecular Biology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1170-7_9.
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Quek, Lake-Ee, and Lars K. Nielsen. "Customization of 13C-MFA Strategy According to Cell Culture System." In Methods in Molecular Biology. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1170-7_5.
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