ArticleBMC bioinformatics2008
13C labeling experiments at metabolic nonstationary conditions: an exploratory study.
Article in BMC bioinformatics, 2008. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Using Kinetic Modelling to Infer Adaptations inMetabolites · 2023Article
- Review
- The Metabolic Flux Probe (MFP)-Secreted Protein as a Non-Disruptive Information Carrier forInternational journal of molecular sciences · 2021Article
- Metabolic Modelling as a Framework for Metabolomics Data Integration and Analysis.Metabolites · 2020Review
- Metabolic switches from quiescence to growth in synchronized Saccharomyces cerevisiae.Metabolomics : Official journal of the Metabolomic Society · 2019Article
- Metabolic adjustment upon repetitive substrate perturbations using dynamicMicrobial cell factories · 2017Article
- Understanding metabolism with flux analysis: From theory to application.Metabolic engineering · 2017Review
- Interaction of storage carbohydrates and other cyclic fluxes with central metabolism: A quantitative approach by non-stationaryMetabolic engineering communications · 2016Article
- Impact of kinetic isotope effects in isotopic studies of metabolic systems.BMC systems biology · 2015Article
- A roadmap for interpreting (13)C metabolite labeling patterns from cells.Current opinion in biotechnology · 2015Review
- Systems modeling approaches for microbial community studies: from metagenomics to inference of the community structure.Frontiers in microbiology · 2015Article
- Pool size measurements facilitate the determination of fluxes at branching points in non-stationary metabolic flux analysis: the case of Arabidopsis thaliana.Frontiers in plant science · 2015Article
- Fast "Feast/Famine" Cycles for Studying Microbial Physiology Under Dynamic Conditions: A Case Study with Saccharomyces cerevisiae.Metabolites · 2014Article
- Promise and reality in the expanding field of network interaction analysis: metabolic networks.Bioinformatics and biology insights · 2014Article
- Error Analysis and Propagation in Metabolomics Data Analysis.Computational and structural biotechnology journal · 2013Article
- Recent advances in mapping environmental microbial metabolisms through 13C isotopic fingerprints.Journal of the Royal Society, Interface · 2012Review
- Stable isotope-resolved metabolomics and applications for drug development.Pharmacology & therapeutics · 2012Review
- Integrating flux balance analysis into kinetic models to decipher the dynamic metabolism of Shewanella oneidensis MR-1.PLoS computational biology · 2012Article
- Compartmentation of glycogen metabolism revealed from 13C isotopologue distributions.BMC systems biology · 2011Article
- Edelfosine-induced metabolic changes in cancer cells that precede the overproduction of reactive oxygen species and apoptosis.BMC systems biology · 2010Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundStimulus Response Experiments to unravel the regulatory properties of metabolic networks are becoming more and more popular. However, their ability to determine enzyme kinetic parameters has proven to be limited with the presently available data. In metabolic flux analysis, the use of 13C labeled substrates together with isotopomer modeling solved the problem of underdetermined networks and increased the accuracy of flux estimations significantly.
resultsIn this contribution, the idea of increasing the information content of the dynamic experiment by adding 13C labeling is analyzed. For this purpose a small example network is studied by simulation and statistical methods. Different scenarios regarding available measurements are analyzed and compared to a non-labeled reference experiment. Sensitivity analysis revealed a specific influence of the kinetic parameters on the labeling measurements. Statistical methods based on parameter sensitivities and different measurement models are applied to assess the information gain of the labeled stimulus response experiment.
conclusionIt was found that the use of a (specifically) labeled substrate will significantly increase the parameter estimation accuracy. An overall information gain of about a factor of six is observed for the example network. The information gain is achieved from the specific influence of the kinetic parameters towards the labeling measurements. This also leads to a significant decrease in correlation of the kinetic parameters compared to an experiment without 13C-labeled substrate.
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