scholarly journals Metabolic flux determination by stationary 13-C tracer experiments: Analysis of sensitivity, identifiability and redundancy

1996 ◽  
pp. 128-135 ◽  
Author(s):  
Wolfgang Wiechert
Keyword(s):  
Metabolic Flux ◽  
Tracer Experiments ◽  
Flux Determination

scholarly journals Comprehensive metabolic modeling of multiple 13C-isotopomer data sets to study metabolism in perfused working hearts

2016 ◽  
Vol 311 (4) ◽  
pp. H881-H891 ◽  
Author(s):  
Scott B. Crown ◽  
Joanne K. Kelleher ◽  
Rosanne Rouf ◽  
Deborah M. Muoio ◽  
Maciek R. Antoniewicz
Keyword(s):  
Network Model ◽  
Metabolic Flux ◽  
Parameter Determination ◽  
Algebraic Equations ◽  
Acetyl Coa ◽  
Energy Substrate ◽  
Substrate Metabolism ◽  
Metabolic Fluxes ◽  
Heart Perfusion ◽  
Tracer Experiments

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.

Metabolic flux distribution analysis by 13C-tracer experiments using the Markov chain–Monte Carlo method

2005 ◽  
Vol 33 (6) ◽  
pp. 1421-1422 ◽  
Author(s):  
J. Yang ◽  
S. Wongsa ◽  
V. Kadirkamanathan ◽  
S.A. Billings ◽  
P.C. Wright
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Monte Carlo Method ◽  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
13C Tracer ◽  
Tracer Experiments

Metabolic flux analysis using 13C-tracer experiments is an important tool in metabolic engineering since intracellular fluxes are non-measurable quantities in vivo. Current metabolic flux analysis approaches are fully based on stoichiometric constraints and carbon atom balances, where the over-determined system is iteratively solved by a parameter estimation approach. However, the unavoidable measurement noises involved in the fractional enrichment data obtained by 13C-enrichment experiment and the possible existence of unknown pathways prevent a simple parameter estimation method for intracellular flux quantification. The MCMC (Markov chain–Monte Carlo) method, which obtains intracellular flux distributions through delicately constructed Markov chains, is shown to be an effective approach for deep understanding of the intracellular metabolic network. Its application is illustrated through the simulation of an example metabolic network.

13C tracer experiments and metabolite balancing for metabolic flux analysis: Comparing two approaches

1998 ◽  
Vol 58 (2-3) ◽  
pp. 254-257 ◽  
Author(s):  
Karsten Schmidt ◽  
Achim Marx ◽  
Albert A. de Graaf ◽  
Wolfgang Wiechert ◽  
Hermann Sahm ◽  
...  
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Flux Analysis ◽  
13C Tracer ◽  
Tracer Experiments

scholarly journals An analytic and systematic framework for estimating metabolic flux ratios from 13C tracer experiments

2008 ◽  
Vol 9 (1) ◽  
Author(s):  
Ari Rantanen ◽  
Juho Rousu ◽  
Paula Jouhten ◽  
Nicola Zamboni ◽  
Hannu Maaheimo ◽  
...  
Keyword(s):  
Metabolic Flux ◽  
Systematic Framework ◽  
13C Tracer ◽  
Tracer Experiments

Metabolic flux determination using carbon 13 NMR. Application to normal and tumoral cells from central nervous system

1995 ◽  
Vol 92 ◽  
pp. 1783-1792 ◽  
Author(s):  
M Merle ◽  
M Martin ◽  
JC Portais ◽  
R Schuster ◽  
P Canioni
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Metabolic Flux ◽  
Tumoral Cells ◽  
Flux Determination

scholarly journals Metabolic Flux Analysis—Linking Isotope Labeling and Metabolic Fluxes

Metabolites ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 447
Author(s):  
Yujue Wang ◽  
Fredric E. Wondisford ◽  
Chi Song ◽  
Teng Zhang ◽  
Xiaoyang Su
Keyword(s):  
Metabolic Flux Analysis ◽  
Metabolic Flux ◽  
Isotope Labeling ◽  
Weighted Average ◽  
Fundamental Principle ◽  
Flux Analysis ◽  
Intracellular Metabolites ◽  
Tracer Selection ◽  
Labeling Pattern ◽  
Tracer Experiments

Metabolic flux analysis (MFA) is an increasingly important tool to study metabolism quantitatively. Unlike the concentrations of metabolites, the fluxes, which are the rates at which intracellular metabolites interconvert, are not directly measurable. MFA uses stable isotope labeled tracers to reveal information related to the fluxes. The conceptual idea of MFA is that in tracer experiments the isotope labeling patterns of intracellular metabolites are determined by the fluxes, therefore by measuring the labeling patterns we can infer the fluxes in the network. In this review, we will discuss the basic concept of MFA using a simplified upper glycolysis network as an example. We will show how the fluxes are reflected in the isotope labeling patterns. The central idea we wish to deliver is that under metabolic and isotopic steady-state the labeling pattern of a metabolite is the flux-weighted average of the substrates’ labeling patterns. As a result, MFA can tell the relative contributions of converging metabolic pathways only when these pathways make substrates in different labeling patterns for the shared product. This is the fundamental principle guiding the design of isotope labeling experiment for MFA including tracer selection. In addition, we will also discuss the basic biochemical assumptions of MFA, and we will show the flux-solving procedure and result evaluation. Finally, we will highlight the link between isotopically stationary and nonstationary flux analysis.

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