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

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

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.

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