Paper Info
Title
Markov Chain Monte Carlo Algorithm based metabolic flux distribution analysis on Corynebacterium glutamicum.
Abstract
Metabolic flux analysis via a (13)C tracer experiment has been achieved using a Monte Carlo method with the assumption of system noise as Gaussian noise. However, an unbiased flux analysis requires the estimation of fluxes and metabolites jointly without the restriction on the assumption of Gaussian noise. The flux distributions under such a framework can be freely obtained with various system noise and uncertainty models.In this paper, a stochastic generative model of the metabolic system is developed. Following this, the Markov Chain Monte Carlo (MCMC) approach is applied to flux distribution analysis. The disturbances and uncertainties in the system are simplified as truncated Gaussian multiplicative models. The performance in a real metabolic system is illustrated by the application to the central metabolism of Corynebacterium glutamicum. The flux distributions are illustrated and analyzed in order to understand the underlying flux activities in the system.Algorithms are available upon request.
Year
DOI
Venue
2006
10.1093/bioinformatics/btl445
Bioinformatics
Keywords
Field
DocType
corynebacterium glutamicum,metabolic flux distribution analysis,flux distribution,flux distribution analysis,underlying flux activity,real metabolic system,markov chain monte carlo,metabolic system,metabolic flux analysis,various system noise,system noise,unbiased flux analysis,gaussian noise,monte carlo method
Monte Carlo method in statistical physics,Statistical physics,Data mining,Monte Carlo method,Mathematical optimization,Markov chain Monte Carlo,Computer science,Hybrid Monte Carlo,Kinetic Monte Carlo,Dynamic Monte Carlo method,Monte Carlo molecular modeling,Gaussian noise
Journal
Volume
Issue
ISSN
22
21
1367-4811
Citations 
PageRank 
References 
5
0.50
2
Authors
4
Name
Order
Citations
PageRank
Visakan Kadirkamanathan143162.00
Jing Yang250.50
Steve A Billings343231.41
Phillip C Wright4364.61