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Bioinformatics Advance Access originally published online on August 29, 2006
Bioinformatics 2006 22(21):2681-2687; doi:10.1093/bioinformatics/btl445
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Markov Chain Monte Carlo Algorithm based metabolic flux distribution analysis on Corynebacterium glutamicum

Visakan Kadirkamanathan 1,*, Jing Yang 1, Stephen A. Billings 1 and Phillip C. Wright 2

1 Signal Processing and Complex Systems Research Group, Department of Automatic Control and Systems Engineering, University of Sheffield Sheffield, S1 3JD, UK
2 Biological and Environmental Systems Group, Department of Chemical and Process Engineering, University of Sheffield Sheffield, S1 3JD, UK

*To whom correspondence should be addressed.

Motivation: Metabolic flux analysis via a 13C 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.

Results: 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.

Availability: Algorithms are available upon request.

Contact: visakan{at}sheffield.ac.uk

Received on May 25, 2006; revised on August 11, 2006; accepted on August 15, 2006

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