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

      Bioinformatics

      Bacterial Proteins, metabolism, Computer Simulation, Corynebacterium glutamicum, Energy Metabolism, physiology, Gene Expression Profiling, methods, Markov Chains, Metabolic Clearance Rate, Models, Biological, Models, Statistical, Signal Transduction, Algorithms

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          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.

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          Journal
          16940326
          10.1093/bioinformatics/btl445

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