Key result
A new methodology based on a Bayesian statistical approach to flux balance analysis, utilizing a Markov Chain Monte Carlo scheme, was proposed to estimate fluxes in a skeletal muscle model.
The study proposes a novel Bayesian statistical approach to flux balance analysis for estimating metabolic fluxes in a skeletal muscle model.
May refine skeletal muscle flux estimates; extends FBA methods but leaves clinical adoption open.
The steady state condition of two‐compartment skeletal muscle model is considered. The inverse problem is to estimate the reaction and transport fluxes, as well as the concentrations in venous blood when the stoichiometry and bound constraints for the fluxes and the concentrations are given. A new methodology based on a Bayesian statistical approach to the flux balance analysis (FBA) is proposed. We show how the bound constraints and optimality condition can be incorporated into the model in the Bayesian framework by proper construction of the prior densities. An effective Markov Chain Monte Carlo (MCMC) scheme to explore the posterior densities is proposed. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
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Calvetti et al. (2007) studied Skeletal muscle metabolic model. Bayesian statistical approach to flux balance analysis (FBA) was evaluated on Estimation of reaction and transport fluxes and concentrations in venous blood. A new methodology based on a Bayesian statistical approach to flux balance analysis, utilizing a Markov Chain Monte Carlo scheme, was proposed to estimate fluxes in a skeletal muscle model.
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