Bayesian stationary state flux balance analysis for a skeletal muscle metabolic model
2007
Cellular metabolism can be modelled as a multi-compartment
dynamical system, the compartments representing the circulatory
system consisting of blood and interstitial fluid, and different
subcellular structures. The inverse problem in cellular metabolism
is to obtain information about the state of the system based on
few measured concentrations of metabolites or intermediates either
in the blood or in the tissue. In this article, we first discuss a
new three compartment metabolic model for human skeletal muscle
metabolism and the corresponding inverse problem of determining
the metabolic reaction and transport rates given blood
concentration data under sustained exercise. We introduce the
concept of a metabolic stationary state, describe a Bayesian
methodology to analyze it and apply it to study the stationary
state of human leg skeletal muscles under exercise. Our analysis
demonstrates that the system is fairly well identified if the
concentrations of certain species in the blood are known, and that
the lack of oxygen concentration data can be replaced by
prescribing either the ATP hydrolysis level or the glycogen
depletion rate.
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