Reconstruction of linlog kinetics combining constraint-based and bayesian methodologies

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Copyright: Gotama, Michael
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Abstract
The behaviours of metabolic networks consisting of hundreds to thousands of reactions cannot be inferred by intuition alone. Constraint-based methodologies are commonly used to gain insight into these networks as all they require is their stoichiometry matrix. The problem with these types of approaches is that the dynamics of the system cannot be elucidated. Another approach is kinetic modelling that can provide insight into the model’s dynamics but requires a high number of parameters. Linear-logarithmic (linlog) kinetics is a kinetic model that can be built based on stoichiometry data due to the simplicity of its parameters and their correlation with the stoichiometry values. The elasticity for this kinetic model is best predicted from time course or steady state experimental data. We combine the calculation of flux from different steady state conditions using minimization of metabolic adjustment, and use the resulting flux to predict metabolite concentrations using the metabolic tug of war method. Flux and metabolite concentrations are then used to improve the prediction of the elasticities that are required as the parameters for a linlog model. The model has the potential to offer useful predictions of metabolite concentrations in different hypothetical cell conditions. Elasticities calculated from the hypothetical data had predictive capabilities comparable to those calculated using stoichiometry and simulation. Furthermore, by analysing the stability of the reactions and flux control coefficient from the balanced linlog, the rate limiting step could be estimated. The estimation of rate limiting steps using balanced linlog is better than the standard linlog method.
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Author(s)
Gotama, Michael
Supervisor(s)
Gaeta, Bruno
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Publication Year
2017
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Thesis
Degree Type
Masters Thesis
UNSW Faculty
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