Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data.

ABSTRACT:

Background

Translating a known metabolic network into a dynamic model requires reasonable guesses of all enzyme parameters. In Bayesian parameter estimation, model parameters are described by a posterior probability distribution, which scores the potential parameter sets, showing how well each of them agrees with the data and with the prior assumptions made.

Results

We compute posterior distributions of kinetic parameters within a Bayesian framework, based on integration of kinetic, thermodynamic, metabolic, and proteomic data. The structure of the metabolic system (i.e., stoichiometries and enzyme regulation) needs to be known, and the reactions are modelled by convenience kinetics with thermodynamically independent parameters. The parameter posterior is computed in two sep

SUBMITTER: Liebermeister W 

PROVIDER: S-EPMC1781439 | biostudies-literature | 2006 Dec

REPOSITORIES: biostudies-literature