tailieunhanh - Báo cáo y học: " Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học quốc tế cung cấp cho các bạn kiến thức về ngành y đề tài: " Bringing metabolic networks to life: integration of kinetic, metabolic, and proteomic data | Theoretical Biology and Medical Modelling BioMed Central Open Access Bringing metabolic networks to life integration of kinetic metabolic and proteomic data Wolfram Liebermeister and Edda Klipp Address Computational Systems Biology Max Planck Institute for Molecular Genetics Ihnestrafie 63-73 14195 Berlin Germany Email Wolfram Liebermeister - lieberme@ Edda Klipp - klipp@ Corresponding author Published 15 December 2006 Received II September 2006 Theoretical Biology and Medical Modelling 2006 3 42 doi 1742-4682-3-42 Accepted 15 December 2006 This article is available from http content 3 1 42 2006 Liebermeister and Klipp licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 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 . 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 separate steps a first posterior summarises the available data on enzyme kinetic parameters an improved second posterior is obtained by integrating metabolic fluxes concentrations and enzyme concentrations for one or more steady states. The data .