tailieunhanh - Báo cáo y học: " Distinguishing enzymes using metabolome data for the hybrid dynamic/static method"

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: " Distinguishing enzymes using metabolome data for the hybrid dynamic/static method | Theoretical Biology and Medical Modelling BioMed Central Research Open Access Distinguishing enzymes using metabolome data for the hybrid dynamic static method Nobuyoshi Ishii1 Yoichi Nakayama 1 2 and Masaru Tomita1 Address 1Institute for Advanced Biosciences Keio University Tsuruoka 997-0035 Japan and 2Network Biology Research Centre Articell Systems Corporation Keio Fujisawa Innovation Village 4489 Endo Fujisawa 252-0816 Japan Email Nobuyoshi Ishii - nishii@ Yoichi Nakayama - ynakayam@ Masaru Tomita - mt@ Corresponding author Published 20 May 2007 Received I December 2006 Theoretical Biology and Medical Modelling 2007 4 19 doi 1742-4682-4-19 Accepted 20 May 2007 This article is available from http content 4 1 19 2007 Ishii et al 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 In the process of constructing a dynamic model of a metabolic pathway a large number of parameters such as kinetic constants and initial metabolite concentrations are required. However in many cases experimental determination of these parameters is time-consuming. Therefore for large-scale modelling it is essential to develop a method that requires few experimental parameters. The hybrid dynamic static HDS method is a combination of the conventional kinetic representation and metabolic flux analysis MFA . Since no kinetic information is required in the static module which consists of MFA the HDS method may dramatically reduce the number of required parameters. However no adequate method for developing a hybrid model from experimental data has been proposed. Results In this study we develop a method for constructing hybrid models based on metabolome data. The method