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Báo cáo y học: "A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles"

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: A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles | Theoretical Biology and Medical Modelling BioMed Central Research Open Access A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles Tomoya Kitayama11 Ayako Kinoshita11 Masahiro Sugimoto1 2 Yoichi Nakayama 1 3 and Masaru Tomita1 Address 1Institute of Advanced Bioscience Keio University Fujisawa 252-8520 Japan 2Department of Bioinformatics Mitsubishi Space Software Co. Ltd. Amagasaki Hyogo 661-0001 Japan and 3Network Biology Research Centre Articell Systems Corporation Keio Fujisawa Innovation Village 4489 Endo Fujisawa 252-0816 Japan Email Tomoya Kitayama - tomoyan@sfc.keio.ac.jp Ayako Kinoshita - ayakosan@sfc.keio.ac.jp Masahiro Sugimoto - msugi@sfc.keio.ac.jp Yoichi Nakayama - ynakayam@sfc.keio.ac.jp Masaru Tomita - mt@sfc.keio.ac.jp Corresponding author fEqual contributors Published 17 July 2006 Received 08 January 2006 Theoretical Biology and Medical Modelling 2006 3 24 doi 10.1186 1742-4682-3-24 Accepted I7 July 2006 This article is available from http www.tbiomed.eom content 3 1 24 2006 Kitayama et al licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License http creativecommons.org licenses by 2.0 which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background In order to improve understanding of metabolic systems there have been attempts to construct S-system models from time courses. Conventionally non-linear curve-fitting algorithms have been used for modelling because of the non-linear properties of parameter estimation from time series. However the huge iterative calculations required have hindered the development of large-scale metabolic pathway models. To solve this problem we propose a novel method involving power-law modelling of metabolic pathways from the Jacobian of the targeted system and the steady-state flux profiles by .