title: A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles.
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 linearization of S-systems.
  
  
  RESULTS
  The results of two case studies modelling a straight and a branched pathway, respectively, showed that our method reduced the number of unknown parameters needing to be estimated. The time-courses simulated by conventional kinetic models and those described by our method behaved similarly under a wide range of perturbations of metabolite concentrations.
  
  
  CONCLUSIONS
  The proposed method reduces calculation complexity and facilitates the construction of large-scale S-system models of metabolic pathways, realizing a practical application of reverse engineering of dynamic simulation models from the Jacobian of the targeted system and steady-state flux profiles.
authors.affiliation: Institute of Advanced Bioscience, Keio University, Fujisawa, 252-8520, Japan. tomoyan@sfc.keio.ac.jp
authors.forename: Tomoya
authors.initials: T
authors.lastname: Kitayama
authors.forename: Ayako
authors.initials: A
authors.lastname: Kinoshita
authors.forename: Masahiro
authors.initials: M
authors.lastname: Sugimoto
authors.forename: Yoichi
authors.initials: Y
authors.lastname: Nakayama
authors.forename: Masaru
authors.initials: M
authors.lastname: Tomita
mesh_terms: D007700:Kinetics; D008660:Metabolism / Q000502:physiology*; D008954:Models, Biological; D013997:Time Factors