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@PHDTHESIS{Wahl:1253,
      author       = {Wahl, Sebastian Aljoscha},
      title        = {{M}ethoden zur integrierten {A}nalyse metabolischer
                      {N}etzwerke unter stationären und instationären
                      {B}edingungen},
      volume       = {1},
      school       = {Universität Siegen},
      type         = {Dr. (Univ.)},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {PreJuSER-1253},
      isbn         = {978-3-89336-506-7},
      series       = {Schriften des Forschungszentrums Jülich. Reihe Gesundheit
                      / Health},
      year         = {2008},
      note         = {Record converted from VDB: 12.11.2012; Universität Siegen,
                      Diss., 2007},
      abstract     = {In biological organisms a variety of enzyme catalyzed
                      reactions are taking place that deliver energy as well as
                      precursors for biomass and products . These reactions are
                      modeled by metabolic networks . The simulation and analysis
                      of these networks is getting more important especially for
                      „Metabolic Engineering" approaches. Current models are
                      very detailed and nowadays can only be handled with computer
                      techniques. Metabolic analyses are performed under different
                      experimental conditions depending an the aim of the studies.
                      Firstly metabolic stationary conditions are applied to
                      quantify metabolic fluxes under e .g . production
                      conditions. Secondly non-stationary conditions that aim at a
                      kinetic characterization of the metabolic interactions .
                      Kinetic models can in contrast to stationary fluxes be used
                      for prediction of further states. The modeling approaches
                      differ depending an the available measurements. In
                      literature up to now there is no systematic approach that
                      could be applied to the different conditions . Nevertheless
                      there are several overlaps and the approaches are based an
                      the same chemical and physical laws. In Gase of stationary
                      metabolic analyses relatively simple linear equation systems
                      are used . There are a variety of methods for analysis
                      available . Nevertheless, these theoretical concepts are
                      often not applied for the interpretation of measured data.
                      In the presented work a method is developed that enables to
                      use a theoretic method for the interpretation of a measured
                      flux distribution. Kinetic models are muck more evolved .
                      The information needed for modeling is only partly available
                      and sometimes even contradictorily. Therefore in this work a
                      concept was developed that enables to extract hypotheses an
                      reaction mechanisms from measured time-series data. Based an
                      these hypotheses a variety of possible models are set up and
                      fitted to the data . It was observed that several models are
                      able to reproduce the measured data. Fortunately these
                      models share common characteristics that seem to be
                      essential for the correct reproduction of the data. Several
                      approaches are discussed how the identiflcation of kinetic
                      parameters can be improved . It is shown that the
                      simultaneous evaluation of experiments with two genetically
                      different strains does improve the statistical accuracy.
                      From a simulative study it is derived that an additional
                      labeling pulse will further increase the statistical
                      accuracy up to a factor of ten .},
      cin          = {IBT-2},
      ddc          = {610},
      cid          = {I:(DE-Juel1)VDB56},
      pnm          = {Biotechnologie},
      pid          = {G:(DE-Juel1)FUEK410},
      typ          = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
      url          = {https://juser.fz-juelich.de/record/1253},
}