Methoden zur integrierten Analyse metabolischer Netzwerke unter stationären und instationären Bedingungen

Wahl, Sebastian Aljoscha

Dissertation / PhD Thesis/Book

Methoden zur integrierten Analyse metabolischer Netzwerke unter stationären und instationären Bedingungen

Wahl, S. A. (Corresponding author)FZJ*

2008
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag Jülich
ISBN: 978-3-89336-506-7

Jülich : Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag, Schriften des Forschungszentrums Jülich. Reihe Gesundheit / Health 1, (2008) = Universität Siegen, Diss., 2007

Please use a persistent id in citations: http://hdl.handle.net/2128/15176

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 .

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