Haunschild, Marc Daniel

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Haunschild, Marc Daniel

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    PublicationAccess status: Open Access ,
    Metabolische Stimulus-Response-Experimente : Werkzeuge zur Modellierung, Simulation und Auswertung
    (2006)
    Haunschild, Marc Daniel
    Metabolic modeling has become a major activity in metabolic engineering in recent years, in order to understand the complex regulation phenomena in a living cell. In this thesis, a versatile tool has been developed to support a modeler with the setup and refining of a model, that simulates the data extracted from a rapidly sampled stimulus response experiment. In the course of the modeling process, the modeler is typically not only concerned with a single model but with sequences, alternatives and structural variants of models. Supporting the modeling process of dynamic biochemical networks based on sampled in vivo data requires more than just simulation. For this purpose, the new concept of model families is specified and implemented in this thesis. With this concept, a multitude of similar models can be formulated in a single description by using network and kinetic variants The concept allows to automatically navigate in the space of models and to exclude biologically meaningless models on the basis of elementary flux mode analysis. An incremental usage of the measured data is supported by using splined data instead of state variables. Powerful automatic methods are then required to assist the modeler in the organization and evaluation of alternative models. This tool has been developed as a computational engine, intended to be built into a tool chain. By the use of automatic code generation, automatic differentiation for sensitivity analysis, and Grid computing technology, a high performance computing environment is achieved. It supplies XML model specification and several software interfaces. The performance and usability of the tool of this thesis is illustrated by several examples from ongoing research projects. An optimization algorithm was developed, enabling the software tool to automatically carry through the tasks of model variant switching and parameter fitting. The computation results into a ranking of models variants fitting best to the experimental data.
    Source Type:Doctoral Thesis
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