Online Publikation - im ZESS - Universität Siegen

Summary
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 multi-
tude 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 in-
stead 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 ex-
amples 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 va-
riants fitting best to the experimental data.