Modeling non linear dynamic phenomena in biochemical networks Facilitated by the development of high throughput techniques the focus of biological research has changed in the last decades from the investigation of single cell components to a system level approach which aims at an understanding of interactions between these cell components. This objective requires modeling and analysis methods for these regulatory networks. In this thesis we investigate mechanisms causing qualitative dynamic behaviors of regulatory subsystems. For this purpose we introduce a differential equation model based on underlying molecular binding reactions whose parameters are estimated using time series concentration data. In the first part the model is applied to subsystems with qualitatively different dynamic behaviors: The response of the Mycobacterium tuberculosis to DNA damages is described as the relaxation of a system to its steady state after external perturbation. Specific repression of genes in Escherichia coli by the global regulator protein H NS is explained by the interrelation of feedback mechanisms. In order to prevent overfitting a typical problem in network inference from experimental data we introduce an approach based on Bayesian statistics which includes prior knowledge about the system in terms of prior probability distributions ...