To unravel the complex in vivo regulatory interdependences of biochemical networks, experiments with the living organism are absolutely necessary. Stimulus response experiments (SREs) have become increasingly popular in recent years. The response of metabolite concentrations from all major parts of the central metabolism is monitored over time by modem analytical methods, producing several thousand data points. SREs are applied to determine enzyme kinetic parameters and to find unknown enzyme regulatory mechanisms. Owing to the complex regulatory structure of metabolic networks and the amount of measured data, the evaluation of an SRE has to be extensively supported by modelling. If the enzyme regulatory mechanisms are part of the investigation, a large number of models with different enzyme kinetics have to be tested for their ability to reproduce the observed behaviour. In this contribution, a systematic model-building process for data-driven exploratory modelling is introduced with the aim of discovering essential features of the biological system. The process is based on data pre-processing, correlation-based hypothesis generation, automatic model family generation, large-scale model selection and statistical analysis of the best-fitting models followed by an extraction of common features. It is illustrated by the example of the aromatic amino acid synthesis pathway in Escherichia coli.