This PhD thesis contributes to the modeling and the numerical simulation of metabolic reaction networks coupled with gene expression via the inclusion of gene products as catalysts for the reactions. These networks describe the foundation of all (bio-)chemical processes in any living cell. A widespread method for modeling and analyzing these networks is the Flux Balance Analysis, which uses a stoichiometric description of the network and experimentally determined reaction rates to predict

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... y usage, growth rates and by-products. This method helps in understanding the inner workings of metabolic networks but is restrained to static environments and relies on experimentally measured reaction rates. The basis for this work is an extension of this method called dynamic enzymecost Flux Balance Analysis (deFBA). It models the metabolism as a resource allocation problem with the goal to maximize biomass accumulation in a dynamic environment. As this method is quite young, we start this work by introducing new standards for the deFBA with regards to the creation, saving and evaluation of the models. A result of this standardization is a software package in Python allowing simple access and evaluation to the models. The deFBA is limited by its reliance on a deterministic description of the environmental dynamics. But single-celled organisms have learned to prepare for unforeseen and sudden shifts in the availability of nutrients. To be able to model this robustness we developed a new simulation environment using a combination of the deFBA with the multi-stage Model Predictive Control. This shifts the objective of the modeled cells from optimal adaptation to the known environment to the more general goal of survival while retaining as much growth rate as possible. We expect this method to support the identification of the mechanisms leading to the robustness against environmental changes.