The metabolism of cells is based on a huge network of interlinked chemical reactions and membrane transport processes providing the building blocks and energy (ATP) for a multitude of different physiological functions such as growths, proliferation, motility and secretion of extracellular metabolites. Since more than four decades, mathematical models based on principles of chemical reaction kinetics have been successfully applied to understand and predict the dynamic behavior of specific metabolic pathways, e.g. glycolysis or the tricarbonic acid cycle, in terms of the underlying individual enzymatic reactions and specific external conditions. More recently, sequencing of full genomes and the establishment of high-throughput methods (transcriptomics, proteomics, metabolomics…) have challenged the development of novel modeling techniques, which are suited to model whole metabolic networks even in cases where detailed information on the kinetic properties of individual reactions is lacking. In the first part of my talk, I will present a critical review of modeling techniques and bioinformatics’ tools commonly applied in metabolic research. In the second part, I will focus on results and open issues of our work on the dynamics of metabolic networks in neurons and liver cells.