This thesis investigates the underlying mechanism and the effects of anomalous diffusion in crowded fluids by means of computer simulations. In order to elucidate the mechanism behind crowding-induced subdiffusion we discuss the average shape of tracer trajectories as a potential criterion that allows to reliably discriminate between frequently proposed models. Our simulations show that measurement errors inherent to single particle tracking generally impair the determination of the underlying random process from experimental data. We propose a particle-based model for the crowded cytoplasm that incorporates soft-core repulsion and weak attraction between globular proteins of various sizes. Under these prerequisites simulations reveal transient subdiffusion of proteins. On experimental time scales, however, diffusion is normal indicating that realistic, microscopic models of crowded fluids require further detail of the relevant interactions. In the second part of this thesis, the impact of subdiffusion on biochemical reactions is studied via mesoscopic, stochastic simulations. Due to their compact trajectories subdiffusive reactants get increasingly segregated over time. This results in anomalous kinetics that differs strongly from classical theories. Moreover, for a two-step reaction scheme relying on an intermediate dissociation-association event, subdiffusion can substantially improve the overall productivity because spatio-temporal correlations are exploited with high efficiency
