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Time-lapse fluorescence microscopy (TLFM) is an important and powerful toolin synthetic biological research. Modeling TLFM experiments based on real datamay enable researchers to repeat certain experiments with minor effort. Thisthesis is a study towards deep learning-based modeling of TLFM experiments onthe image level. The modeling of TLFM experiments, by way of the example oftrapped yeast cells, is split into two tasks. The first task is to generatesynthetic image data based on real image data. To approach this problem, anovel generative adversarial network, for conditionalized and unconditionalizedimage generation, is proposed. The second task is the simulation of brightfieldmicroscopy images over multiple discrete time-steps. To tackle this simulationtask an advanced future frame prediction model is introduced. The proposedmodels are trained and tested on a novel dataset that is presented in thisthesis. The obtained results showed that the modeling of TLFM experiments, withdeep learning, is a proper approach, but requires future research toeffectively model real-world experiments.

Generation and Simulation of Yeast Microscopy Imagery with Deep Learning