  Time-lapse fluorescence microscopy (TLFM) is an important and powerful tool
in synthetic biological research. Modeling TLFM experiments based on real data
may enable researchers to repeat certain experiments with minor effort. This
thesis is a study towards deep learning-based modeling of TLFM experiments on
the image level. The modeling of TLFM experiments, by way of the example of
trapped yeast cells, is split into two tasks. The first task is to generate
synthetic image data based on real image data. To approach this problem, a
novel generative adversarial network, for conditionalized and unconditionalized
image generation, is proposed. The second task is the simulation of brightfield
microscopy images over multiple discrete time-steps. To tackle this simulation
task an advanced future frame prediction model is introduced. The proposed
models are trained and tested on a novel dataset that is presented in this
thesis. The obtained results showed that the modeling of TLFM experiments, with
deep learning, is a proper approach, but requires future research to
effectively model real-world experiments.
