In this paper, we propose a novel application of Generative Adversarial
Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy.
Compared to natural images, cells tend to have a simpler and more geometric
global structure that facilitates image generation. However, the correlation
between the spatial pattern of different fluorescent proteins reflects
important biological functions, and synthesized images have to capture these
relationships to be relevant for biological applications. We adapt GANs to the
task at hand and propose new models with casual dependencies between image
channels that can generate multi-channel images, which would be impossible to
obtain experimentally. We evaluate our approach using two independent
techniques and compare it against sensible baselines. Finally, we demonstrate
that by interpolating across the latent space we can mimic the known changes in
protein localization that occur through time during the cell cycle, allowing us
to predict temporal evolution from static images.