  Cell motion and appearance have a strong correlation with cell cycle and
disease progression. Many contemporary efforts in machine learning utilize
spatio-temporal models to predict a cell's physical state and, consequently,
the advancement of disease. Alternatively, generative models learn the
underlying distribution of the data, creating holistic representations that can
be used in learning. In this work, we propose an aggregate model that combine
Generative Adversarial Networks (GANs) and Autoregressive (AR) models to
predict cell motion and appearance in human neutrophils imaged by differential
interference contrast (DIC) microscopy. We bifurcate the task of learning cell
statistics by leveraging GANs for the spatial component and AR models for the
temporal component. The aggregate model learned results offer a promising
computational environment for studying changes in organellar shape, quantity,
and spatial distribution over large sequences.
