  A Recurrent Neural Network (RNN) was used to perform video frame prediction
of microbial growth for a population of two mutants of Pseudomonas aeruginosa.
The RNN was trained on videos of 20 frames that were acquired using
fluorescence microscopy and microfluidics. The network predicted the last 10
frames of each video, and the accuracy's of the predictions was assessed by
comparing raw images, population curves, and the number and size of individual
colonies. Overall, we found the predictions to be accurate using this approach.
The implications this result has on designing autonomous experiments in
microbiology, and the steps that can be taken to make the predictions even more
accurate, are discussed.
