Interpreting time series data from dynamic signaling pathways in single cells

A growing body of evidence suggests that cells encode information in the dynamics of signaling molecules. For instance, both the identity and dose of different external ligands may be encoded in the temporal dynamics of a single transcription factor. Understanding which aspects of experimental time-series are informative, and which can plausibly be decoded by cells given biochemical constraints, remains an open problem. Here, we combine modified versions of interpretable machine learning techniques, such as InfoGAN, with domain knowledge of the NF-kB pathway to obtain insights on how experimental time-series data of NF-kB encodes information about the ligands TNFa and IL-2.