Biophysical Constraints on Decoding Temporal Dynamics

Recent advances in visualization of single molecules, such as transcription factors with temporal resolution in single cells, have shown that information is transmitted through time-varying dynamics of components shared between multiple pathways. This phenomenon stands in contrast to the typical paradigm where information is transmitted via structurally specific interactions (e.g. the lock and key model). Consequently, signaling through time dynamics via shared components raises natural questions about how such interactions can effect only the intended response. By analyzing realistic, coarse-grained biochemical networks informed by experimental studies, we explore the response of general network topologies to temporally varying upstream inputs. From these results, we are able to establish theoretical limits on the signaling capacity of such networks as a function of biophysical parameters and network complexity, emphasizing the importance of functional network diversity in decoding the full dimensionality of the input space.