Previous efforts in understanding and exploiting allostery emphasize the need to better understand entropic contributions to binding, allostery, and activation. Allostery through concerted structural changes can be inferred directly from MSMs. However, dynamic allostery, considering the dynamical states of two residues or the dynamical states of one residue and structural states of another, is far more challenging. Capturing local entropic states requires parsing system kinetics properties and behaviors, inherently requiring parsing of the raw parallel simulations. Message passing algorithms have proven a frutiful way to consider holistic allosteric coupling, considering both structure and entropic states . Dynamic allostery, and its importance to biological phenomena, remains a frontier area in biophysics. A comprehensive understanding of allostery will require models that integrate both structural and dynamic data using large time-series data to uncovering novel mechanisms.