  Multi-component molecular machines are ubiquitous in biology. We review
recent progress on describing their thermodynamic properties using autonomous
bipartite Markovian dynamics. The first and second laws can be split into local
versions applicable to each subsystem of a two-component system, illustrating
that one can not only resolve energy flows between the subsystems but also
information flows quantifying how each subsystem's dynamics influence the joint
system's entropy balance. Applying the framework to molecular-scale sensors
allows one to derive tighter bounds on their energy requirement. Two-component
strongly coupled machines can be studied from a unifying perspective
quantifying to what extent they operate conventionally by transducing power or
like an information engine by generating information flow to rectify thermal
fluctuations into output power.
