This thesis makes significant advances in the theoretically-grounded analysis of experimental biophysical data, applying existing and novel tools from stochastic thermodynamics to study multicomponent biological molecular machines. The work in this book derives fundamental limits, explores model systems, and develops tools for inference from experimental data, all of which allow for novel analysis of molecular machines. Particular innovations reported in this thesis include: a new Jensen inequality relating subsystem entropy production to physically accessible measurements, which leads to performance bounds and Pareto frontiers for collective transport of intracellular cargo; a new approach to quantify the efficiency of coupled components in multicomponent motors, drawing upon the language of information thermodynamics; and a new theoretical understanding of symmetries between heat and information engines, with surprising implications for light-harvesting molecular machines like those responsible for photosynthesis. Ultimately, these advances lead to the identification of design principles which will help to guide future engineering of synthetic nanomachines.
