The statistical properties of membrane protein random walks reveal information on the interactions between the proteins and their environments. These interactions can be included in an overdamped Langevin equation framework where they are injected in either or both the friction field and the potential field. Using a Bayesian inference scheme, both the friction and potential fields acting on the ε-toxin receptor in its lipid raft have been measured. Two types of events were used to probe these interactions. First, active events, the removal of cholesterol and sphingolipid molecules, were used to measure the time evolution of confining potentials and diffusion fields. Second, passive rare events, de-confinement of the receptors from one raft and transition to an adjacent one, were used to measure hopping energies. Lipid interactions with the ε-toxin receptor are found to be an essential source of confinement. ε-toxin receptor confinement is due to both the friction and potential field induced by cholesterol and sphingolipids. Finally, the statistics of hopping energies reveal sub-structures of potentials in the rafts, characterized by small hopping energies, and the difference of solubilization energy between the inner and outer raft area, characterized by higher hopping energies.

Tracking single molecules in living cells provides invaluable information on their environment and on the interactions that underlie their motion. New experimental techniques now permit the recording of large amounts of individual trajectories, enabling the implementation of advanced statistical tools for data analysis. In this primer, we present a Bayesian approach toward treating these data, and we discuss how it can be fruitfully employed to infer physical and biochemical parameters from single-molecule trajectories.