The last decade has seen substantial advances in single-molecule tracking methods with nano-metre level precision. A powerful tool in single-molecule tracking is fluorescence imaging. One particular application, total internal reflection microscopy, can capture biological processes at high contrast video rate imaging at the single-particle level. This thesis presents methodologically novel methods in analysing single particle tracking data. Presented here is an application of a Bayesian statistical approach that can discriminate between the different diffusive modes that appear with the presence of membrane architecture. This algorithm is denoted BARD; a Bayesian Analysis to Ranking Diffusion. These algorithms are applied to a total internal fluorescence microscopy based experimental data of a novel membrane probe in Escherichia coli. This probe is a plasmid expressed, non-native membrane integrating trans-membrane helix and thus acts as an ideal protein based probe under no specific native control. Two experiments were performed using a combination of varying helix probe size and growth temperature experiments effectively altering the transition temperature of the membrane. These data are suggestive of a passive partitioning of the helix protein into mobile and immobile domains that emerge from the underlying phase behaviour of the membrane.