In this work, I investigated the motion of bacteriophages (phages) through their mucosal environment. Recently, biologists here at San Diego State University have proposed a model in which phages move sub-diffusively through mucosal fibers in their hunt for bacteria to prey upon. Through a Hoc protein located upon the capsid of the wild type phages, these phages are allowed to bind to mucosal fibers, and extend the amount of time spent in a single location hunting for bacteria. Contrarily, the delta hoc phages are unable to. The ability of the wild type phages to attach itself to mucosal fibers is what enables its subdiffusive behavior. This study investigates the diffusive behavior of these phages in different mucus concentrations. It expands on previous studies in which only short tracks could be observed. In the study at hand, phages are imaged in a highly doped optical fiber with varying concentrations of mucus present in solution. Through rigorous image processing techniques, trajectories of these phages are created with a minimized noise level. We developed code that created position-versus-time files for each phage present in the experimental data. These files were then further analyzed. The sub-diffusive behavior is investigated via mean squared displacement versus time. The diffusive exponent can be obtained from fits to these data. For large enough time intervals, I always obtained an exponent of one for space and time averaged data. This indicates that the diffusion is normal, or sub-diffusive of the CTRW type. CTRW sub-diffusive motion is characterized by waiting times that resemble a power law distribution and have long tails. I investigate these stuck time distributions, however am unable to determine if a power law or exponential fits the data best. Moreover, the distribution gives the same power law exponent for phages moving through water, or mucus, for wild type and delta hoc phages. These exponents would predict super-diffusive instead of sub-diffusive behavior. We conclude that many of these problems result from the small amount of data available to us and the still primitive conditions of the setup at the time the data were collected.