Computational modeling of biological processes plays a key role in embodying various sources of experimental information into a comprehensive understanding of cellular behavior. These models give us the ability to perform in silico experiments to test hypotheses, better understand how pathway components and parameters affect the system and potentially lead to new experiments. Different modeling methodologies cover a wide range of spatiotemporal scales with each methodology being best suited to a particular class of problems based on the assumptions made and computational costs incurred. Biochemical models range from computationally inexpensive but spatially undetailed macro-scale continuum models to computationally expensive and spatially detailed all atomistic models. Agent based modeling is emerging as a highly capable method for simulating biochemical systems at the meso-scale range of the modeling spectrum with application to modeling cellular pathways. Agent based models of cellular pathways have traditionally relied on arbitrary movement rules and rates for simulating diffusion of biochemical components despite the fact that diffusion plays an important role in determining how the system evolves through time as a result of phenomena such as emergence of spatial gradients or molecular crowding. Subsequently, we explore methodologies for the accurate modeling of diffusion in agent based models.