Articular Cartilage (AC) is the main load carrying material in synovial joints {Hamerman, 1962} and degeneration of AC can cause pain in the form of arthritis. Current work is centered on the method of replacing damaged cartilage inside the body (in vivo) with tissue engineered outside the body (ex vivo) {Temenoff, 2000}. In order to engineer tissue ex vivo similar to the native tissue in structure and function there must be a comprehensive understanding of the mechanical properties of AC. This work focuses on the study of glycosaminoglycans (GAGs), a molecule known to be primarily responsible for the compressive stiffness of AC, using molecular dynamics methods. First, a single chain simulation is run to establish a chain length to use for the rest of the study. Then two more simulations are run that mimic a possible physical scenario for changing GAG density. The first is a five chain simulation that mimics the situation where GAG chains are compressed and pushed together. Pressure and density relations are generated and compared to the micro-structural level Donnan model {Maroudas, 1979} and Poisson-Boltzmann unit cell (PB) model {Marcus, 1955}. The last simulation imitates the scenario of one GAG chain sliding between two adjacent GAG chains. The work to pull the central chain through the adjacent chains is calculated and plotted at different chain spacing. A 20 disaccharide-unit long chain is found to be the most stable chain length, but for the purpose of saving computational time without a large loss in stability a 10 unit chain is used for the rest of the simulations. The pressure-density relations found from the five chain simulation are of the same magnitude as the micro-structural level models. Observations made based on the graphical playback of the pulling simulation give insight into the importance of ion interaction with the GAG chains. It was found to take more work to pull the chain with more open space around because of the binding nature of the ions coming between the chains. The tighter spaced chains allowed fewer ions to fit between chains creating less binding force, therefore taking less work to pull. This work can be scaled up to the next level using coarse-graining methods which will be more comparable to experimental work, possibly leading to results that will help characterize AC for better implementation of engineered tissue.