High-throughput studies continue to produce volumes of data, providing a wealth of information that can be used to better guide biological research. However, models that can readily identify true biological signals from this data have not been developed at the same rate, due in part to a lack of well-developed algorithms that can handle the magnitude, variability and veracity of the data. One promising and effective solution to this complex issue is network modeling, due to its capabilities for representing biological elements and relationships en masse. In this research, we use correlation networks for analysis where genes are represented as nodes and indirect relationships (derived from expression patterns) are represented as edges. Here, we define "gateway" nodes as elements representing genes that change in co-expression and possibly co-regulation between states. We use the gateway node approach to identify critical genes in the aging mouse brain and perform a cursory investigation of the robustness of these gateway nodes according to network structure. Our results highlight the power of the gateway nodes approach and show how it can be used to limit search space and determine candidate genes for targeted studies. The novelty of this approach lies in application of the gateway node approach on novel mouse datasets, and the investigation into robustness of network structures.