Microarray technology allows for high throughput gene expression studies. However, because of the large amounts of data produced, additional work is required to put the data into biological context. An emerging approach to provide this context is pathway and network analysis. The objective of this work was to design and implement such pathway analysis strategies. Our strategy identifies enriched biological pathways when given a list of genes found to be differentially expressed in a microarray experiment. After identification of these pathways, our strategy generates scores to quantify how significant and functional the pathways are. These scores include statistical significance and unique graph theory scores such as pathway port connectedness, adjacency ratio,and completeness. Enzyme-centric scores are also generated to quantify the importance of each enzyme coding gene in the pathway it participates in. Pathways in un-annotated organisms are inferred based on homology and several methods of visualizing the pathways are also provided. This approach can also be extended to proteomic data, or comparative genomic data to assess the relevance of a pathway or network in an organism or group of organisms.