ABSTRACT:
Graph-based approaches in bioinformatics and biotechnology provide valuable insights into complex biological systems. Building on prior research comparing co-expression and protein-protein interaction (PPI) networks in S. cerevisiae, we construct an intersection graph and analyze common genes using R. First, we examine the structural properties of the PPI graph, analyzing its degree distribution and identifying connected components. Most subgraphs are singletons; however we visualize communities present in the third and seventh components, to reveal genes crucial for maintaining cellular functions. Next, the network analysis reveals thirty clusters of genes, forming sub- graphs that are internally connected but disjoint from one another. Pairs of genes with both PPI and shared expression cluster membership are identified. Cohesive subgroups within the intersection graph highlight 2-cliques, with at least four genes linked to DNA replication and polymerases, which are responsible for maintaining genomic integrity during cell division. Finally, we assess centrality measures within the intersection graph, identifying top ten genes ranked by degree, betweenness, closeness and eigenvector centrality to understand the importance of individual nodes which may be involved in cellular and genomic stability, as well as global cellular functions. This study demonstrates how graph-theoretical methods can aid in identifying biologically relevant genes. We conclude by discussing the broader implications of this study for bioinformatics and biotechnology, emphasizing the importance of genomic stability and the challenges of modeling dynamic molecular networks using graph-based methods.