Multi Weighted Graphs as Magnifier to Discover Hidden Cross-Interactions among Biological Pathways

Scientists and researchers need robust and flexible methods to identify interactions among multiple biological pathways, which unveil crucial insights otherwise obscured when pathways are analyzed in isolation. Understanding these complex interrelations is vital for comprehensively mapping the processes relevant to their research.Biological pathways are categorized into three main classes: signaling, metabolic, and regulatory.The pathways representation can be significantly enhanced using multi weighted graphs models, a concept from graph theory where multiple edges can connect nodes. This allows for a more nuanced representation of the complex interactions and multiple relationships between pathway elements. In this model, each node represents a pathway element, and the multiple edges between nodes depict various types of interactions, capturing the dynamic and multifaceted nature of cellular processes.To address the needs of researchers for more sophisticated and accurate analysis, we have developed a new methodology to convert and represent biological pathways as multi weighted graphs. In this way, it is possible to recall the graph theory metrics to evaluate the elements within the graphs. We show that the use of multi weighted graphs to connect several pathways, allowing to identify hidden cross-pathways links, e.g., genes shared among pathways, through the use of PageRank and Kats index. The discovered genes allow to improve of some order the magnitude of pathway enrichment analysis results, as conveyed from the reached p-values.