Integration of Sabio-RK to the Reactome Graph Database for Efficient Gathering of Cell Signaling Pathways

Over the years, several tools have been developed with the aim of recreating signaling pathways, allowing the in silico representation of a biological system to be glimpsed from afar, which would improve disease studies. However, despite all the progress, much information needed to create a reliable model is diffused in public repositories with different objectives (e.g., Sabio-RK, which stores kinetic constants, and Reactome, a database for biochemical reactions) and the computational cost for simulating large sections of pathways in an exponential universe of possibilities can be challenging. As an alternative to deal with complex and heavy data, graph databases have been increasingly used to represent biological models. Here, we present a way to combine the stored quantitative information from Sabio-RK into the Reactome Graph Database, while keeping the graph-based structure of the latter. To assess the proposed integration, we implemented it using Python and subsequently verified its correctness through cypher queries. We expect that such integrated database would be a useful tool for cell signaling pathways studies, especially in the designing of computational models of those pathways.