As the scientific curiosity shifts toward system-level investigation of genomicscale information, data produced about cellular processes at molecular level has been accumulating with an accelerating rate. Graph-based pathway ontologies and databases have been in wide use for such data. This representation has made it possible to programmatically integrate cellular networks as well as investigating them using the well-understood concepts of graph theory to predict their structural and dynamic properties. In this regard, it is essential to effectively query such integrated large networks to extract the sub-networks of interest with the help of efficient algorithms and software tools. Towards this goal, we have developed a querying framework along with a number of graph-theoretic algorithms from simple neighborhood queries to shortest paths to feedback loops, applicable to all sorts of graph-based pathway databases from PPIs to metabolic pathways to signaling pathways. These algorithms can also account for compound or nested structures present in the pathway data, and have been implemented within the querying components of Patika (Pathway Analysis Tools for Integration and Knowledge Acquisition) tools and have proven to be useful for answering a number of biologically significant queries for a large graph-based pathway database.