%0 Journal Article
%T PathSys: integrating molecular interaction graphs for systems biology
%A Michael Baitaluk
%A Xufei Qian
%A Shubhada Godbole
%A Alpan Raval
%A Animesh Ray
%A Amarnath Gupta
%J BMC Bioinformatics
%D 2006
%I BioMed Central
%R 10.1186/1471-2105-7-55
%X Here we present PathSys, a graph-based system for creating a combined database of networks of interaction for generating integrated view of biological mechanisms. We used PathSys to integrate over 14 curated and publicly contributed data sources for the budding yeast (S. cerevisiae) and Gene Ontology. A number of exploratory questions were formulated as a combination of relational and graph-based queries to the integrated database. Thus, PathSys is a general-purpose, scalable, graph-data warehouse of biological information, complete with a graph manipulation and a query language, a storage mechanism and a generic data-importing mechanism through schema-mapping.Results from several test studies demonstrate the effectiveness of the approach in retrieving biologically interesting relations between genes and proteins, the networks connecting them, and of the utility of PathSys as a scalable graph-based warehouse for interaction-network integration and a hypothesis generator system. The PathSys's client software, named BiologicalNetworks, developed for navigation and analyses of molecular networks, is available as a Java Web Start application at http://brak.sdsc.edu/pub/BiologicalNetworks webcite.Complex networks of molecular and genetic interactions are increasingly being studied for insights into biological mechanisms [1-3]. Such studies include deciphering genome-wide protein-protein interactions [4]], large-scale analysis and prediction of gene regulatory networks [5], construction of metabolic pathways [6], and development of synthetic genetic interaction networks [7,8]. Here we collectively call these different networks Molecular Interaction Graphs (MIGs). The availability of MIGs has paved the way for the emergence of a new paradigm of biology in which networks of interactions are being analyzed for understanding of biological phenomena [3,9-12]. Truly integrated analyses across multiple databases of different functionalities are still rare yet promising [13]. Suc
%U http://www.biomedcentral.com/1471-2105/7/55