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Sudden cardiac arrest is a leading cause of death world wide, with about 100.000 to 150.000 cases each year in Germany alone [Weidringer and Sefrin, 2006]. This means that annually one out of 1000 citizens are affected [Bahr, 2007]. At standard conditions the human brain has a relative low ischemic1 tolerance. Therefore after 3 - 5 minutes without therapy, irreversible damage is to be expected. The rate of survival drops 7% - 10% each minute, without resuscitation [Bahr, 2007]. Since the arrival of the organized emergency medical service usually takes more than 5 minutes after the emergency call [Wahlen et al., 2003, Weisfeldt et al., 2010], the instant and adequate resuscitation by bystanders in this period is of vital importance. The advantage of basic life support2 (BLS) by laymen shows a fourfold higher rate of survival, once resuscitation has begun, until the arrival of the emergency medical service [Bahr, 2007].
Cytoscape is an open source platform for complex network analysis and visualisation. The Pathway Interaction Database (PID) is a highly structured, curated collection of information about known biomolecular interactions and key cellular processes assembled into signalling pathways. Despite the obvious potential and advantageous usage of both tool (Cytoscape) and information source (PID), there has been no conclusive effort to merge and synergise them. This project aims to make use of the open source characteristics of Cytoscape and optimally visualise the biomolecular interactions found in the PID. This is made possible by the development of a plugin which imports a user-selected pathway file, converts it into a Cytoscape-readable file, and then visualises it. Finally, the user has options to further optimise the pathway by the use of a filter (Barcode – Affymetrix) that removes nodes from the network which are lowly expressed in the Affymetrix microarray data. The user then obtains visual results in a matter of seconds. Additionally, the process of subgraphing nodes through the shortest path method could be applied to the network. This can further assist the user in identifying the molecular pathways of the nodes of interest, a useful feature in network analysis.