Development of advanced techniques for biological network visualisation is crucial for successful progress in the areas of systems-level biology and data-intensive bioinformatics. However, current techniques for biological network visualisation fall short of expectations for representing extensive biological networks. In order to provide really useful network visualisation tools, new approaches have to be proposed and applied alongside with those most powerful features of current visualisation systems. The resulting representation techniques have to be tested by applying to large-scale examples that would include metabolic, signaling and gene expression events. User survey should also be carried out to further prove the advantages of the new techniques. The present thesis describes an attempt to achieve the above objectives, by performing the following steps: 1) existing approaches in the area of network representation were analyzed and their shortcomings and advantages were defined; 2) new notation has been developed, in which, the defined best features of the existing systems were integrated with newly introduced potent features such as compact visualization, ‘functional gate’ and ‘identity gate’, 4) new framework was developed that allows managing large-scale networks that are represented on different levels of details and different levels of constrains, while keeping each diagram semantically unambiguous, 5) extensive examples, including genome-scaled human metabolic network and TNF-alpha receptor signalling network, were used to prove that the designed notation and the framework can be applied efficiently, and, finally, 6) a notation survey has been carried out to validate the advantages of the newly developed notation over the existing ones.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:563405 |
| Date | January 2011 |
| Creators | Mazein, Alexander |
| Contributors | Goryanin, Igor. : Sorokin, Anatoly |
| Publisher | University of Edinburgh |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://hdl.handle.net/1842/5295 |
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