| dc.description.abstract | Systems Biology uses a multidisciplinary cooperation between dry lab and wet lab scientists, with the aim to carry out an integrated approach to produce, analyse and interpret biological data and assess it in the context of what is already known about a biological system. This thesis emphasises on the ‘dry lab’ side of systems biology in a number of different projects and approaches, which together illustrate the breadth and depth of the working domain of the systems biology ‘bioinformatician’.
These projects included-
a) ‘Knowledge discovery’ in studying plant stress responses: This involved a complete survey of the Systems Biology tools and resources available in the plant field and the use of some tools and resources (e.g. TAIR) for basic bioinformatics analysis of novel high throughput data produced in a HFSPO funded systems approach to understand plant defence mechanisms.
b) Analysis and application of knowledge for technology development in a project that aimed to test a new approach for clustering time series data, first by using gene ontology annotations for validation of data clusters from a high-throughput dataset that was divided in clusters that should be more robust than ordinary clusters obtained through kmeans or other unsupervised clustering methods. This in turn led to the creation of Genes2GO, a web application for biologists to obtain specific sets of gene ontology terms of specific sets of genes, in a matrix format. In addition, we did a network-based analysis of a signalling pathway of cholecystokinin receptors 1 and 2 (CCKR), representing the cellular signalling response to gastrin and cholecystokinin, built through literature curation. We investigated its properties in Cytoscape and modularised it using the BiNoM app, both for a simpler representation of the complex network and because individual network modules may be more relevant for studying particular pathway activity in specific cell lines.
c) ‘Knowledge management’: the process to take care of the knowledge generated by studies from the scientific community and to preserve this knowledge for future use. We created a database of manually curated transcription factors called TFcheckpoint. This curation work produced knowledge that was shared with the research community through the GO database, for standardised quality assurance and assured long term management.
Rapid evolution in technology leads to the production of a large amount of data and information, management of which needs community efforts. Our main aim has been to understand, spearhead and further structure this knowledge discovery process, its analysis and preservation in the field of biology. | nb_NO |
