Systems biology analysis of large-scale gene expression data
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Abstract
Dynamics of gene expression in the context of gene regulatory networks are key to our understanding of cellular function. Particular with the advent of genome wide measurement of mRNA and protein abundances, large-scale gene expression data to investigate gene expression and gene expression networks are made available. Systems biology analysis provides means of extracting relevant information from this data and to further improve our quantitative understanding of mRNA transcription and consecutive translation into protein.
In this thesis a variety of biological topics related to transcriptional and translational gene regulation are addressed. Topics range from the identification of circadian expressed genes in the context of circadian rhythm, prediction of transcriptional regulator-target interactions from time-course gene expression data, dynamic modelling of the gene regulatory network coordinating the epithelial-to-mesenchymal transition, and the identification of post-transcriptionally regulated genes during textit{Drosophila} embryogenesis.
In each case study, collected large-scale gene expression data serves as the basis for computational analysis using a combination of different pre-existing as well as newly formulated methods. Whenever feasible, the performance of computational methods is evaluated and an experimental validation of predictions is pursued. As a result, the detailed computational analysis of large-scale gene expression data performed in this study not only provides valuable insight into the biological problem at hand, but further offers the opportunity for the development of systems biology tools and their evaluation under realistic experimental conditions.