Thermodynamic Models for the Analysis of Quantitative Transcriptional Regulation

Denis Bauer (2009). Thermodynamic Models for the Analysis of Quantitative Transcriptional Regulation PhD Thesis, Institute for Molecular Bioscience, The University of Queensland.

       
Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
s4088661_phd_abstract.pdf abstract application/pdf 160.09KB 0
s4088661_phd_totalthesis.pdf total thesis application/pdf 11.51MB 14
Author Denis Bauer
Thesis Title Thermodynamic Models for the Analysis of Quantitative Transcriptional Regulation
School, Centre or Institute Institute for Molecular Bioscience
Institution The University of Queensland
Publication date 2009-10
Thesis type PhD Thesis
Supervisor Timothy L. Bailey
Mikael Bodén
Total pages 251
Total colour pages 47
Total black and white pages 190
Subjects 06 Biological Sciences
Abstract/Summary Understanding transcriptional regulation quantitatively is a crucial step towards uncovering and ultimately utilizing the regulatory semantics encoded in the genome. Transcription of a gene is induced by the binding of site-specific transcription factors (TFs) to so-called cis-regulatory-modules (CRMs). The frequency and duration of the binding events are influenced by the concentrations of the TFs, the binding affinities and location of the transcription factor binding sites (TFBSs) in the CRM as well as the properties of the TFs themselves (e.g. effectiveness, competitive interaction with other TFs). Modeling these interactions using a mathematical approach, based on sound biochemical and thermodynamic foundations, enables the understanding and quantitative prediction of transcriptional output of a target gene. In the thesis I introduce the developed framework for modeling, visualizing, and predicting the regulation of the transcription rate of a target gene. Given the concentrations of a set of TFs, the TFBSs in a regulatory DNA region, and the transcription rate of the target gene, the method will optimize its parameters to generate a predictive model that incorporates the regulatory mechanism of the observed gene. I demonstrate the generalization ability of the model by subjecting it to standard machine learning and hypothesis testing procedures. Furthermore, I demonstrate the potential of the approach by training the method on a gene in D. melanogaster and predicting the output of the homologous genes in 12 other Drosophila species where the regulatory sequence has evolved substantially but the regulatory mechanism was conserved. Finally, I investigate the proposed role-switching behaviour of TFs regulating the development of D. melanogaster, which suggests that SUMOylation is the biological mechanism facilitating the switch.
Keyword Bioinformatics
Transcriptional Regulation
Gene Regulation
Transcription Factor
Thermodynamic modeling
Optimization Problems
Comparative Genomics
Evolution and phylogenetics
SUMOylation
D. melanogaster
Additional Notes colour 33-34, 37-40, 65-78, 79-80, 93-94, 97-98, 115-116, 119-130, 141-146, 165-166, 171-174, 179-192, 203-206, 227-230 landscape 53, 79, 248

 
Citation counts: Google Scholar Search Google Scholar
Access Statistics: 73 Abstract Views, 14 File Downloads  -  Detailed Statistics
Created: Wed, 21 Apr 2010, 15:33:11 EST by Ms Denis Bauer on behalf of Library - Information Access Service