Computational analyses on transcriptional regulation in mammals

Abstract

The genomes of various organisms have been sequenced and their transcriptome elucidated. With the information about genes and gene products readily available it has become of the utmost importance to decipher the underlying biological mechanisms that are involved in the transcriptional control of these genes. Transcription initiation is a fundamental process in living cells. It involves the interaction of transcription factors with DNA to regulate the transcription of a gene. Despite significant research during the last few decades into transcription factors and their role in gene regulation we are still far from understanding the complete transcriptional machinery that acts within biological systems. In this dissertation two computational approaches are presented to contribute to a better understanding of the transcriptional control of genes in mammals. The first addresses the transcriptional regulation of microRNA genes and its influence on the microRNA gene expression during monocytic differentiation. This is the first large-scale approach to decipher how microRNA genes are regulated by transcription factors during monocytic differentiation. The second approach relates to combinatorial gene regulation and the physical interaction of transcription factors. Here, a computational approach is used together with a novel form of numerical representation of transcription factors to predict their interactions. In this setup, the information necessary to predict the transcription factor interactions is kept at the lowest level to minimize the data acquisition overhead that often occurs in computational prediction tasks. Both approaches enhance our insights into transcriptional control and have an impact on the further study of gene regulation.