Prediction and analysis of gene regulatory networks in prokaryotic genomes

TitlePrediction and analysis of gene regulatory networks in prokaryotic genomes
Publication TypeBook Chapter
Year of Publication2011
AuthorsMünch, R, Klein, J, Jahn, D
Book TitleSystems and Computational Biology - Molecular and Cellular Experimental Systems
Chapter8
Pagination149-162
PublisherInTech
CityRijeka, Croatia
ISBN Number978-953-307-280-7
Abstract

The availability of over 1500 completely sequenced and annotated prokaryotic genomes offers a variety of comparative and predictive approaches on genome-scale. The results of such analyses strongly rely on the quality of the employed data and the computational strategy of their interpretation. Today, comparative genomics allows for the quick and accurate assignment of genes and often their corresponding functions. The resulting list of classified genes provides information about the overall genomic arrangement, of metabolic capabilities, general and unique cellular functions, however, almost nothing about the underlying complex regulatory networks. Transcriptional regulation of gene expression is a central part of these networks in all organisms. It determines the actual RNA, protein and as a consequence metabolite composition of a cell. Moreover, it allows cells to adapt these parameters in response to changing environmental conditions. An integral part of transcriptional regulation
is the specific interaction of transcription factors (TFs) with their corresponding DNA targets, the transcription factor binding sites (TFBSs) or motifs. Recent advances in extensive data mining using various high-throughput techniques provided first insights into the complex regulatory networks and their interconnections. However, the computational prediction of regulatory interactions in the promoter regions of identified genes remains to be difficult. Consequently, there is a high demand for the in silico identification and analysis of involved regulatory DNA sequences and the development of software tools for the accurate prediction of TFBSs.

URLhttp://www.intechopen.com/download/pdf/pdfs_id/20314