Development of Bioinformatic and Experimental Technologies for Identification of Prokaryotic Regulatory Networks

Lawrence, Charles E ; McCue, Lee Ann

Brown University, Providence, RI

关键词: 97 Mathematics And Computing Bioinformatics;    Bioinformatics;    59 Basic Biological Sciences;    Transcription Regulation;    Centroid;   

DOI  :  10.2172/935264
RP-ID  :  DOE/ER/63942-1
RP-ID  :  FG02-04ER63942
RP-ID  :  935264

美国|英语

来源: UNT Digital Library

【 摘 要 】

The transcription regulatory network is arguably the most important foundation of cellular function, since it exerts the most fundamental control over the abundance of virtually all of a cell’s functional macromolecules. The two major components of a prokaryotic cell’s transcription regulation network are the transcription factors (TFs) and the transcription factor binding sites (TFBS); these components are connected by the binding of TFs to their cognate TFBS under appropriate environmental conditions. Comparative genomics has proven to be a powerful bioinformatics method with which to study transcription regulation on a genome-wide level. We have further extended comparative genomics technologies that we introduced over the last several years. Specifically, we developed and applied statistical approaches to analysis of correlated sequence data (i.e., sequences from closely related species). We also combined these technologies with functional genomic, proteomic and sequence data from multiple species, and developed computational technologies that provide inferences on the regulatory network connections, identifying the cognate transcription factor for predicted regulatory sites. Arguably the most important contribution of this work emerged in the course of the project. Specifically, the development of novel procedures of estimation and prediction in discrete high-D settings has broad implications for biology, genomics and well beyond. We showed that these procedures enjoy advantages over existing technologies in the identification of TBFS. These efforts are aimed toward identifying a cell’s complete transcription regulatory network and underlying molecular mechanisms.