Bedoukian   RussellIPM   RussellIPM   Piezoelectric Micro-Sprayer


Home
Animal Taxa
Plant Taxa
Semiochemicals
Floral Compounds
Semiochemical Detail
Semiochemicals & Taxa
Synthesis
Control
Invasive spp.
References

Abstract

Guide

Alphascents
Pherobio
InsectScience
E-Econex
Counterpart-Semiochemicals
Print
Email to a Friend
Kindly Donate for The Pherobase

« Previous AbstractBromine in waste incineration: partitioning and influence on metal volatilisation    Next AbstractFatal accidental inhalation of brake cleaner aerosols »

Genet Mol Res


Title:Gene networks as a tool to understand transcriptional regulation
Author(s):Veiga DF; Vicente FF; Bastos G;
Address:"Laboratorio de Bioinformatica, Centro de Informatica, Universidade Federal de Pernambuco, Caixa Postal 7851, 50732-970 Recife, PE, Brazil. dfv@cin.ufpe.br"
Journal Title:Genet Mol Res
Year:2006
Volume:20060331
Issue:1
Page Number:254 - 268
DOI:
ISSN/ISBN:1676-5680 (Electronic) 1676-5680 (Linking)
Abstract:"Gene regulatory networks, or simply gene networks (GNs), have shown to be a promising approach that the bioinformatics community has been developing for studying regulatory mechanisms in biological systems. GNs are built from the genome-wide high-throughput gene expression data that are often available from DNA microarray experiments. Conceptually, GNs are (un)directed graphs, where the nodes correspond to the genes and a link between a pair of genes denotes a regulatory interaction that occurs at transcriptional level. In the present study, we had two objectives: 1) to develop a framework for GN reconstruction based on a Bayesian network model that captures direct interactions between genes through nonparametric regression with B-splines, and 2) to demonstrate the potential of GNs in the analysis of expression data of a real biological system, the yeast pheromone response pathway. Our framework also included a number of search schemes to learn the network. We present an intuitive notion of GN theory as well as the detailed mathematical foundations of the model. A comprehensive analysis of the consistency of the model when tested with biological data was done through the analysis of the GNs inferred for the yeast pheromone pathway. Our results agree fairly well with what was expected based on the literature, and we developed some hypotheses about this system. Using this analysis, we intended to provide a guide on how GNs can be effectively used to study transcriptional regulation. We also discussed the limitations of GNs and the future direction of network analysis for genomic data. The software is available upon request"
Keywords:"Bayes Theorem Gene Expression Profiling/methods Gene Expression Regulation/*genetics Humans Models, Genetic Pheromones/*genetics/metabolism Saccharomyces cerevisiae/*chemistry Signal Transduction/*genetics Statistics, Nonparametric Transcription, Genetic/;"
Notes:"MedlineVeiga, Diogo Fernando Vicente, Fabio Fernandes da Rocha Bastos, Gustavo eng Research Support, Non-U.S. Gov't Brazil 2006/06/07 Genet Mol Res. 2006 Mar 31; 5(1):254-68"

 
Back to top
 
Citation: El-Sayed AM 2024. The Pherobase: Database of Pheromones and Semiochemicals. <http://www.pherobase.com>.
© 2003-2024 The Pherobase - Extensive Database of Pheromones and Semiochemicals. Ashraf M. El-Sayed.
Page created on 01-07-2024