Gene networks as a tool to understand transcriptional regulation.
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.
Veiga
Diogo Fernando
DF
Laboratório de Bioinformática, Centro de Informática, Universidade Federal de Pernambuco, Caixa Postal 7851, 50732-970 Recife, PE, Brazil. dfv@cin.ufpe.br
Vicente
Fábio Fernandes da Rocha
FF
Bastos
Gustavo
G
D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation / Q000235:genetics*; D006801:Humans; D008957:Models, Genetic; D010675:Pheromones / Q000235:genetics* / Q000378:metabolism; D012441:Saccharomyces cerevisiae / Q000737:chemistry*; D015398:Signal Transduction / Q000235:genetics*; D018709:Statistics, Nonparametric; D014158:Transcription, Genetic / Q000235:genetics*
D010675:Pheromones