ArticleProceedings of the National Academy of Sciences of the United States of America2002
A systematic approach to reconstructing transcription networks in Saccharomycescerevisiae.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2002. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Network-based approaches that exploit inferred transcription factor activity to analyze the impact of genetic variation on gene expression.Current opinion in systems biology · 2017Article
- Using Sub-Network Combinations to Scale Up an Enumeration Method for Determining the Network Structures of Biological Functions.PloS one · 2016Article
- SLDR: a computational technique to identify novel genetic regulatory relationships.BMC bioinformatics · 2014Article
- Differential binding of the related transcription factors Pho4 and Cbf1 can tune the sensitivity of promoters to different levels of an induction signal.Nucleic acids research · 2013Article
- Leveraging models of cell regulation and GWAS data in integrative network-based association studies.Nature genetics · 2012Article
- Mathematical biology modules based on modern molecular biology and modern discrete mathematics.CBE life sciences education · 2010Article
- Characteristics of transcriptional activity in nonlinear dynamics of genetic regulatory networks.Gene regulation and systems biology · 2009Article
- A primer on regression methods for decoding cis-regulatory logic.PLoS computational biology · 2009Article
- Classifying transcription factor targets and discovering relevant biological features.Biology direct · 2008Article
- Identification of direct target genes using joint sequence and expression likelihood with application to DAF-16.PloS one · 2008Article
- Rank-based edge reconstruction for scale-free genetic regulatory networks.BMC bioinformatics · 2008Article
- fREDUCE: detection of degenerate regulatory elements using correlation with expression.BMC bioinformatics · 2007Article
- A statistical method to incorporate biological knowledge for generating testable novel gene regulatory interactions from microarray experiments.BMC bioinformatics · 2007Article
- Machine learning for regulatory analysis and transcription factor target prediction in yeast.Systems and synthetic biology · 2007Article
- Analysis of time-series gene expression data: methods, challenges, and opportunities.Annual review of biomedical engineering · 2007Article
- A correlation with exon expression approach to identify cis-regulatory elements for tissue-specific alternative splicing.Nucleic acids research · 2007Article
- Connectivity in the yeast cell cycle transcription network: inferences from neural networks.PLoS computational biology · 2006Article
- Bayesian error analysis model for reconstructing transcriptional regulatory networks.Proceedings of the National Academy of Sciences of the United States of America · 2006Article
- Iterative reconstruction of transcriptional regulatory networks: an algorithmic approach.PLoS computational biology · 2006Article
- Modeling gene expression control using Omes Law.Molecular systems biology · 2006Article
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4 authors.
Funding
Abstract
Decomposing regulatory networks into functional modules is a first step toward deciphering the logical structure of complex networks. We propose a systematic approach to reconstructing transcription modules (defined by a transcription factor and its target genes) and identifying conditionsperturbations under which a particular transcription module is activateddeactivated. Our approach integrates information from regulatory sequences, genome-wide mRNA expression data, and functional annotation. We systematically analyzed gene expression profiling experiments in which the yeast cell was subjected to various environmental or genetic perturbations. We were able to construct transcription modules with high specificity and sensitivity for many transcription factors, and predict the activation of these modules under anticipated as well as unexpected conditions. These findings generate testable hypotheses when combined with existing knowledge on signaling pathways and protein-protein interactions. Correlating the activation of a module to a specific perturbation predicts links in the cell's regulatory networks, and examining coactivated modules suggests specific instances of crosstalk between regulatory pathways.
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