Genomic analysis of regulatory network dynamics reveals large topological changes

By Nicholas M. Luscombe, M. Babu, Haiyuan Yu, M. Snyder, Sarah A. Teichmann, Mark Gerstein. Nature, 2004

1,029 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f8fc771e4665c254cdc452edd72fd1bf4586b17f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

Dynamic mixed membership blockmodel for evolving networks (2009, methodology): “In this section, we use our dMMSB as a visualization and exploration tool to study the dynamic gene regulatory networks of yeast collected by Luscombe et al. (2004). This dynamic network is built upon a static network assembled from known regulatory interactions from genetic, biochemical and ChIP-ch”
Studying and modelling dynamic biological processes using time-series gene expression data (2012, background): “Early work along these lines 82 identified differentially expressed genes at different time points and used these to assign, for each time point, a set of transcription factors that were bound to the differentially expressed genes, even though the transcription factor binding was only determined in…”
NOA: a novel Network Ontology Analysis method (2011, methodology): “19, and an example for protein–protein interaction network dynamic in the tissue-speciﬁc protein interaction networks in ref.”
Time-Varying Dynamic Bayesian Networks (2009, background): “As a result, the molecular networks at each time point are context-dependent and can undergo systematic rewiring rather than being invariant over time [3].”
A state-space mixed membership blockmodel for dynamic network tomography (2008, background): “, the gene regulatory networks at each time point are context-dependent DYNAMIC NETWORK TOMOGRAPHY 21 and can undergo systematic rewiring, rather than being invariant over time. In a seminal study by Luscombe et al. (2004), it was shown that the “active regulatory paths” in the gene regulatory netwo”
Transcriptional Regulatory Networks in Saccharomyces cerevisiae (2009, background): “The endogenous and exogenous subnetworks were also analyzed for the presence of network motifs [15].”
Human protein interaction networks across tissues and diseases (2015, methodology): “This approach had been used previously in model organisms to analyze their network dynamics in response to stimuli (Luscombe et al., 2004) or during cell cycle (de Lichtenberg et al., 2005), and has been used extensively for analyzing tissue interactomes (e.g., Lopes et al., 2011; Barshir et al.,…”
Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles (2007, background): “Pioneering efforts to identify regulatory interactions on a genome scale have used machine-learning algorithms to identify cis-regulatory motifs or transcription factor target genes using a large set of expression arrays [2–18], genomewide location analysis chromatin immunoprecipitation (ChIP-Chip) ”
Integrative approaches for finding modular structure in biological networks (2013, background): “Another prominent group of applications relates to the integration of omics profiles with protein–DNA interaction networks for the identification of active regulatory pathways 4,81,90 .”
Structure and evolution of transcriptional regulatory networks. (2004, neutral): “2006) or the changes in the structure of entire regulatory network of an organism (Luscombe et al. 2004; Martinez-Antonio et al. 2008) are already being investigated.”
Current approaches to gene regulatory network modelling (2007, background): “Luscombe et al . compiled data from ChIP-on-chip experiments for yeast to construct a network of 142 transcription factors, 3420 target genes and 7074 regulatory interactions [32].”
An Atlas of Combinatorial Transcriptional Regulation in Mouse and Man (vol 140, pg 744, 2010) (2010, neutral): “Such widespread expression of TF hubs bears some similarity to previous studies of TF-DNA (transcriptional) interactions, in which the number of promoters bound by a TF was found to correlate with the number of growth conditions in which it is expressed (Luscombe et al., 2004; Zhou et al., 2008).”
Differential network biology (2012, methodology): “Someunderstanding of the dynamics of large networks has been achieved by integrating static interaction measurements with dynamic changes in gene expression or metabolic ﬂuxes (Ideker et al , 2002; Luscombe et al , 2004; Sauer, 2004; de Lichtenberg et al , 2005).”
An atlas of combinatorial transcriptional regulation in mouse and man (2010, neutral): “Such widespread expression of TF hubs bears some similarity to previous studies of TF-DNA (transcriptional) interactions, in which the number of promoters bound by a TF was found to correlate with the number of growth conditions in which it is expressed (Luscombe et al., 2004; Zhou et al., 2008).”