Topological and causal structure of the yeast transcriptional regulatory network

By Nabil Guelzim, S. Bottani, Paul Bourgine, François Képès. Nature Genetics, 2002

661 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/29717bc984ade5198e0fcd6aaa5a8e8f2b331f6f

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Citations in the corpus

SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms (2006, positive): “cerevisiae [21] are used as source graphs (an overview of their main properties is given in Table 1).”
Scaling Laws in the Functional Content of Genomes (2003, background): “The experimental data on transcription regulatory networks is rather incomplete but they also suggest that the number of genes regulated per transcription factor might have power-law tails [7, 15].”
Graph theory and networks in Biology. (2006, background): “Note however that for transcriptional regulatory networks, while the outgoing degree distribution again appears to follow a power law, the incoming degree distribution is better approximated by an exponential rule of the form Pin(k) ∼ e −�k [13, 74 , 60].”
Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors (2003, background): “To examine more generally the extent to which regulatory variation resides in transcription factors, we considered a more complete list of 123 transcription factor”
Comparative Genomics in Eukaryotes (2005, background): “The evolution of scale-free networks has been explained in terms of selection on global properties such as robustness (Guelzim et al., 2002; Jeong et al., 2000) and the fast spread of perturbations (Fell and Wagner, 2000).”
Genomic analysis of regulatory network dynamics reveals large topological changes (2004, methodology): “SANDY This extends the methodology used by the TopNet software tool(27) and it evaluates each sub-network with the following: (1) standard statistics, including global measures of topology (k in, kout, l, and c) 6 and local motif occurrence (SIM, MIM and FFL)(4); (2) follow-on statistics, including ”
The Structure and Function of Complex Networks (2003, background): “The statistical structure of regulatory networks has been studied recently by various authors [152, 184, 368].”
Using graph theory to analyze biological networks (2011, background): “In E. coli and S. sereviase , the degree distribution is P ( k ) = k - g , g ≈ 2 and most of the molecules are involved into few interactions and only few of them have higher connectivity [8,165].”
Transcriptional Regulatory Networks in Saccharomyces cerevisiae (2009, background): “[15] The networks discussed above were only analyzed statically [4, 9, 18, 23, 26, 30].”
Gene networks inference using dynamic Bayesian networks (2003, methodology): “…networks: Shen-Orr et al. (2002) give 577 interactions for 116 transcription factors in E.coli using the RegulonDB database, which leads to k = 9.95, while Guelzim et al. (2002) propose a yeast transcriptional network composed of 491 genes and 909 transcriptional interactions giving k = 3.70.”
Inferring gene regulatory networks from time series data using the minimum description length principle (2006, neutral): “Guelzim et al. (2002) summarized that the number of regulating genes per regulated gene decayed exponentially whereas the number of regulated genes per regulating gene decayed in a power law and assumed a broader-support distribution.”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “Distinct cellular processes are regulated by discrete and separable modules [356].”