Revealing modular organization in the yeast transcriptional network

Ihmels, Jan; Friedlander, Gilgi; Bergmann, Sven; Sarig, Ofer; Ziv, Yaniv; Barkai, Naama

doi:10.1038/ng941

Cited by 653 publications

(596 citation statements)

References 15 publications

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“…We collected a data set of more than 1500 transcription profiles (Ihmels et al 2002) describing the expression changes of all yeast genes upon a variety of conditions (environmental stresses, mutations, or developmental transitions). Using these data, we cal-culated for each gene the average magnitude by which its expression was modulated across all conditions, or a subset of them (see Methods).…”

Section: Resultsmentioning

confidence: 99%

Two strategies for gene regulation by promoter nucleosomes

Tirosh

Barkai²

2008

Genome Res.

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Chromatin structure is central for the regulation of gene expression, but its genome-wide organization is only beginning to be understood. Here, we examine the connection between patterns of nucleosome occupancy and the capacity to modulate gene expression upon changing conditions, i.e., transcriptional plasticity. By analyzing genome-wide data of nucleosome positioning in yeast, we find that the presence of nucleosomes close to the transcription start site is associated with high transcriptional plasticity, while nucleosomes at more distant upstream positions are negatively correlated with transcriptional plasticity. Based on this, we identify two typical promoter structures associated with low or high plasticity, respectively. The first class is characterized by a relatively large nucleosome-free region close to the start site coupled with well-positioned nucleosomes further upstream, whereas the second class displays a more evenly distributed and dynamic nucleosome positioning, with high occupancy close to the start site. The two classes are further distinguished by multiple promoter features, including histone turnover, binding site locations, H2A.Z occupancy, expression noise, and expression diversity. Analysis of nucleosome positioning in human promoters reproduces the main observations. Our results suggest two distinct strategies for gene regulation by chromatin, which are selectively employed by different genes.

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Section: Resultsmentioning

confidence: 99%

Two strategies for gene regulation by promoter nucleosomes

Tirosh

Barkai²

2008

Genome Res.

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364

481

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“…Assuming that the edges of the metabolic network contribute to more or less the same degree to the dynamics of the chemical system, these network modules should be subsystems (of the dynamic reaction system) with some degree of autonomy. This is close to the general idea of biological modularity-a biological module is commonly defined as a subsystem performing some specific, rather well-defined, biological function (Ihmels et al 2002;Han et al 2004;Kitano 2004;Del Vecchio et al 2008). One can, of course, think of modules of non-biological reaction systems as well.…”

Section: Introductionmentioning

confidence: 99%

Model validation of simple-graph representations of metabolism

Holme

2009

J. R. Soc. Interface.

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The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure-network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate-product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules.

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“…In many organisms, in-depth transcriptome analysis has revealed the modular architecture of gene expression [22]. A regulatory module is a self-consistent regulatory unit R ( TF , G , I ) representing a set of co-expressed genes G = { g 1 , g 2 , ..., g n } regulated in concert by a group of TFs in TF = { tf 1 , tf 2 , ..., tf m } that govern the target genes' behaviors via regulatory interaction I [5].…”

Section: Methodsmentioning

confidence: 99%

Inferring transcription factor collaborations in gene regulatory networks

Awad

Chen

2014

BMC Systems Biology

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BackgroundLiving cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the expression of target genes is a challenging task, especially when multiple TFs collaboratively participate in the transcriptional regulation.ResultsWe model the underlying regulatory interactions in terms of the directions (activation or repression) and their logical roles (necessary and/or sufficient) with a modified association rule mining approach, called mTRIM. The experiment on Yeast discovered 670 regulatory interactions, in which multiple TFs express their functions on common target genes collaboratively. The evaluation on yeast genetic interactions, TF knockouts and a synthetic dataset shows that our algorithm is significantly better than the existing ones.ConclusionsmTRIM is a novel method to infer TF collaborations in transcriptional regulation networks. mTRIM is available at http://www.msu.edu/~jinchen/mTRIM.

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Revealing modular organization in the yeast transcriptional network

Cited by 653 publications

References 15 publications

Two strategies for gene regulation by promoter nucleosomes

Two strategies for gene regulation by promoter nucleosomes

Model validation of simple-graph representations of metabolism

Inferring transcription factor collaborations in gene regulatory networks

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