Searching journal content for articles similar to Gutiérrez-Ríos et al. 13 (11): 2435.

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  1. LETTER: Integrated analysis of regulatory and metabolic networks reveals novel regulatory mechanisms in Saccharomyces cerevisiae
    • Markus J. Herrgård,
    • Baek-Seok Lee,
    • Vasiliy Portnoy,
    • and Bernhard Ø. Palsson
    Genome Res. May 2006 16: 627-635; Published in Advance April 10, 2006, doi:10.1101/gr.4083206
    ...can be identified to iteratively improve the model. In the case of metabolic networks, the network reconstruction step can now be routinely done and has been accomplished for a number of key model organisms including Escherichia coli ( Reed et al. 2003 ) and Saccharomyces cerevisiae ( Famili et al...
  2. LETTER: Reconciling Gene Expression Data With Known Genome-Scale Regulatory Network Structures
    • Markus J. Herrgård,
    • Markus W. Covert,
    • and Bernhard ø. Palsson
    Genome Res. November 2003 13: 2423-2434; Published in Advance January 1, 2003, doi:10.1101/gr.1330003
    ...based on annotated genome information, well-curated databases, and primary research literature. As a first step toward reconciling the two approaches, we examine the consistency between known genome-wide regulatory network structures and extensive gene expression data collections in Escherichia coli...
  3. Method: Linking the signaling cascades and dynamic regulatory networks controlling stress responses
    • Anthony Gitter,
    • Miri Carmi,
    • Naama Barkai,
    • and Ziv Bar-Joseph
    Genome Res. February 2013 23: 365-376; Published in Advance October 11, 2012, doi:10.1101/gr.138628.112
    ...(the time of the splits) with the static protein–DNA interaction data. DREM was successfully applied to reconstruct networks in a large number of species, including yeast (Ernst et al. 2007), Escherichia coli (Ernst et al. 2008), the fly (The modENCODE Consortium et al. 2010), and humans (Gu et al...
  4. Method: Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks
    • Daniel Marbach,
    • Sushmita Roy,
    • Ferhat Ay,
    • Patrick E. Meyer,
    • Rogerio Candeias,
    • Tamer Kahveci,
    • Christopher A. Bristow,
    • and Manolis Kellis
    Genome Res. July 2012 22: 1334-1349; Published in Advance March 28, 2012, doi:10.1101/gr.127191.111
    ...if these relationships may be indirect. The reconstruction of high-quality, -scale regulatory networks requires integration of both physical and functional evidence. Substantial progress has been made in the assembly and analysis of regulatory networks in unicellular organisms such as Escherichia coli and Saccharomyces...
  5. ARTICLE: A Motif Co-Occurrence Approach for Genome-Wide Prediction of Transcription-Factor-Binding Sites in Escherichia coli
    • Martha L. Bulyk,
    • Abigail M. McGuire,
    • Nobuhisa Masuda,
    • and George M. Church
    Genome Res. February 2004 14: 201-208; doi:10.1101/gr.1448004
    ...-regulatory DNA elements in prokaryotic genomes. We describe a novel method for predicting transcription-factor-binding sites in Escherichia coli . Our method takes advantage of the principle that transcription factors frequently coregulate gene expression, but without requiring prior knowledge of which...
  6. Methods: Refinement and expansion of signaling pathways: The osmotic response network in yeast
    • Irit Gat-Viks and
    • Ron Shamir
    Genome Res. March 2007 17: 358-367; Published in Advance January 31, 2007, doi:10.1101/gr.5750507
    ...existing qualitative knowledge as a probabilistic model that depicts known interactions between molecules (genes, proteins, etc.) as a network and known regulatory relations as logics. We present algorithms that analyze experimental results (e.g., transcription profiles) vis-à-vis the model and propose...
  7. RESOURCE: CADLIVE dynamic simulator: Direct link of biochemical networks to dynamic models
    • Hiroyuki Kurata,
    • Kouichi Masaki,
    • Yoshiyuki Sumida,
    • and Rei Iwasaki
    Genome Res. April 2005 15: 590-600; doi:10.1101/gr.3463705
    .... In order to demonstrate the feasibility of the CADLIVE Simulator, we analyzed the Escherichia coli nitrogen-assimilation system that consists of multiple and complicated negative and positive feedback loops, where the activity and synthesis of glutamine synthetase is regulated to adapt to the great changes...
  8. Methods: Making connections between novel transcription factors and their DNA motifs
    • Kai Tan,
    • Lee Ann McCue,
    • and Gary D. Stormo
    Genome Res. February 2005 15: 312-320; Published in Advance January 14, 2005, doi:10.1101/gr.3069205
    .... Received August 5, 2004. Cold Spring Harbor Laboratory Press References ↵ Babu, M.M. and Teichmann, S.A. 2003 . Evolution of transcription factors and the gene regulatory network in Escherichia coli. Nucleic Acids Res. 31 : 1234 -1244. ↵ Bar-Joseph, Z., Gerber, G.K., Lee, T.I., Rinaldi, N.J., Yoo, J...
  9. Review: A systematic guide for identifying transcription factors that directly regulate the expression of a gene of interest
    • Andrew D. Bates,
    • Dawid Grzela,
    • Maciej Studzian,
    • Louise Brennan,
    • Moli Williams,
    • Conor Fawcett,
    • Beth Hammond,
    • Manreen Grewal,
    • Marcin Ratajewski,
    • Lukasz Pulaski,
    • and Urszula L. McClurg
    Genome Res. March 2026 36: 433-459; Published in Advance February 17, 2026, doi:10.1101/gr.281154.125
    ...vector machines have given way to neural networks, such as convolutional and recurrent models, which power tools like DeepBIND (Alipanahi et al. 2015) and DeepTF (Bao et al. 2019). Although user-friendly classical tools remain popular, newer models like DeepReg (Ledesma-Dominguez et al. 2024) and Deep...
    OPEN ACCESS ARTICLE
  10. Method: Mapping functional transcription factor networks from gene expression data
    • Brian C. Haynes,
    • Ezekiel J. Maier,
    • Michael H. Kramer,
    • Patricia I. Wang,
    • Holly Brown,
    • and Michael R. Brent
    Genome Res. August 2013 23: 1319-1328; Published in Advance May 1, 2013, doi:10.1101/gr.150904.112
    ...profiles. Algorithms that exploit DE typically construct networks with regulatory edges from each TF to all the genes that are differentially expressed when the TF is deleted (Hu et al. 2007; Pinna et al. 2010), overexpressed, or otherwise perturbed. Some edges in this network reflect direct binding...
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