TITLE

Metabolic Constraint-Based Refinement of Transcriptional Regulatory Networks

AUTHOR(S)
Chandrasekaran, Sriram; Price, Nathan D.
PUB. DATE
December 2013
SOURCE
PLoS Computational Biology;Dec2013, Vol. 9 Issue 12, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
There is a strong need for computational frameworks that integrate different biological processes and data-types to unravel cellular regulation. Current efforts to reconstruct transcriptional regulatory networks (TRNs) focus primarily on proximal data such as gene co-expression and transcription factor (TF) binding. While such approaches enable rapid reconstruction of TRNs, the overwhelming combinatorics of possible networks limits identification of mechanistic regulatory interactions. Utilizing growth phenotypes and systems-level constraints to inform regulatory network reconstruction is an unmet challenge. We present our approach Gene Expression and Metabolism Integrated for Network Inference (GEMINI) that links a compendium of candidate regulatory interactions with the metabolic network to predict their systems-level effect on growth phenotypes. We then compare predictions with experimental phenotype data to select phenotype-consistent regulatory interactions. GEMINI makes use of the observation that only a small fraction of regulatory network states are compatible with a viable metabolic network, and outputs a regulatory network that is simultaneously consistent with the input genome-scale metabolic network model, gene expression data, and TF knockout phenotypes. GEMINI preferentially recalls gold-standard interactions (p-value = 10−172), significantly better than using gene expression alone. We applied GEMINI to create an integrated metabolic-regulatory network model for Saccharomyces cerevisiae involving 25,000 regulatory interactions controlling 1597 metabolic reactions. The model quantitatively predicts TF knockout phenotypes in new conditions (p-value = 10−14) and revealed potential condition-specific regulatory mechanisms. Our results suggest that a metabolic constraint-based approach can be successfully used to help reconstruct TRNs from high-throughput data, and highlights the potential of using a biochemically-detailed mechanistic framework to integrate and reconcile inconsistencies across different data-types. The algorithm and associated data are available at https://sourceforge.net/projects/gemini-data/
ACCESSION #
93395149

 

Related Articles

  • Two yeast forkhead genes regulate the cell cycle and pseudohyphal growth. Gefeng Zhu; Spellman, Paul T.; Volpe, Tom; Brown, Patrick O.; Botstein, David; Davis, Trisha N.; Futcher, Bruce // Nature;7/6/2000, Vol. 406 Issue 6791, p90 

    Finds that the genes in the CLB2 cluster in Saccharomyces cerevisiae lose their cell cycle regulation in a mutant that lacks two forkhead transcription factors, Fkh1 and Fkh2. Methods; Results, which indicate that Fkh proteins are transcription factors for the CLB2 cluster; Conclusions.

  • A novel TBP-TAF complex on RNA polymerase II-transcribed snRNA genes. Zaborowska, Justyna; Taylor, Alice; Roeder, Robert G.; Murphy, Shona // Transcription (2154-1264);2012, Vol. 3 Issue 2, p92 

    Initiation of transcription of most human genes transcribed by RNA polymerase II (RNAP II) requires the formation of a preinitiation complex comprising TFIIA, B, D, E, F, H and RNAP II. The general transcription factor TFIID is composed of the TATA-binding protein and up to 13 TBP-associated...

  • Disruption of Rpn4-Induced Proteasome Expression in Saccharomyces cerevisiae Reduces Cell Viability Under Stressed Conditions. Xiaogang Wang; Haiming Xu; Donghong Ju; Youming Xie // Genetics;Dec2008, Vol. 180 Issue 4, p1945 

    The proteasome homeostasis in Saccharomyces cerevisiae is regulated by a negative feedback circuit in which the transcription activator Rpn4 upregulates the proteasome genes and is rapidly degraded by the assembled proteasome. Previous studies have shown that rpn4Δ cells are sensitive to a...

  • Under-Dominance Constrains the Evolution of Negative Autoregulation in Diploids. Stewart, Alexander J.; Seymour, Robert M.; Pomiankowski, Andrew; Reuter, Max // PLoS Computational Biology;Mar2013, Vol. 9 Issue 3, p1 

    Regulatory networks have evolved to allow gene expression to rapidly track changes in the environment as well as to buffer perturbations and maintain cellular homeostasis in the absence of change. Theoretical work and empirical investigation in Escherichia coli have shown that negative...

  • Zinc-Regulated DNA Binding of the Yeast Zap1 Zinc- Responsive Activator. Frey, Avery G.; Bird, Amanda J.; Evans-Galea, Marguerite V.; Blankman, Elizabeth; Winge, Dennis R.; Eide, David J. // PLoS ONE;2011, Vol. 6 Issue 7, p1 

    The Zap1 transcription factor of Saccharomyces cerevisiae plays a central role in zinc homeostasis by controlling the expression of genes involved in zinc metabolism. Zap1 is active in zinc-limited cells and repressed in replete cells. At the transcriptional level, Zap1 controls its own...

  • Network analysis of transcriptional regulation in response to intramuscular interferon-β-1a multiple sclerosis treatment. Hecker, M; Goertsches, R H; Fatum, C; Koczan, D; Thiesen, H-J; Guthke, R; Zettl, U K // Pharmacogenomics Journal;Aug2012, Vol. 12 Issue 4, p360 

    No abstract available.

  • Context-specific transcriptional regulatory network inference from global gene expression maps using double two-way t-tests. Qi, Jianlong; Michoel, Tom // Bioinformatics;Sep2012, Vol. 28 Issue 18, p2325 

    Motivation: Transcriptional regulatory network inference methods have been studied for years. Most of them rely on complex mathematical and algorithmic concepts, making them hard to adapt, re-implement or integrate with other methods. To address this problem, we introduce a novel method based on...

  • NetBenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference. Bellot, Pau; Olsen, Catharina; Salembier, Philippe; Oliveras-Vergés, Albert; Meyer, Patrick E. // BMC Bioinformatics;9/29/2015, Vol. 16 Issue 1, p1 

    Background: In the last decade, a great number of methods for reconstructing gene regulatory networks from expression data have been proposed. However, very few tools and datasets allow to evaluate accurately and reproducibly those methods. Hence, we propose here a new tool, able to perform a...

  • Identification of Vascular and Hematopoietic Genes Downstream of etsrp by Deep Sequencing in Zebrafish. Gomez, Gustavo; Lee, Jae-Hyung; Veldman, Matthew B.; Lu, Jing; Xiao, Xinshu; Lin, Shuo // PLoS ONE;Mar2012, Vol. 7 Issue 3, p1 

    The transcription factor etsrp/Er71/Etv2 is a master control gene for vasculogenesis in all species studied to date. It is also required for hematopoiesis in zebrafish and mice. Several novel genes expressed in vasculature have been identified through transcriptional profiling of zebrafish...

Share

Read the Article

Courtesy of VIRGINIA BEACH PUBLIC LIBRARY AND SYSTEM

Sorry, but this item is not currently available from your library.

Try another library?
Sign out of this library

Other Topics