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Linking transcripti...
Linking transcriptional regulation and high resolution metabolic fluxes in yeast modulated by the global regulator Gcn4p
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Moxley, J.F. (author)
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Jewett, M.C. (author)
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Antoniewicz, M.R (author)
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Villas-Boas, S.G (author)
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Alper, H. (author)
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Wheeler, R.T (author)
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Tong, L. (author)
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Hinnebusch, A.G. (author)
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Ideker, T. (author)
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- Nielsen, Jens B, 1962 (author)
- Chalmers tekniska högskola,Chalmers University of Technology
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Stephanopoulos, G. (author)
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(creator_code:org_t)
- 2009-04-21
- 2009
- English.
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 106:16, s. 6477-6482
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http://dx.doi.org/10...
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Abstract
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- Genome sequencing dramatically increased our ability to understand cellular response to perturbation. Integrating system-wide measurements such as gene expression with networks of protein protein interactions and transcription factor binding revealed critical insights into cellular behavior. However, the potential of systems biology approaches is limited by difficulties in integrating metabolic measurements across the functional levels of the cell despite their being most closely linked to cellular phenotype. To address this limitation, we developed a model-based approach to correlate mRNA and metabolic flux data that combines information from both interaction network models and flux determination models. We started by quantifying 5,764 mRNAs, 54 metabolites, and 83 experimental (13)C-based reaction fluxes in continuous cultures of yeast under stress in the absence or presence of global regulator Gcn4p. Although mRNA expression alone did not directly predict metabolic response, this correlation improved through incorporating a network-based model of amino acid biosynthesis (from r = 0.07 to 0.80 for mRNA-flux agreement). The model provides evidence of general biological principles: rewiring of metabolic flux (i.e., use of different reaction pathways) by transcriptional regulation and metabolite interaction density (i.e., level of pairwise metabolite-protein interactions) as a key biosynthetic control determinant. Furthermore, this model predicted flux rewiring in studies of follow-on transcriptional regulators that were experimentally validated with additional (13)C-based flux measurements. As a first step in linking metabolic control and genetic regulatory networks, this model underscores the importance of integrating diverse data types in large-scale cellular models. We anticipate that an integrated approach focusing on metabolic measurements will facilitate construction of more realistic models of cellular regulation for understanding diseases and constructing strains for industrial applications.
Subject headings
- NATURVETENSKAP -- Biologi -- Bioinformatik och systembiologi (hsv//swe)
- NATURAL SCIENCES -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Industriell bioteknik -- Annan industriell bioteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Industrial Biotechnology -- Other Industrial Biotechnology (hsv//eng)
Keyword
- amino acid stress response
- gcn4
- systems biology
- fluxomics
Publication and Content Type
- art (subject category)
- ref (subject category)
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- By the author/editor
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Moxley, J.F.
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Jewett, M.C.
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Antoniewicz, M.R
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Villas-Boas, S.G
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Alper, H.
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Wheeler, R.T
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show more...
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Tong, L.
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Hinnebusch, A.G.
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Ideker, T.
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Nielsen, Jens B, ...
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Stephanopoulos, ...
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show less...
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- NATURAL SCIENCES
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NATURAL SCIENCES
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and Biological Scien ...
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- ENGINEERING AND TECHNOLOGY
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ENGINEERING AND ...
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and Industrial Biote ...
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and Other Industrial ...
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Chalmers University of Technology