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Results: 1 to 20 of 176

1.

Evolutionary Conservation of Bacterial Essential Metabolic Genes across All Bacterial Culture Media.

Ish-Am O, Kristensen DM, Ruppin E.

PLoS One. 2015 Apr 20;10(4):e0123785. doi: 10.1371/journal.pone.0123785. eCollection 2015.

2.

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm.

Seaver SM, Bradbury LM, Frelin O, Zarecki R, Ruppin E, Hanson AD, Henry CS.

Front Plant Sci. 2015 Mar 10;6:142. doi: 10.3389/fpls.2015.00142. eCollection 2015.

3.

Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia.

Wagner A, Cohen N, Kelder T, Amit U, Liebman E, Steinberg DM, Radonjic M, Ruppin E.

Mol Syst Biol. 2015 Mar 3;11(1):791. doi: 10.15252/msb.20145486.

4.

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration.

Yizhak K, Le Dévédec SE, Rogkoti VM, Baenke F, de Boer VC, Frezza C, Schulze A, van de Water B, Ruppin E.

Mol Syst Biol. 2014 Nov 27;10:744. doi: 10.15252/msb.20145746.

5.

Fumarate induces redox-dependent senescence by modifying glutathione metabolism.

Zheng L, Cardaci S, Jerby L, MacKenzie ED, Sciacovelli M, Johnson TI, Gaude E, King A, Leach JD, Edrada-Ebel R, Hedley A, Morrice NA, Kalna G, Blyth K, Ruppin E, Frezza C, Gottlieb E.

Nat Commun. 2015 Jan 23;6:6001. doi: 10.1038/ncomms7001.

6.

Proteomics-based metabolic modeling reveals that fatty acid oxidation (FAO) controls endothelial cell (EC) permeability.

Patella F, Schug ZT, Persi E, Neilson LJ, Erami Z, Avanzato D, Maione F, Hernandez-Fernaud JR, Mackay G, Zheng L, Reid S, Frezza C, Giraudo E, Fiorio Pla A, Anderson K, Ruppin E, Gottlieb E, Zanivan S.

Mol Cell Proteomics. 2015 Mar;14(3):621-34. doi: 10.1074/mcp.M114.045575. Epub 2015 Jan 8.

7.

The effects of telomere shortening on cancer cells: a network model of proteomic and microRNA analysis.

Uziel O, Yosef N, Sharan R, Ruppin E, Kupiec M, Kushnir M, Beery E, Cohen-Diker T, Nordenberg J, Lahav M.

Genomics. 2015 Jan;105(1):5-16. doi: 10.1016/j.ygeno.2014.10.013. Epub 2014 Nov 13.

PMID:
25451739
8.

Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer.

Yizhak K, Gaude E, Le Dévédec S, Waldman YY, Stein GY, van de Water B, Frezza C, Ruppin E.

Elife. 2014 Nov 21;3. doi: 10.7554/eLife.03641.

9.

Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality.

Jerby-Arnon L, Pfetzer N, Waldman YY, McGarry L, James D, Shanks E, Seashore-Ludlow B, Weinstock A, Geiger T, Clemons PA, Gottlieb E, Ruppin E.

Cell. 2014 Aug 28;158(5):1199-209. doi: 10.1016/j.cell.2014.07.027.

PMID:
25171417
10.

Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease.

Stempler S, Yizhak K, Ruppin E.

PLoS One. 2014 Aug 15;9(8):e105383. doi: 10.1371/journal.pone.0105383. eCollection 2014.

11.

Glycan degradation (GlyDeR) analysis predicts mammalian gut microbiota abundance and host diet-specific adaptations.

Eilam O, Zarecki R, Oberhardt M, Ursell LK, Kupiec M, Knight R, Gophna U, Ruppin E.

MBio. 2014 Aug 12;5(4). pii: e01526-14. doi: 10.1128/mBio.01526-14.

12.

A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration.

Yizhak K, Le Dévédec SE, Rogkoti VM, Baenke F, de Boer VC, Frezza C, Schulze A, van de Water B, Ruppin E.

Mol Syst Biol. 2014 Aug 1;10:744. doi: 10.15252/msb.20134993. Erratum in: Mol Syst Biol. 2014 Nov;10(11):765.

13.

Network-level architecture and the evolutionary potential of underground metabolism.

Notebaart RA, Szappanos B, Kintses B, Pál F, Györkei Á, Bogos B, Lázár V, Spohn R, Csörgő B, Wagner A, Ruppin E, Pál C, Papp B.

Proc Natl Acad Sci U S A. 2014 Aug 12;111(32):11762-7. doi: 10.1073/pnas.1406102111. Epub 2014 Jul 28.

14.

A novel nutritional predictor links microbial fastidiousness with lowered ubiquity, growth rate, and cooperativeness.

Zarecki R, Oberhardt MA, Reshef L, Gophna U, Ruppin E.

PLoS Comput Biol. 2014 Jul 17;10(7):e1003726. doi: 10.1371/journal.pcbi.1003726. eCollection 2014 Jul.

15.

Maximal sum of metabolic exchange fluxes outperforms biomass yield as a predictor of growth rate of microorganisms.

Zarecki R, Oberhardt MA, Yizhak K, Wagner A, Shtifman Segal E, Freilich S, Henry CS, Gophna U, Ruppin E.

PLoS One. 2014 May 27;9(5):e98372. doi: 10.1371/journal.pone.0098372. eCollection 2014.

16.

p53 promotes the expression of gluconeogenesis-related genes and enhances hepatic glucose production.

Goldstein I, Yizhak K, Madar S, Goldfinger N, Ruppin E, Rotter V.

Cancer Metab. 2013 Feb 4;1(1):9. doi: 10.1186/2049-3002-1-9.

17.

Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious.

Wagner A, Zarecki R, Reshef L, Gochev C, Sorek R, Gophna U, Ruppin E.

Proc Natl Acad Sci U S A. 2013 Nov 19;110(47):19166-71. doi: 10.1073/pnas.1312361110. Epub 2013 Nov 6.

18.

Model-based identification of drug targets that revert disrupted metabolism and its application to ageing.

Yizhak K, Gabay O, Cohen H, Ruppin E.

Nat Commun. 2013;4:2632. doi: 10.1038/ncomms3632.

PMID:
24153335
19.

A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells.

Waldman YY, Geiger T, Ruppin E.

PLoS Genet. 2013;9(9):e1003806. doi: 10.1371/journal.pgen.1003806. Epub 2013 Sep 19.

20.

Environmental stresses disrupt telomere length homeostasis.

Romano GH, Harari Y, Yehuda T, Podhorzer A, Rubinstein L, Shamir R, Gottlieb A, Silberberg Y, Pe'er D, Ruppin E, Sharan R, Kupiec M.

PLoS Genet. 2013;9(9):e1003721. doi: 10.1371/journal.pgen.1003721. Epub 2013 Sep 5.

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