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Items: 1 to 20 of 186

1.

Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease.

Hyötyläinen T, Jerby L, Petäjä EM, Mattila I, Jäntti S, Auvinen P, Gastaldelli A, Yki-Järvinen H, Ruppin E, Orešič M.

Nat Commun. 2016 Feb 3;7:8994. doi: 10.1038/ncomms9994.

PMID:
26839171
2.

Systems-Wide Prediction of Enzyme Promiscuity Reveals a New Underground Alternative Route for Pyridoxal 5'-Phosphate Production in E. coli.

Oberhardt MA, Zarecki R, Reshef L, Xia F, Duran-Frigola M, Schreiber R, Henry CS, Ben-Tal N, Dwyer DJ, Gophna U, Ruppin E.

PLoS Comput Biol. 2016 Jan 28;12(1):e1004705. doi: 10.1371/journal.pcbi.1004705. eCollection 2016 Jan.

3.

Functional Alignment of Metabolic Networks.

Mazza A, Wagner A, Ruppin E, Sharan R.

J Comput Biol. 2016 Jan 13. [Epub ahead of print]

PMID:
26759932
4.

Glutamine synthetase activity fuels nucleotide biosynthesis and supports growth of glutamine-restricted glioblastoma.

Tardito S, Oudin A, Ahmed SU, Fack F, Keunen O, Zheng L, Miletic H, Sakariassen PØ, Weinstock A, Wagner A, Lindsay SL, Hock AK, Barnett SC, Ruppin E, Mørkve SH, Lund-Johansen M, Chalmers AJ, Bjerkvig R, Niclou SP, Gottlieb E.

Nat Cell Biol. 2015 Dec;17(12):1556-68. doi: 10.1038/ncb3272. Epub 2015 Nov 23.

PMID:
26595383
5.

Diversion of aspartate in ASS1-deficient tumours fosters de novo pyrimidine synthesis.

Rabinovich S, Adler L, Yizhak K, Sarver A, Silberman A, Agron S, Stettner N, Sun Q, Brandis A, Helbling D, Korman S, Itzkovitz S, Dimmock D, Ulitsky I, Nagamani SC, Ruppin E, Erez A.

Nature. 2015 Nov 19;527(7578):379-83. doi: 10.1038/nature15529. Epub 2015 Nov 11.

PMID:
26560030
6.

Harnessing the landscape of microbial culture media to predict new organism-media pairings.

Oberhardt MA, Zarecki R, Gronow S, Lang E, Klenk HP, Gophna U, Ruppin E.

Nat Commun. 2015 Oct 13;6:8493. doi: 10.1038/ncomms9493.

7.

Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.

Megchelenbrink W, Katzir R, Lu X, Ruppin E, Notebaart RA.

Proc Natl Acad Sci U S A. 2015 Sep 29;112(39):12217-22. doi: 10.1073/pnas.1508573112. Epub 2015 Sep 14.

PMID:
26371301
8.

The role of branched chain amino acid and tryptophan metabolism in rat's behavioral diversity: Intertwined peripheral and brain effects.

Asor E, Stempler S, Avital A, Klein E, Ruppin E, Ben-Shachar D.

Eur Neuropsychopharmacol. 2015 Oct;25(10):1695-705. doi: 10.1016/j.euroneuro.2015.07.009. Epub 2015 Jul 31.

PMID:
26271721
9.

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;11(3):791.

10.

Modeling cancer metabolism on a genome scale.

Yizhak K, Chaneton B, Gottlieb E, Ruppin E.

Mol Syst Biol. 2015 Jun 30;11(6):817. doi: 10.15252/msb.20145307. Review.

11.

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.

12.

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.

13.

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.

14.

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.

15.

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.

16.

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.

17.

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
18.

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.

19.

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.

20.

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.

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