Eytan Ruppin - Once          And today...  

My Lab (Make lab no war..): The people, research and downloads

Our Metabolic Modeling Forum & our Joint Computational Systems Biology Seminar

Papers

Selected Publications

  • Metabolic network prediction of drug side effects
    (I. Shaked, M.A. Oberhardt, N. Atias, R. Sharan, E. Ruppin).
    Cell Systems, 2, 209-213, 2016.
  • Genome-scale study reveals reduced metabolic adaptability in patients with non-alchoholic fatty liver disease.
    (T. Hyotylanen, L. Jerby, E. Petaja, I. Mattila, S. Jantti, P. Auvinen, A. Gastaldelli, i H. Yki-Jarvinen, E. Ruppin, M. Oresic).
    Nature Communications, 7, 8994, 2016.
  • Diversion of aspartate in ASS1-deficient tumors fosters de novo pyrimidine synthesis
    (S. Rabinovich,L. Adler, K. Yizhak, A. Sarver, A. Silberman, S. Agron, N. Stettner, Q. Sun, A. Brandis, D. Heibling, S. Korman, S. Itzkovitz, D. Dimmock, I. Ulitsky, S. CS. Nagamani, E. Ruppin, A. Erez)
    Nature, 527, 379-383, 2015.
  • Harnessing the landscape of microbial culture media to predict new organism-media pairings
    (M.A. Oberhardt*1, R. Zarecki*1, S. Gronow, E. Lang, H.P. Klenk, U. Gophna*2, E. Ruppin*2) *1 - equal controbuting 1st author; *2 - equal contributing last author.
    Nature Communications, 6, 8493, 2015.
  • Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival
    (W. Megchelenbrink*1, R. Katzir*1, Xiaowen Lu, E. Ruppin*2, R.A. Notebaart*2), *1 - equal controbuting 1st author; *2 - equal contributing last author.
    Proceedings of the National Academy of Sciences (PNAS), 112, 39, 12217-12222, 2015.
  • Modeling cancer metabolism on a genome scale
    (K. Yizhak, B. Chaneton, E. Gottlieb, E. Ruppin),
    Molecular Systems Biology (MSB), 11, 817, 2015.
  • Phenotype-based cell specific modeling reveals metabolic liabilities of cancer
    (K. Yizhak*, E. Guade*, S. E. Devedec, Y.Y. Waldman, G.Y. Stein, B. van de Water#, C. Frezza#,E. Ruppin#), (first author equal contribution *, last author equal contribution #).
    eLife, 3, e03641, DOI: 10.7554/eLife.03641, 2014.
  • A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration
    (K. Yizhak*, S. E. Devedec*, V.M. Rogkoti, F. Baenke, V.C. de Boer, C. Frezza, A. Schulze, B. van de Water#, E. Ruppin#), (first author equal contribution *, last author equal contribution #).
    Molecular Systems Biology (MSB), 10:744, 2014.
  • Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality
    (L. Jerby-Arnon, N. Pfetzer, Y.Y. Yaldman, L. McGarry, D. James, E. Shanks, B. Seashore-Ludlow, A. Weinstock, T. Geiger, P.A. Clemons, E. Gottlieb, E. Ruppin)
    Cell, 158, 1199-1209, 2014.
    See also DAISY: Picking synthetic lethal pairs from cancer genomes, (C.J Ryan, C.J. Lord, A. Ashworth) Cancer Cell, 26, 306-308, 2014, and
    Nature Reviews Drug Discovery, November 2014.
  • Haem oxygenase is synthetically lethal with the mitochondrial tumour suppressor fumarate hydratase
    (C. Frezza, L. Zheng, O. Folger, K. Rajagopalan, E.D. MacKenzie, L. Jerby, M. Micaroni, B. Chaneton, J. Adam, A. Hedley, G. Kalna, I.P.M. Tomlinson, P.J. Pollard, D.G. Watson, R.J. Deberardinis, T. Shlomi*, E. Ruppin*, E. Gottlieb)
    Nature, 17 Aug 2011 (doi:10.10.1038/nature10363). *equal contribution, highlighted in
    Haem is where the heart is, by N. McCarthy, Nature Reviews Cancer, October 2011.
  • Predicting selective drug targets in cancer through metabolic networks
    (O. Folger, L. Jerby, C. Frezza, E. Gottlieb, E. Ruppin*, T. Shlomi*)
    Molecular Systems Biology (MSB), doi:10.1038/msb.2011.35, 2011. *equal contribution, highlighted in
    Lethal weakness , by N. McCarthy, Nature Reviews Cancer, August 2011,
    and in , What is your top 2011 MSB paper? by Andrew Hufton
  • Network based prediction of human tissue specific metabolism
    (T. Shlomi, M.N Cabili, M.J. Herrgard, B.O. Palsson, E. Ruppin)
    Nature Biotechnology, doi: 10.1038/nbt.1487, August 2008.
  • Multiple knockouts analysis of genetic robustness in the yeast metabolic metwork
    (D. Deutscher, I. Meilijson, M. Kupiec, E. Ruppin)
    Nature Genetics, 38(9), 993-998, 2006.
  • Evolutionary AutonomousAgents: A Neuroscience Perspective
    (E. Ruppin)
    Nature Reviews Neuroscience, 3(2), (2002), 132-141.

    • Recent Publications

    2016 --

  • Essential genes embody increased mutational robusntess to compensate for the lack of backup genetic reduandancy
    (O. Cohen, M. Oberhardt, K. Yizhak, E. Ruppin) ).
    PLoS One, to appear.
  • Data driven metabolic pathway compositions enhance cancer survival prediction
    (N. Auslander, A. Wagner, M. Oberhardt, E. Ruppin) ).
    PLoS Computational Biology, 12(9), e1005125.doi:10.1371/journal.pcbi.1005125 (2016).
  • Therapeutic relevance of protein phosphatase 2A in cancer.
    (C.E. Cunningham,...,J.S. Lee,...,E.Ruppin,...,F.J. Vizecoumar) ).
    Oncotarget, doi: 10.18632/oncotarget.11399 (2016).
  • A joint analysis of transcriptomic and metabolic data uncovers enhances enzyme-metabolite coupling in breast cancer
    (N. Auslander, K. Yizhak, A. Weinstock, A. Budhu, W. Tang, X.W. Wang, S. Ambs, E. Ruppin).
    Scientific Reports, 6, Article number: 29662, doi:10.1038/srep29662, July 2016.
  • The role of temporal trends in growing networks
    (O. Mokyrn, A. Wagner, M. Blattner, E. Ruppin, Y. Shavitt).
    PLoS One, 11(8), e0156505, doi:10.1371/journal.pone.0156505, August, 2016.
  • Metabolic network prediction of drug side effects
    (I. Shaked, M.A. Oberhardt, N. Atias, R. Sharan, E. Ruppin).
    Cell Systems, 2, 209-213, March 2016.
  • System-wide clinical proteomics of breast cancer reveals global remodeling of tissue homeostasis
    (Y. Pozniak, N. Lahat, J.D Rudolph, C. Lindskog, R. Katzir, C. Avivi, F. Ponten, E. Ruppin, I. Barchack, T. Geiger).
    Cell Systems, 2, 172-184, 2016.
  • Systems-wide prediction of enzyme promiscuity reveal a new undergound alternative route for pyridoxal 5'-phophate in E. coli
    (M.A. Oberhardt, R. Zarecky, L. Reshef, F. Xia, M. Duran-Frigola, R. Schreiber, C.S. Henry, N. Ben-Tal, D.J. Dwyer, U. Gophna, E. Ruppin).
    PLoS Computational Biology, Jan 28, 2016.
  • Genome-scale study reveals reduced metabolic adaptability in patients with non-alchoholic fatty liver disease.
    (T. Hyotylanen, L. Jerby, E. Petaja, I. Mattila, S. Jantti, P. Auvinen, A. Gastaldelli, i H. Yki-Jarvinen, E. Ruppin, M. Oresic).
    Nature Communications, 7, 8994, 2016.

    • 2015 --

  • Functional alignment of metabolic networks
    (A. Mazza, A. Wagner, E. Ruppin, R. Sharan).
    Journal of Computational Biology, in press.
  • Astrocytic glutamine synthetase fulfill glutamine requirement of glioblastoma multiforme.
    (S. Tardito, A. Oudin, S.U. Ahmed, F. Fack, O. Keunen, L. Zheng, H. Militec, P.O. Sakariassen, A. Weinstock, A. Wagner, S.L. Lindsay, A.K. Hock, S.C. Barnett, E. Ruppin, S.H. Morkve, M. Lund-Johansen, A.J. Chalmers, R. Bjerkvig, S.P. Niclou, E. Gottlieb).
    Nature Cell Biology, 17, 12, 1556-1570, 2015.
  • Diversion of aspartate in ASS1-deficient tumors fosters de novo pyrimidine synthesis
    (S. Rabinovich,L. Adler, K. Yizhak, A. Sarver, A. Silberman, S. Agron, N. Stettner, Q. Sun, A. Brandis, D. Heibling, S. Korman, S. Itzkovitz, D. Dimmock, I. Ulitsky, S. CS. Nagamani, E. Ruppin, A. Erez)
    Nature, 527, 379-383, 2015.
  • Harnessing the landscape of microbial culture media to predict new organism-media pairings
    (M.A. Oberhardt*1, R. Zarecki*1, S. Gronow, E. Lang, H.P. Klenk, U. Gophna*2, E. Ruppin*2) *1 - equal controbuting 1st author; *2 - equal contributing last author.
    Nature Communications, 6, 8493, 2015.
  • Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival
    (W. Megchelenbrink*1, R. Katzir*1, Xiaowen Lu, E. Ruppin*2, R.A. Notebaart*2), *1 - equal controbuting 1st author; *2 - equal contributing last author.
    Proceedings of the National Academy of Sciences (PNAS), 112, 39, 12217-12222, 2015.
  • The role of branched amino acid and tryptophan metabolism in rat's behavioral diversity: Intertwined peripheral and brain effects
    (E. Asor, S. Stempler, A. Avital, E. Klein, E. Ruppin, D. Ben-Shachar),
    European Neuropsychophramacology, In press.
  • Modeling cancer metabolism on a genome scale
    (K. Yizhak, B. Chaneton, E. Gottlieb, E. Ruppin),
    Molecular Systems Biology (MSB), 11,817, 2015.
  • Evolutionary conservation of bacterial metabolic genes across all bacterial culture media
    (O. Ish-Am, D. Kristensen, E. Ruppin)
    PLoS One, to appear, 2015.
  • Improved evidence-based genome scale metabolic models for maize leaf, embryo and endosperm
    (S. Seaver, L. Bradbury, O. Ferlin, R. Zarecky, E. Ruppin, A.D. Hanson, C.S. Henry),
    Frontiers in Plant Science, to appear, 2015.
  • Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia
    (A. Wagner, N. Cohen, T. Kelder, U. Amit, E. Liebman, D.M. Steinberg, M. Radonjic, E. Ruppin),
    Molecular Systems Biology (MSB), 11, 791, 2015.
  • Proteomics-based metabolic modelling reveals that fatty acid oxidation controls endothelial cell permeability
    (F. Pattela, Z. Schug,E. Persi, L.J Neilson, Z. Erami,..., E. Ruppin, E. Gottlieb, S. Zanivan).
    Molecular & Cellular Proteomics, Jan 8, 2015.
  • The effects of telomere shortening on cancer cells: A network model of proteomic and microRNA analysis
    (O. Uziel, N. Yosef, R. Sharan, E. Ruppin, M. Kupiec, M. Kushnir, E. Beery, T. Cohen-Diker, J. Nordenberg, M. Lahav ).
    Genomics, 105, 5-16, 2015.

    • 2014 --

  • Fumarate induces redox-dependent senescenceaby modifying glutathione metabolism
    (L. Zheng, S. Cardaci, L. Jerby, E.D. MacKenzie, M. Sciacovelli, T. Isaac Johnson, E. Gaude, A. King, J.D.G. Leach, R. Edrada-Ebel, A. Hedly, N.A. Morrice, G. Kalna, K. Blyth, E. Ruppin, C. Frezza, E. Gottlieb. ).
    Nature Communications, to appear.
  • Phenotype-based cell specific modeling reveals metabolic liabilities of cancer
    (K. Yizhak*, E. Guade*, S. E. Devedec, Y.Y. Waldman, G.Y. Stein, B. van de Water#, C. Frezza#,E. Ruppin#), (first author equal contribution *, last author equal contribution #).
    eLife, 3, e03641, DOI: 10.7554/eLife.03641, 2014.
  • Moving ahead on harnessing synthetic lethality to fight cancer (Authors view)
    (L. Jerby-Arnon, E. Ruppin) Molecular & Cellular Oncology, to appear.
  • Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease
    (S. Stempler, K. Yizhak, E. Ruppin)
    PLoS One, 9(8); e105383. doi:10.1371/journal.pone.0105383, 2014.
  • Network-level architecture and the evolutionary potential of underground metabolism
    (R.A. Notebaart, B. Szappanos, B. Kinteses, F. Pal, A. Gyorkei, B. Bogos, V. Lazar, R. Spohn, A. Wagner, E. Ruppin, C. Pal, B. Papp)
    Proceedings of the National Academy of Sciences (PNAS), 111(32), August 2014.
  • A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration
    (K. Yizhak*, S. E. Devedec*, V.M. Rogkoti, F. Baenke, V.C. de Boer, C. Frezza, A. Schulze, B. van de Water#, E. Ruppin#) (first author equal contribution *, last author equal contribution #).
    Molecular Systems Biology (MSB), 10:744, 2014.
  • Glycan Degradation (GlyDer) analysis predicts mammalian gut microbiota abundance and host diet-specific adaptations
    (O. Eilam, R. Zarecky, M. Oberhardt, L.K. Ursell, M. Kupiec, R. Knight, U. Gophna, E. Ruppin)
    mBio, 5(4); doi:10.1128/mBio.01526-14, 2014.
  • Predicting cancer-specific vulnerability via data-driven detection of synthetic lethality
    (L. Jerby-Arnon, N. Pfetzer, Y.Y. Yaldman, L. McGarry, D. James, E. Shanks, B. Seashore-Ludlow, A. Weinstock, T. Geiger, P.A. Clemons, E. Gottlieb, E. Ruppin)
    Cell, 158, 1199-1209, 2014.
  • A novel nutritional predictor links microbial fastidiousness wth lowered ubiquity, growth rate and cooperativeness
    (R. Zarecki, M. Oberhardt, L. Reshef, U. Gophna, E. Ruppin)
    PLoS Computational Biology, 10(7): e1003726, doi:10.1371/journal.pcbi.1003726, 2014.
  • Maximal sum of metabolic exchange fluxes outperforms biomass yield as a predictor of growth rate of microorganisms
    (R. Zarecki, M. Oberhardt, K. Yizhak, A. Wagner, E. Shiftman Segal, C.S. Henry, U. Gophna, E. Ruppin)
    PLoS One, 9(5): e98372, doi:10.1371/journalpone.0098372, 2014.

    • 2013 --

  • Computational evaluation of cellular metabolic costs successfully predicts genes whose expression is deleterious
    (A. Wagner, R. Zarecki, L. Reshef, C. Gochev, R. Sorek, U. Gophna, E. Ruppin)
    Proceedings of the National Academy of Sciences (PNAS), www.pnas.org/cgi/doi/10.1073/pnas.1312361110, 2013.
  • Large models differ from small ones by much more than size (Opinion article)
    (M. Oberhardt, E. Ruppin)
    EMBO Reports, doi:10.1038/embor.2013.145, 2013.
  • Model-based identification of drug targets that revert disrupted metabolism and its application to aging
    (K. Yizhak, O. Gabay, H. Cohen, E. Ruppin)
    Nature Communications, 4:2632, DOI: 10.1038/ncomms3632, 2013.
  • A genome-wide systematic analysis reveals different and predictive proliferation expression signatures of cancerous vs. non-cancerous cells
    (Y. Waldman, T. Geiger, E. Ruppin)
    PLoS Genetics, 9(9): e1003806. doi:10.1371/journal.pgen.1003806, 2013.
  • A method for inferring medical diagnoses from patients similarities
    (A. Gottlieb, G.Y. Stein, E. Ruppin, R.B. Altman, R. Sharan)
    BMC Medicine, 11:194, 2013.
  • Environmental stresses disrupt telomere length homoestasis
    (G. Romano, Y. Harari, T. Yehuda, A. Podhorzer, L. Rubinstein, R. Shamir, A. Gottlieb, Y. Silberberg, D. Pe'er, E. Ruppin, R. Sharan, M. Kupiec)
    PLoS Genetics, 9(9), e1003721, doi:10.1371/journal/pgen.1003721, 2013.
  • Metabolically re-Modeling the drug pipeline
    (M. Oberhardt*, K. Yizhak*, E. Ruppin) *equal contribution
    Curr. Opin. in Pharmacology, 13, 778-785, http://dx.doi.org/10.1016/j.coph.2013.05.006, 2013
  • p53 promotes the expression of gluconeogenesis-related genes and enhances hepatic glucose production
    (I. Goldstein, K. Yizhak, S. Madar, N. Goldfinger, E. Ruppin, V. Rotter)
    Cancer & Metabolism, 1:9, doi:10.1186/2049-3002-1-9, February 2013.
  • Common and specific signatures of gene expression and protein-protein interactions in autoimmune diseases
    (T. Tuller, S. Atar, E. Ruppin, M. Gurevich, A. Achiron)
    Genes and immunity, 14(2), 67-82, DOI: 10.1038/gene.2012.55, March 2013.

    • 2012 --

  • Metabolic associations of reduced proliferation and oxidative stress in advanced breast cancer
    (L. Jerby, L. Wolf, C. Denkert, G.Y. Stein, M. Hilvo, M. Oresic, T. Geiger*, E. Ruppin*) *equal contribution
    Cancer Research, 72(22); 1-9, September 2012.
  • Predicting drug-targets and biomarkers of cancer via genome-scale metabolic modeling
    (L. Jerby & E. Ruppin)
    Clinical Cancer Research, 18, 5572-5584, October, 2012.
  • Met kinetic signature derived from the response to HGF/SF in a cellular model predicts breast cancer patient survivial
    (G.Y. Stein, N. Yosef, H. Raichman, J. Horev, A. Laser-Azougi, A. Berens, J. Resau, E. Ruppin, R. Sharan, I. Tsarfaty)
    PLoS One, 7(9): e45969, doi:10.1371/journal.pone.0045969, 2012.
  • Analyzing gene expression from whole tissue vs. different cell types reveals the central role of neurons in predicting severity of alzhiemer's disease
    (S. Stempler & E. Ruppin)
    PLoS One, 7(9), e45879, doi:10.1371/journal.pone.0045879, September 2012.
  • Enhancing the prioritization of disease-causing genes through tissue specific protein interaction networks
    (O. Magger, Y. Waldman, E. Ruppin, R. Sharan)
    PLoS Computational Biology, 8(9): e1002690, doi:10.1371/journal.pcbi.1002690, 2012.
  • Integrative genomic analysis identifies Isoleucine and CodY as regulators of Listeria monocytogenes virulence
    (L. Lobel, N. Sigal, I. Borovok, E. Ruppin, A. Herskovits)
    PLoS Genetics 8(9): e1002887, doi:10.1371/journal.pgen.1002887, 2012.
  • INDI: A novel framework for inferring drug interactions and their associated recommendations
    (A. Gottlieb, G.Y Stein, Y. Oron, E. Ruppin, R. Sharan)
    Molecular Systems Biology (MSB), 8, article number 592, doi:10.1038/msb.2012.26, 2012.
  • Hippocampus neuronal metabolic gene expression outperforms whole tissue data in accurately predicting Alzheimer's disease progression
    (S. Stempler, Y. Waldman, L. Wolf, E. Ruppin)
    Neurobiology of Aging, May, 2012.
  • Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity
    (S. Mintz-Oron, S. Meir, S. Malitsky, E. Ruppin, A. Aharoni, T. Shlomi)
    Proceedings of the National Academy of Sciences (PNAS), 109(1), 339-344, January 2012.
  • Systematic indentification of gene annotation errors in the widely used yeast mutation collections
    ( T. Ben-Shitrit*, N. Yosef*, K. Shemesh,R. Sharan*, E. Ruppin*, M. Kupiec), *equal contribution.
    Nature Methods, 9, 373-378, February 2012.
  • A linearized constraint based approach for modelling signaling networks
    (L. Vardi, E. Ruppin*, R. Sharan*), *equal contribution
    Journal of Computational Biology, 19(2), 232-240, February 2012.
  • Large scale elucidation of drug response pathways in humans
    (Y. Silberberg, A. Gottlieb, E. Ruppin, R. Sharan)
    Journal of Computational Biology, 19(2), 163-174, February 2012.

    • 2011

  • Competitive and cooperative metabolic interactions in bacterial communities
    (S. Freilich*, R. Zarecki*, O. Eilam, E. Shiftman Segal, C.S. Henry, M. Kupiec, U. Gophna*, R. Sharan*, E. Ruppin), *equal contribution.
    Nature Communications, 2,589, doi:10.1038/ncomms1597, December, 2011.
  • PRINCIPLE: A tool for associating genes with diseases via network propoagation
    (A. Gottlieb, O. Magger, I. Berman, E. Ruppin, R. Sharan)
    Bioinformatics, 27(23), 3325-3326, 2011.
  • ANAT - A software tool for reconstructing and analyzing functional networks of proteins.
    (N. Yosef, E. Zalckvar, N. Atias, A.D. Rubinstein, M. Homilus, L. Vardi, H. Zur, A. Kimchi, E. Ruppin, R. Sharan)
    Science Signaling, 4(196), pl1, 2011.
  • Composite effects of the coding sequences determinants on the speed and density of ribosomes.
    (T. Tuller, I. Veksler, N. Gazit, M. Kupiec, E. Ruppin*, M. Ziv*)
    Genome Biology, 12, 8110, 2011. *equal contribution
  • Selection for translation efficiency on synonymous polymorphisms in recent human evolution
    (Y. Waldman, T. Tuller*, A. Keinan*, E. Ruppin*) Genome Biology & Evolution, 3, 749-761, July, 2011. *equal contribution.
  • Haem oxygenase is synthetically lethal with the mitochondrial tumour suppressor fumarate hydratase
    (C. Frezza, L. Zheng, O. Folger, K. Rajagopalan, E.D. MacKenzie, L. Jerby, M. Micaroni, B. Chaneton, J. Adam, A. Hedley, G. Kalna, I.P.M. Tomlinson, P.J. Pollard, D.G. Watson, R.J. Deberardinis, T. Shlomi*, E. Ruppin*, E. Gottlieb)
    Nature, 17 Aug 2011 (doi:10.1038/nature10363). *equal contribution.
  • Global map of physical interactions among differentially expressed genes in multiple sclerosis relapses and remissions
    (T. Tuller, S. Atar, E. Ruppin, M. Gurevich, A. Achiron)
    Human Molecular Genetics, to appear, 2011.
  • Genome-scale analysis of translation elongation with a ribosomal flow model
    (S. Reuveni, I. Meilijson, M. Kupiec, E. Ruppin, T. Tuller )
    PLoS Computational Biology, to appear, 2011.
  • Predicting selective drug targets in cancer through metabolic networks
    (O. Folger, L. Jerby, C. Frezza, E. Gottlieb, E. Ruppin*, T. Shlomi*)
    Molecular Systems Biology (MSB), doi:10.1038/msb.2011.35, 2011. *equal contribution
  • PREDICT: A method for inferring novel drug indications with application to personalized medicine
    (A. Gottlieb, G.Y. Stein, E. Ruppin, R. Sharan)
    Molecular Systems Biology (MSB), 7, article number 490, doi:10.1038/msb.2011.26, 2011.
  • Metabolic modeling of endosymbiont genome reduction on a temporal scale
    (K. Yizhak, T. Tuller, B. Papp, E. Ruppin)
    Molecular Systems Biology (MSB), 7, article number 479; doi:10.1038/msb.2011.11, 2011.
  • Gene expression in the rodent brain is associated with its regional connectivity
    (L. Wolf, C. Goldberg, N. Manor, R. Sharan, E. Ruppin)
    PLoS Computational Biology, 7(5), e1002040, 2011.
  • Genome-scale metabolic modeling elucidates the role of proliferative adaptation in causing the Warburg effect
    (T. Shlomi, T. Benyamini, E. Gottlieb, R. Sharan, E. Ruppin)
    PLoS Computational Biology, 7(3), e1002018, 2011
  • Associations between translation efficiency and horizontal gene transfer within microbial communities.
    (T. Tuller, Y. Girshovich, Y. Sella, A. Kreimer, S. Freilich, M. Kupiec, U. Gophna, E. Ruppin)
    Nucleic Acids Research (NAR), 39(11): 4743-4755. doi: 10.1093/nar/gkr054, 2011.

    • 2010

  • iMAT: an integrative metabolic analysis tool
    (H. Tzur, E. Ruppin*, T. Shlomi*)
    Bioinformatics, doi: 10.1093/bioinformatics.btq602. *equal contribution
  • Combining drug and gene similarity metrics for drug-target elucidation
    (L. Perlman, A. Gottlieb, N. Atias, E. Ruppin, R. Sharan)
    Journal of Computational Biology (JCB), to appear.
  • A novel HMM-based method for detecting enriched transcription factor binding sites reveals RUNX3 as a potential target in pancreatic cancer biology
    (L. Levkovitz, N. Yosef, M.C. Gershgoren, E. Ruppin, R. Sharan, Y. Oron)
    PLoS One, to appear.
  • Metabolic reconstruction, constraint-based analysis and game theory to proble genome-scale metabolic networks
    (E. Ruppin, J.A. Papin, L.F. Figueiredo, S. Schuster)
    Current Opinion in Biotechnology, 21:1-9, 2010, doi:10.1016/j.ocpbio.2010.07.002.
  • Transcriptional regulation by CHIP/LDB complexes
    (R. Bronstein, L. Levkovitz, N. Yosef, M. Yanku, E. Ruppin, R. Sharan, H. Westphal, B. Oliver, D. Segal)
    PLoS Genetics, 6(8), August 2010, e10001063.
  • Computational reconstruction of tissue-specific metabolic models: Application to human liver metabolism
    (L. Jerby, T. Shlomi*, E. Ruppin*)
    Molecular Systems Biology (MSB), 6, Article number 401; doi: 10.1038/msb.2010.56, September 2010. *equal contribution.
  • MuD: an interactive web-server for the prediction of non-neutral using protein structural data.
    (G. Wainreb, Y. Bromberg, H. Ashkenazy, A. Starovolsky-Shitrit, T. Haliloglu, E. Ruppin, K. Avraham, B. Rost, N. Ben-Tal)
    Nucleic Acids Research (NAR), to appear.
  • Reconstructing ancestral genomic sequences by co-evolution: formal definitions, computational issues and biological examples.
    (T. Tuller, H. Birin, M. Kupiec, E. Ruppin)
    Journal of Computational Biology (JCB), 17(9), 1327-1344, September 2010.
  • Cooperation and cheating in exoenzyme production by microorganisms -- theoretical analysis in view of biotechnological applications
    (S. Schuster, J.U Kreft, N. Brenner, F. Wessely, G. Theiben, E. Ruppin, A. Schroeter)
    Biotechnology Journal, 5, 2010, doi: 10.1002/biot.200900303.
  • Flux balance analysis accounting for metabolite dilution
    (T. Benyamini, Ori Folger, E. Ruppin, T. Shlomi)
    Genome Biology, 11:R43, 2010, doi: 10.1186/gb-2010-11-4-r43.
  • Integrating quantitative proteomics and metabolomics with a genome-scale metabolic network model.
    (K. Yizhak, T. Benyamini, W. Liebermeister, E. Ruppin, T. Shlomi)
    Bioinformatics, 26, ISMB 2010, p i255-i260, doi: 10.1093/bioinformatics/btq183.
  • The large scale organization of the bacterial network of ecological co-occurence interactions
    (S. Freilich, A. Kreimer, I. Meilijson, U. Gophna, R. Sharan, E. Ruppin)
    Nucleic Acids Research (NAR), 2010, doi:10.1093/nar/gkq118.
  • Translation efficiency in humans: tissue specificity, global optimization and the differences between developmental stages
    (Y. Waldman, T. Tuller, T. Shlomi, R. Sharan, E. Ruppin)
    Nucleic Acids Research (NAR), 38(9), 2010, doi:/10.1093/nar/gkq009.
  • A systems level strategy for analyzing the cell death network: Implication in exploring the apoptosis/autophagy connection
    (E. Zalckvar, N. Yosef, S. Reef, Y. Ber, A. Rubinstein, R. Sharan, E. Ruppin, A. Kimchi)
    Cell Death and Differentiation (CDD), February 2010, doi:10.1038/cdd.2010.7.
  • Translation efficiency is determined by both codon bias and folding energy
    (T. Tuller, Y. Waldman, M. Kupiec, E. Ruppin)
    Proceedings of the National Academy of Sciences (PNAS), doi: 10.1073/pnas.0909910107, February 2010.
  • Decoupling environmental-dependent and independent genetic robustness across bacterial species
    (S. Freilich*, A. Kreimer*, E. Borenstein, U. Gophna, R. Sharan, E. Ruppin; *equal contribution)
    PLoS Computational Biology, 6(2), e1000690, 2010.
  • Associating genes and protein complexes with disease via network propagation
    (O. Vanunu, O. Magger, E. Ruppin, T. Shlomi, R. Sharan)
    PLoS Computational Biology, 6(1): e1000641, 2010.
  • Network-free prediction of knockout effects in yeast
    (T. Peleg, N. Yosef, E. Ruppin, R. Sharan)
    PLoS Computational Biology, 6(1): e1000635, 2010.

    • 2009

  • Reconstructing ancestral gene content by co-evolution
    (T. Tuller, H. Birin, U. Gophna, M. Kupiec, E. Ruppin)
    Genome Research, November 2009, doi:10.1101/gr.096115.109.
  • TP53 cancerous mutations exhibit selection for translation efficiency
    (Y. Waldman, T. Tuller, R. Sharan, E. Ruppin )
    Cancer Research, 69: (22), 8807-8813, November 2009.
  • Properties of untranslated regions of the S. cerevisiae Genome
    (T. Tuller, E. Ruppin, M. Kupiec)
    BMC Genomics, 10: 391, August 2009.
  • Metabolic network-based analysis of yeast gene-nutrient interactions
    (I. Diamant, Y. Eldar, O. Rokhlenko, E. Ruppin, T. Shlomi)
    Molecular Biosystems, 5, 1732-1739, doi: 10.1039/b823287n, 2009.
  • Metabolic-network driven analysis of bacterial ecological strategies
    (S. Freilich, A. Kreimer, E. Borenstein, R. Sharan, U. Gophna, E. Ruppin)
    Genome Biology, 10(6):R61, June 5, 2009.
  • Increased microRNA activity in human cancers
    (A. Israel, R. Sharan, E. Ruppin, E. Galun)
    PLoS One, 4(6):e6045, June 25, 2009.
  • A complex-centric view of protein network evolution
    (N. Yosef, M. Kupiec, E. Ruppin, R. Sharan)
    Nucleic Acids Research (NAR), doi:10.1093/nar/gkp414, May 2009.
  • Co-evolutionary networks of genes and cellular processes across fungal species
    (T. Tuller, M. Kupiec, E. Ruppin)
    Genome Biology, 10:R48, doi:10.1186/gb-2009-5-r48, May 2009.
  • A genome-wide screen for essential yeast genes that affect telomere length maintenance.
    (L. Ungar, N. Yosef, Y. Sela, R. Sharan, E. Ruppin, M. Kupiec)
    Nucleic Acids Research (NAR), doi:10.1093/nar/gkp259, April 2009.
  • Multi-perturbation analysis of distributed neural networks: the case of spatial neglect.
    (A. Kaufman, C. Serfaty, L.Y. Deouell, E. Ruppin, N. Soroker)
    Human Brain Mapping (HBM), May 15,2009.
  • Network-based prediction of metabolic enzymes subcellular localization
    (S. Mintz, A. Aharoni, E. Ruppin, T. Shlomi)
    Bioinformatics (ISMB 2009 Proceedings), 25(12):i247-52, June 15, 2009.
  • Predicting metabolic biomarkers of human inborn errors of metabolism
    (T. Shlomi, M. N. Cabili, E. Ruppin)
    Molecular Systems Biology (MSB), 5:263, doi:10.1038/msb.2009.22, May 2009.
  • Toward accurate reconstruction of functional protein networks
    (N. Yosef, L. Ungar, E. Zalckvar, A. Kimchi, M. Kupiec, E. Ruppin, R. Sharan)
    Molecular Systems Biology (MSB), 5:248, doi:10.1038/msb.2009.5, March 2009.
  • Higher-order genomic organization of cellular functions in yeast
    (T. Tuller, U. Rubinstein, D. Bar, M. Gurevitch, E. Ruppin, M. Kupiec)
    Journal of Computational Biology, Feb 2009, 16(2), 303-316.

    • Papers in Computational Biology, by topics -- 2004 - 2008

    Constraint-based Metabolic Modeling
    Protein & Signalling Networks
    Evolutionary Systems Biology
    Others

    Papers in Past Research Topics -- up to ~2004

    Machine Learning and Natural Language Processing
    Artificial Life and Evolutionary Computation
    Neural Modeling of Brain Disorders
    Cognitive Neuroscience Modeling
    Dynamics of Neural Networks
    Others

    Books

    1. Neural Modeling of Brain and Cognitive Disorders -- Reggia, Ruppin, Berndt (1996)
    2. Brain, Behavioral and Cognitive Disorders: The Computational Perspective -- Reggia, Ruppin, Glanzman (1999)

    teaching

    1. Computational systems biology (2013)
    2. Seminar on genome-scale metabolic modeling (2012)
    3. Computer Structure (2010)
    4. Computational Systems Biology Seminar (2009)
    5. Computational Systems Biology (2008)
    6. Adios Workshop (Sadna, 2005)
    7. Computer Structure (2005) -- a pointer to Yehuda Afek's page
    8. Artificial Life Workshop (Sadna, 2004)
    9. Artificial Life Workshop (Sadna, 2003)
    10. NLP: The IR Perspective (2001, 2002) --- Course notes (zip file)
    11. Seminar on Evolutionary Autonomous Agents and Neuroscience (2002) (Alife)
    12. Introduction to Artificial Intelligence (99,2000)
    13. Seminar on Evolutionary Autonomous Agents (99) (Alife)
    14. Seminar on Artificial life (98)
    15. Seminar on Neural Modeling of Brain Disorders (96/97)

    Links

    1. The Edmond J. Safra Bioinformatics Program at TAU
    2. Metabolic Networks Analysis
    3. Statistical Natural Language Processing
    4. Evoweb - European network of excellence in Evolutionary Computing
    5. Complex Adaptive Systems and Artificial Life
    6. Neural Computation
    7. Artificial Intelligence in Medicine

    Coordinates

     

    Professor Eytan Ruppin, MD, PhD

    School of Computer Sciences & School of Medicine

    Tel Aviv University

    Tel Aviv 69978, Israel

    email: ruppin@post.tau.ac.il

    +972-3-640-6528 (voice)

    +972-3-640-9357 (fax)

    +972-8-9261565 (home)