Back to homepage

Papers in Computational Biology

1. Constraint-based Metabolic Modeling

  1. Metabolic network-based analysis of yeast gene-nutrient interactions
    (I. Diamant, Y. Eldar, O. Rokhlenko, E. Ruppin, T. Shlomi)
    Molecular Biosystems, 5, 1732-1739, 2009.
  2. 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.
  3. 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.
  4. 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, 2008.
  5. Can single knockouts accurately single our gene functions?
    (D. Deutscher, I. Meilijson, S. Schuster, E. Ruppin)
    BMC Systems Biology, 2:50, doi:10.1186/1752-0509-2-50, June 2008.
  6. Structural robustness of metabolic networks with repsect to multiple knockouts
    (J. Behre, T. Wilhelm, A. von Kamp, E. Ruppin, S. Schuster)
    Journal of Theoretical Biology, to appear, 2008.
  7. Systematic condition-dependent annotation of metabolic genes
    (T. Shlomi, M. Herrgard, V. Portnoy, E. Naim, B.O. Palsson, R. Sharan, E. Ruppin)
    Genome Research, doi:10.1101/gr.6678707, 2007.
  8. A genome scale computational study of the interplay between transcriptional regulation and metabolism.
    (T. Shlomi, Y. Eisenberg, R. Sharan, E. Ruppin)
    Molecular Systems Biology (MSB), 3:101, doi:10.1038/msb4100141, 2007.
  9. 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.
  10. Conservation of expression and sequence of metabolic genes is reflected by their activity across metabolic states
    (Y. Bilu, T. Shlomi, N. Barkai, E. Ruppin)
    PLoS Computational Biology, 2(8), e106, 2006.
  11. Constraint-based functional similarity of metabolic genes: Going beyond network topology
    (O. Rokhlenko, T. Shlomi, R. Sharan, E. Ruppin, R. Pinter)
    Bioinformatics, doi:10.1093/bioinformatics/btm319, 2007.
  12. Regulatory on/off minimization of metabolic flux changes after genetic perturbations
    (T. Shlomi, O. Berkman, E. Ruppin)
    Proceedings of the National Academy of Sciences (PNAS), 102(21), 7695-7700, 2005.

    13. Back to homepage

    2. Protein & Signalling Networks

    1. Network-free prediction of knockout effects in yeast
      (T. Peleg, N. Yosef, E. Ruppin, R. Sharan)
      PLoS Computational Biology, to appear.
    2. 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.
    3. 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.
    4. 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.
    5. A systems-level approach to mapping the telomere-length maintenance gene circuitry
      (R. Shachar, L. Ungar, M. Kupiec, E. Ruppin, R. Sharan)
      Molecular Systems Biology (MSB), doi:10.1038/msb.2008.13, March 2008.
    6. Determinants of protein abundance and translation efficiency in S. Cerevisiae
      (T. Tuller, M. Kupiec, E. Ruppin)
      PLoS Computational Biology, 3(12): e248, doi:10.1371/journal.pcbi.0030248, 2007.
    7. SPIN: A framework for signaling pathway inference from cause-effect experiments.
      (O. Ourfali, T. Shlomi, T. Ideker, E. Ruppin, R. Sharan)
      ISMB2007 & Bioinformatics, 23, i359-i366, 2007.
    8. Qnet: a tool for querying protein interaction networks.
      (B. Dost, T. Shlomi, N. Gupta, E. Ruppin, V. Bafna, R. Sharan)
      RECOMB 2007.
    9. Evolutionary Conservation and over-representation of functionally enriched network patterns in the yeast regulatory network
      (O. Meshi, T. Shlomi, E. Ruppin)
      BMC Systems Biology, 1:1, 2007.
    10. Inferring functional pathways from multi-perturbation data
      (N. Yosef, A. Kaufman, E. Ruppin)
      Bioinformatics (ISMB 2006 special issue), 22(14), e539-e546, 2006.
    11. A direct comparision of protein interaction confidence assignment schemes
      (S. Suthram, T. Shlomi, E. Ruppin, R. Sharan, T. Ideker)
      BMC Bioinformatics, 7:360 (26 July 2006).
    12. Qpath: A method for querying pathways in a protein-porein interaction network
      (T. Shlomi, D. Segal, E. Ruppin, R. Sharan)
      BMC Bioinofrmatics, 7, 199, April 10, 2006.
    13. Quantitative analysis of genetic and neuronal multi-perturbation experiments
      (A. Kaufman, A. Keinan, I. Meilijson, M. Kupiec, E. Ruppin)
      PLoS Computational Biology, 1(6), e64, 500-506, 2005.

      14. Back to homepage

      3. Evolutionary Systems Biology

      1. 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.
      2. 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.
      3. Evolutionary rate and gene expression across different brain regions
        (Tamir Tuller, Martin Kupiec, E. Ruppin)
        Genome Biology, 9, R142, September 2008.
      4. Large scale reconstruction and phylogenetic analysis of metabolic environments.
        (E. Borenstein, M. Kupiec, M.W. Feldman, E. Ruppin)
        Proceedings of the National Academy of Sciences (PNAS), 105(38), September, 2008.
      5. The evolution of modularity in bacterial metabolic networks
        (A. Kreimer, E. Borenstein, U. Gophna, E. Ruppin)
        Proceedings of the National Academy of Sciences (PNAS), 6976-6981, 105(19), May 2008.
      6. Gene loss rate: a probabilistic measure for the conservation of eukaryotic genes
        (E. Borenstein, T. Shlomi, E. Ruppin, R. Sharan)
        Nucleic Acids Research (NAR), doi:10.1093/nar/gkl792, 2007.
      7. The Effect of Phenotypic Plasticity on Evolution in Multipeaked Fitness Landscapes
        (E. Borenstein, I. Meilijson, E. Ruppin)
        Journal of Evolutionary Biology, 19(5), 1555-70, 2006.
      8. Direct evolution of genetic robustness in microRNA
        (E. Borenstein, E. Ruppin)
        Proceedings of the National Academy of Sciences (PNAS), 103(17), 6593-6598, 2006.

        9. Back to homepage

        4. Others

        1. 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.
        2. Increased microRNA activity in human cancers
          (A. Israel, R. Sharan, E. Ruppin, E. Galun)
          PLoS One, 4(6):e6045, June 25, 2009.
        3. 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.
        4. Pepitope: Epitope mapping from affinity-selected peptides.
          (I. Mayrose, O. Penn, E. Erez, N.D. Rubinstein, T. Shlomi, N.T. Freund, E.M. Bublil, E. Ruppin, R. Sharan, J.M. Gershoni, E. Martz, T. Pupko)
          Bioinformatics, to appear, 2008.
        5. Functional representation of enzymes by specific peptides.
          (V. Kunik, Y. Meroz, Z. Solan, B. Sandbank, U. Weingart, E. Ruppin, D. Horn)
          PLoS Computational Biology, doi:10.1371/journal.pcbi.0030167.eor, 2007.
        6. Nucleotid variation of regulatory motifs may lead to distinct expression patterns.
          (L. Segal, M. Lapidot, Z. Solan, E. Ruppin, Y. Pilpel, D. Horn)
          ISMB2007 & Bioinformatics, 23, i440-i449, 2007.
        7. Meta analysis of gene expression data: A predictor based approach.
          (I Fishel, A. Kaufman, E. Ruppin)
          Bioinformatics, 23, 1599-1606, doi:10.1093/bioinformatics/btm149, 2007.
        8. Genetic interactions in yeast: Is robustness going bust? (News & Views)
          (M. Kupiec, R. Sharan, E. Ruppin)
          Molecular Systems Biology (MSB), doi:10.1038/msb4100136, 2007.
        9. Medical sequencing of extremes of human body mass
          (N. Ahituv, N. Kavaslar, W. Schackwitz, A. Ustazewska, J. Martin, S. Hebert, H. Doelle, B. Ersoy, G. Kryukov, S. Schmidt, N. Yosef, E. Ruppin, R. Sharan, C. Vaisse, S. Sunyaev, R. Dent, J. Cohen, R. McPherson, L.A. Pennacchio)
          Amercan Journal of Human Genetics, 80(4), 779-791, 2007.
        10. A supervised approach for identifying genotype patterns and its application to breast cancer data
          (N. Yosef, Z. Yakhini, A. Tsalenko, V. Kristensen, A.L. Borresen-Dale, E. Ruppin, R. Sharan)
          Bioinformatics (ECCB 2006 special issue), 23(2), e91-e98, 2007.
        11. Epitope mapping using combinatorial phage-display libraries: A graph-based algorithm.
          (I. Mayrose, T. Shlomi, N. Rubinstein, J. Gershoni, E. Ruppin, R. Sharan, T. Pupko)
          Nucleic Acids Research (NAR), doi:10.1093/nar/gkl975, 2007.
        12. Gene expression of C elegans neurons carries information on their synaptic connectivity.
          (A. Kaufman, G. Dror, I. Meilijson, E. Ruppin)
          PLoS Computational Biology, 2(12), e167, doi:10.1371, 2006.
        13. Fair attribution of functional contribution in artificial and biological networks
          (A. Keinan, B. Sandback, C. Hilgetag, I. Meilijson, E. Ruppin)
          Neural Computation, 16(9), 1889-1915, 2004
        14. Fair Localization of Function via Multi-lesion Analysis
          (A. Keinan, B. Sandback, A. Kaufman, N. Sachs, C. Hilgetag, E. Ruppin)
          Neuroinformatics, 2(2), 163-168, 2004.

        Back to homepage

        Papers in Past Research Topics

        1. Machine Learning and Natural Language Processing

        1. A machine learning predictor of human facial attractiveness revealing human-like psychophysical biases
          (A. Kagian, G. Dror, T. Leyvand, I. Meilijson, D. Cohen-Or, E. Ruppin)
          Vision Research, 48, 235-243, 2008.
        2. Feature selection via coalitional game theory
          (S. Cohen, G. Dror, E. Ruppin)
          Neural Computation, 19, 1939-1961, 2007.
        3. A humanlike predictor of facial attractiveness
          (A. Kagian, G. Dror, T. Levyand, D. Cohen-Or, E. Ruppin)
          Neural Information Processing Systems (NIPS) 2006
        4. Boosting unsupervised grammar induction by splitting complex sentences on function words
          (J. Berant, Y. Gross, M. Mussel, B. Sandbank, E. Ruppin, S. Edelman)
          Boston University Conference on Language Development (BUCLD) 2006
        5. Unsupervised learning of natural languages
          (Z. Solan, D. Horn, E. Ruppin, S. Edelman)
          PNAS, 102 (33), 11629-11634, 2005. (Supplamentary material)
        6. Facial attractiveness: Beauty and the machine
          (Y. Eisenthal, G. Dror, E. Ruppin)
          Neural Computation, 18(1), 119-142, 2006
        7. A distributive, self-organizing approach to language acquistion and representation (Zach Solan, CogSci 2003)
        8. Language acquisition via a distributive approach -- NIPS 2002
        9. Placing search in context: the concept revisited
          (L. Finkelstein, E. Gabrilovitch, Y. Matias, E. Rivlin, Z. Solan, G. Wolfman, E. Ruppin)
          ACM Transactions of Information Systems, 20(1), (2002), 116-131.

        Back to homepage

        2. Artificial Life and Evolutionary Computation

        1. Axiomatic Scalable Neurocontroller Analysis Via the Shapley Value
          (A. Keinan, B. Sandbank, C. C. Hilgetag, I. Meilijson, E. Ruppin)
          Artificial Life, 12(3), 333-352, 2006
        2. Evolutionary Network minimization in neurocontrollers
          (Z. Ganon, A. Keinan, E. Ruppin)
          Artificial Life, 12(3), 435-448, 2006
        3. Neural Processing of Counting in Evolved spiking and McCullogh-Pitts agents
          (K. Saggie, A. Keinan, E. Ruppin) 
          Artificial Life 12(1), 1-16, 2006
        4. Evolving imitating agents and mirror neurons' emergence
          (Elhanan Borenstein, E. Ruppin)
          Cognitive Systems Research, 6(3), 229-242, 2005
        5. Enhancing Autonomous Agents Evolution with Learning by imitation
          (E. Borenstein, E. Ruppin)
          Artificial Intelligence and Simulation of Behavior (AISB) Journal, 1(4), (2003), 335-347.
        6. Controlled analysis of neurocontrollers with Informational Lesioning
          (A. Keinan, I. Meilijson, E. Ruppin)
          Phil. Trans. of the Royal Society of London, A, 361, (2003), 2123-2144.
        7. Evolving Small Neurocontrollers with Self-Organized Compact Encoding
          (S. Boshy, E. Ruppin)
          Artificial Life, 9(2), (2003), 131-151.
        8. High-DimensionalAnalysis of Evolutionary Autonomous Agents
          (L. Segev, R. Aharonov, I. Meilijson, E. Ruppin )
          Artificial Life, 9(1), (2003), 385-404.
        9. Localization of Function Via Lesion Analysis
          (R. Aharonov, L. Segev, I. Meilijson, E. Ruppin)
          Neural Computation, 15(4), (2003), 885-913.
        10. Are there representations in evolved agents? Taking measures (Hezy Avraham, Gal Chechik, Eytan Ruppin , ECAL 2003)
        11. Evolutionary AutonomousAgents: A Neuroscience Perspective
          (E. Ruppin)
          Nature Reviews Neuroscience, 3(2), (2002), 132-141.
        12. Evolution of Reinforcement Learning in uncertain environments: A simple explanation for complex foraging behaviors
          (Y. Niv, D. Joel, I Meilijson, E. Ruppin)
          Adaptive Behavior, 10(1), (2002), 5-24.
        13. Emergence of Memory-Driven Command Neurons in Evolved Artificial Agents
          (R. Aharonov-Barki, T. Beker, E. Ruppin.)
          Neural Computation, 13(3), (2001), 691-716.
        14. Co-evolving architectures for cellular machines
          (M. Sipper, E. Ruppin)
          Physica D 99 (1997), 428-441.

        Back to homepage

        3. Neural Modeling of Brain Disorders

        1. Modeling brain energy metabolism and function: a multiparametric monitoring approach
          (L. Vatov, Z. Kizner, E. Ruppin, S. Meilin, T. Manor, A. Mayevsky)
          Bulletin of Mathematical Biology, 68 (2006), 275-291.
        2. NMDA receptor delayed maturation and schizophrenia
          (E. Ruppin)
          Medical Hypotheses, 54(5), (2000), 693-697.
        3. Pathogenic Mechanisms in Ischemic Damage: A Computational Study
          (E. Ruppin, E. Ofer, J. Reggia, K. Revett, S. Goodall)
          Computers in Biology and Medicine, 29(1), (1999), 39-59.
        4. Spreading Depression in Focal Ischemia: A Computational Study
          (K. Revett, E. Ruppin, S. Goodall, J. Reggia)
          Journal of Cerebral Blood Flow and Metabolism, 18(9), (1998), 998-1007.
        5. Synaptic Runaway in Associative Networks and the Pathogenesis of Schizophrenia
          (A. Greenstein-Messica, E. Ruppin)
          Neural Computation, 10(2), (1998), 451-465.
        6. Computer Models: A New Approach to the Investigation of Disease
          (J. Reggia, E. Ruppin, R. Sloan Berndt)
          MD Computing, 14(3), (1997), 160-168.
        7. A Computational Model of Acute Focal Cortical Lesions
          (S. Goodall, J. Reggia, Y. Chen, E. Ruppin, C. Whitney)
          Stroke, 28, (1997), 101-109.
        8. Neuronal-based synaptic compensation: A computational study in Alzheimer's disease
          (D. Horn, N. Levy, E. Ruppin)
          Neural Computation 8(6) (1996), 1227-1243.
        9. Pathogenesis of Schizophrenic Delusions and Hallucinations: A Neural Model
          (E. Ruppin, J. Reggia, D. Horn)
          Schizophrenia Bulletin 22(1) (1996), 105-123.
        10. Neural modeling of psychiatric disorders
          (E. Ruppin)
          Network: Comput. Neural Syst. 6 (1995), 635-656.
        11. Patterns of functional damage in neural network models of associative memory
          (E. Ruppin, J. Reggia)
          Neural Computation 7(5) (1995), 1105-1127.
        12. A Neural Model of Memory Impairment in Diffuse Cerebral Atrophy
          (E. Ruppin, J. Reggia)
          Br. Jour. of Psychiatry 166(1) (1995), 19-28.
        13. Compensatory mechanisms in an attractor neural network model of Schizophrenia.
          (D. Horn, E. Ruppin)
          Neural Computation 7(1) (1994), 1494-1517.
        14. Neural Network Modeling of Memory Deterioration in Alzheimer's Disease
          (D. Horn, E. Ruppin, M. Usher, M. Herrmann)
          Neural Computation 5 (1993), 736-749.
        15. Extra-pyramidal symptoms in Alzheimer's disease: a hypothesis
          (D. Horn, E. Ruppin)
          Medical Hypothesis 39 (1992), 316-318.

        Back to homepage

        4. Cognitive Neuroscience Modeling

        1. 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.
        2. Actor-critic models of the basal ganglia: new anatomical and computational perspectives
          (D. Joel, Y. Niv, E. Ruppin)
          Neural Networks, 15(4-6), (2002), 535-547.
        3. Effective Neuronal Learning with Ineffective Hebbian Learning Rules
          (G. Chechik, I. Meilijson, E. Ruppin)
          Neural Computation, 13(4), (2001), 817-840.
        4. Similarity in Perception: A Window to Brain Development and Organization
          (Z. Solan and E. Ruppin)
          Journal of Cognitive Neuroscience, 13(1), (2001), 18-30.
        5. Reinforcement-driven dimensionality reduction - a model for information processing in the basal ganglia
          (I. Bar-Gad, G. Havazelet-Heimer, J. Goldberg, E. Ruppin and H. Bergman)
          J. Basic and Clin. Physiol. Phram, 11(4), (2000), 305-320.
        6. Associative Memory in a Multi-modular Network
          (N. Levy, D. Horn, E. Ruppin)
          Neural Computation, 11(7), (1999), 1717-1737.
        7. Neuronal Regulation: a mechanism for Synaptic Pruning During Brain Maturation
          (G. Chechik, I. Meilijson, E. Ruppin)
          Neural Computation, 11(8), (1999), 2061-2080.
        8. From Parallel To Serial Processing: A Computational Study of Visual Search
          (E. Cohen, E. Ruppin)
          Perception and Psychophysics, 61(7), (1999), 1449-1461.
        9. Neuronal Regulation vs Synaptic Unlearning in Memory Maintenance
          (D. Horn, N. Levy, E. Ruppin)
          Network: Comput. Neural Syst., 9, (1998), 577-586.
        10. Synaptic Pruning in Development: A Computational Account
          (G. Chechik, I. Meilijson, E. Ruppin)
          Neural Computation, 10(7), (1998), 1759-1777.
        11. Memory Maintenance via Neuronal Regulation
          (D. Horn, N. Levy, E. Ruppin)
          Neural Computation, 10(1), (1998), 1-18.
        12. A neural model of the dynamic activation of memory
          (M. Herrmann, E. Ruppin, M. Usher)
          Biological Cybernetics 68 (1993), 455-463.
        13. Recall and recognition in an attractor neural network of memory retrieval
          (E. Ruppin, Y. Yeshurun>
          Connection Science 3(4) (1991), 381-400.
        14. An attractor neural network model of semantic fact retrieval
          (E. Ruppin, M. Usher)
          Network: Comput. Neural Syst. 1 (1990), 325-344.

        Back to homepage

        5. Dynamics of Neural Networks

        1. Distributed Synchrony in a Cell Assembly of Spiking Neurons
          (N. Levy, D. Horn, I. Mielijson, E. Ruppin)
          Neural Networks, 14(6/7), (2001), 815-824.
        2. Frequency-Spatial Transformation: A Proposal for Parsimonious Intra-cortical Communication
          (R. Levi, E. Ruppin, Y. Matias, J. Reggia)
          Int. Jour. of Neural Systems 7(5) (1996), 591-598.
        3. Optimal firing in sparsely-connected low-activity attractor networks
          (I. Meilijson, E. Ruppin)
          Biological Cybernetics 74 (1996), 479-485.
        4. A single iteration threshold Hamming network
          (I. Meilijson, E. Ruppin, M. Sipper)
          IEEE Trans of NN 6(1) (1995), 261-266.
        5. Optimal signalling in Attractor Neural Networks
          (I. Meilijson, E. Ruppin)
          Network: Comput. Neural Syst. 5(2) (1994), 277-298.
        6. History-dependent signalling in Attractor Neural Networks
          (I. Meilijson, E. Ruppin)
          Network: Comput. Neural Syst. 4 (1993), 195-221.

        Back to homepage

        6. Others

        1. Examining the volume-efficiency of the cortical architecture in a multi-processor network model
          (E. Ruppin, E. Schwartz, Y. Yeshurun)
          Biological Cybernetics, 70, (1993), 89-94.