Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Targeting and tinkering with interaction networks

Nature Chemical Biology volume 4, pages 666–673 (2008)Cite this article

Abstract

Biological interaction networks have been in the scientific limelight for nearly a decade. Increasingly, the concept of network biology and its various applications are becoming more commonplace in the community. Recent years have seen networks move from pretty pictures with limited application to solid concepts that are increasingly used to understand the fundamentals of biology. They are no longer merely results of postgenome analysis projects, but are now the starting point of many of the most exciting new scientific developments. We discuss here recent progress in identifying and understanding interaction networks, new tools that use them in predictive ways in exciting areas of biology, and how they have become the focus of many efforts to study, design and tinker with biological systems, with applications in biomedicine, bioengineering, ecology and beyond.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Targeting the interaction between MDM2 and p53.
Figure 2: Leukemia disease network to predict undesired off-target effects.
Figure 3: Ring-like patterns formed by programmed bacteria.

Similar content being viewed by others

References

  1. Oliver, S.G. et al. The complete DNA sequence of yeast chromosome III. Nature 357, 38–46 (1992).

    CAS  PubMed  Google Scholar 

  2. Fields, S. & Song, O. A novel genetic system to detect protein-protein interactions. Nature 340, 245–246 (1989).

    CAS  PubMed  Google Scholar 

  3. Rigaut, G. et al. A generic protein purification method for protein complex characterization and proteome exploration. Nat. Biotechnol. 17, 1030–1032 (1999).

    CAS  PubMed  Google Scholar 

  4. Eyckerman, S. et al. Reverse MAPPIT: screening for protein-protein interaction modifiers in mammalian cells. Nat. Methods 2, 427–433 (2005).

    CAS  PubMed  Google Scholar 

  5. Paumi, C.M. et al. Mapping protein-protein interactions for the yeast ABC transporter Ycf1p by integrated split-ubiquitin membrane yeast two-hybrid analysis. Mol. Cell 26, 15–25 (2007).

    CAS  PubMed  Google Scholar 

  6. Uetz, P. et al. A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403, 623–627 (2000).

    CAS  PubMed  Google Scholar 

  7. Ito, T. et al. A comprehensive two-hybrid analysis to explore the yeast protein interactome. Proc. Natl. Acad. Sci. USA 98, 4569–4574 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Gavin, A.C. et al. Proteome survey reveals modularity of the yeast cell machinery. Nature 440, 631–636 (2006).

    CAS  PubMed  Google Scholar 

  9. Krogan, N.J. et al. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae. Nature 440, 637–643 (2006).

    CAS  PubMed  Google Scholar 

  10. Giot, L. et al. A protein interaction map of Drosophila melanogaster. Science 302, 1727–1736 (2003).

    CAS  PubMed  Google Scholar 

  11. Li, S. et al. A map of the interactome network of the metazoan C. elegans. Science 303, 540–543 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Rual, J.F. et al. Towards a proteome-scale map of the human protein-protein interaction network. Nature 437, 1173–1178 (2005).

    CAS  PubMed  Google Scholar 

  13. Stelzl, U. et al. A human protein-protein interaction network: a resource for annotating the proteome. Cell 122, 957–968 (2005).

    CAS  PubMed  Google Scholar 

  14. Collins, S.R. et al. Toward a comprehensive atlas of the physical interactome of Saccharomyces cerevisiae. Mol. Cell. Proteomics 6, 439–450 (2007).

    CAS  PubMed  Google Scholar 

  15. Rual, J.F. et al. Human ORFeome version 1.1: a platform for reverse proteomics. Genome Res. 14, 2128–2135 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Wei, C. et al. Closing in on the C. elegans ORFeome by cloning TWINSCAN predictions. Genome Res. 15, 577–582 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Li, Q.R. et al. Revisiting the Saccharomyces cerevisiae predicted ORFeome. Genome Res. 18, 1294–1303 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Vidalain, P.O., Boxem, M., Ge, H., Li, S. & Vidal, M. Increasing specificity in high-throughput yeast two-hybrid experiments. Methods 32, 363–370 (2004).

    CAS  PubMed  Google Scholar 

  19. Mahrus, S. et al. Global sequencing of proteolytic cleavage sites in apoptosis by specific labeling of protein N termini. Cell 134, 866–876 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Yu, H. et al. High-quality binary protein interaction map of the yeast interactome network. Science, published online, doi:10.1126/science.1158684 (21 August 2008).

  21. Stein, A. & Aloy, P. A molecular interpretation of genetic interactions in yeast. FEBS Lett. 582, 1245–1250 (2008).

    CAS  PubMed  Google Scholar 

  22. Pawson, T. & Linding, R. Synthetic modular systems–reverse engineering of signal transduction. FEBS Lett. 579, 1808–1814 (2005).

    CAS  PubMed  Google Scholar 

  23. Bushell, K.M., Sollner, C., Schuster-Boeckler, B., Bateman, A. & Wright, G.J. Large-scale screening for novel low-affinity extracellular protein interactions. Genome Res. 18, 622–630 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Gisler, S.M. et al. Monitoring protein-protein interactions between the mammalian integral membrane transporters and PDZ-interacting partners using a modified split-ubiquitin membrane yeast two-hybrid system. Mol. Cell Proteomics 7, 1362–1377 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Vazquez, M.E., Nitz, M., Stehn, J., Yaffe, M.B. & Imperiali, B. Fluorescent caged phosphoserine peptides as probes to investigate phosphorylation-dependent protein associations. J. Am. Chem. Soc. 125, 10150–10151 (2003).

    CAS  PubMed  Google Scholar 

  26. Linding, R. et al. Systematic discovery of in vivo phosphorylation networks. Cell 129, 1415–1426 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Aloy, P. Shaping the future of interactome networks. Genome Biol. 8, 316 (2007).

    PubMed  PubMed Central  Google Scholar 

  28. Berger, M.F. & Bulyk, M.L. Protein binding microarrays (PBMs) for rapid, high-throughput characterization of the sequence specificities of DNA binding proteins. Methods Mol. Biol. 338, 245–260 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Warner, J.B. et al. Systematic identification of mammalian regulatory motifs' target genes and functions. Nat. Methods 5, 347–353 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Mukhopadhyay, A., Deplancke, B., Walhout, A.J. & Tissenbaum, H.A. Chromatin immunoprecipitation (ChIP) coupled to detection by quantitative real-time PCR to study transcription factor binding to DNA in Caenorhabditis elegans. Nat. Protoc. 3, 698–709 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Vetter, D. Chemical microarrays, fragment diversity, label-free imaging by plasmon resonance–a chemical genomics approach. J. Cell. Biochem. Suppl. 39, 79–84 (2002).

    PubMed  Google Scholar 

  32. Pearson, H. Meet the human metabolome. Nature 446, 8 (2007).

    CAS  PubMed  Google Scholar 

  33. Orchard, S. et al. The minimum information required for reporting a molecular interaction experiment (MIMIx). Nat. Biotechnol. 25, 894–898 (2007).

    CAS  PubMed  Google Scholar 

  34. Kanehisa, M. et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–D484 (2008).

    CAS  PubMed  Google Scholar 

  35. Borneman, A.R. et al. Target hub proteins serve as master regulators of development in yeast. Genes Dev. 20, 435–448 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Barabasi, A.L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).

    CAS  PubMed  Google Scholar 

  37. Albert, R., Jeong, H. & Barabasi, A.L. Error and attack tolerance of complex networks. Nature 406, 378–382 (2000).

    CAS  PubMed  Google Scholar 

  38. Wolf, Y.I., Karev, G. & Koonin, E.V. Scale-free networks in biology: new insights into the fundamentals of evolution? Bioessays 24, 105–109 (2002).

    PubMed  Google Scholar 

  39. Jeong, H., Mason, S.P., Barabasi, A.L. & Oltvai, Z.N. Lethality and centrality in protein networks. Nature 411, 41–42 (2001).

    CAS  PubMed  Google Scholar 

  40. Pache, R., Babu, M. & Aloy, P. Exploiting gene deletion fitness effects in yeast to understand the modular architecture of protein complexes under different growth conditions. PLoS Comput. Biol. (in the press).

  41. Aloy, P. & Russell, R.B. Structural systems biology: modelling protein interactions. Nat. Rev. Mol. Cell Biol. 7, 188–197 (2006).

    CAS  PubMed  Google Scholar 

  42. Jansen, R. et al. A Bayesian networks approach for predicting protein-protein interactions from genomic data. Science 302, 449–453 (2003).

    Article  CAS  PubMed  Google Scholar 

  43. Mani, K.M. et al. A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol. Syst. Biol. 4, 169 (2008).

    PubMed  PubMed Central  Google Scholar 

  44. Pujana, M.A. et al. Network modeling links breast cancer susceptibility and centrosome dysfunction. Nat. Genet. 39, 1338–1349 (2007).

    CAS  PubMed  Google Scholar 

  45. Chuang, H.Y., Lee, E., Liu, Y.T., Lee, D. & Ideker, T. Network-based classification of breast cancer metastasis. Mol. Syst. Biol. 3, 140 (2007).

    PubMed  PubMed Central  Google Scholar 

  46. Pazos, F., Juan, D., Izarzugaza, J.M., Leon, E. & Valencia, A. Prediction of protein interaction based on similarity of phylogenetic trees. Methods Mol. Biol. 484, 523–535 (2008).

    CAS  PubMed  Google Scholar 

  47. Juan, D., Pazos, F. & Valencia, A. High-confidence prediction of global interactomes based on genome-wide coevolutionary networks. Proc. Natl. Acad. Sci. USA 105, 934–939 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Borneman, A.R. et al. Divergence of transcription factor binding sites across related yeast species. Science 317, 815–819 (2007).

    CAS  PubMed  Google Scholar 

  49. Papageorgiou, A.C. & Wikman, L.E. Is JAK3 a new drug target for immunomodulation-based therapies? Trends Pharmacol. Sci. 25, 558–562 (2004).

    CAS  PubMed  Google Scholar 

  50. Johnson, Z. et al. Chemokine inhibition–why, when, where, which and how? Biochem. Soc. Trans. 32, 366–377 (2004).

    CAS  PubMed  Google Scholar 

  51. Wells, J.A. & McClendon, C.L. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 450, 1001–1009 (2007).

    CAS  PubMed  Google Scholar 

  52. Neduva, V. & Russell, R.B. Peptides mediating interaction networks: new leads at last. Curr. Opin. Biotechnol. 17, 465–471 (2006).

    CAS  PubMed  Google Scholar 

  53. Rix, U. et al. Chemical proteomic profiles of the BCR-ABL inhibitors imatinib, nilotinib, and dasatinib reveal novel kinase and nonkinase targets. Blood 110, 4055–4063 (2007).

    CAS  PubMed  Google Scholar 

  54. Adrian, F.J. et al. Allosteric inhibitors of Bcr-abl-dependent cell proliferation. Nat. Chem. Biol. 2, 95–102 (2006).

    CAS  PubMed  Google Scholar 

  55. Oltersdorf, T. et al. An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature 435, 677–681 (2005).

    Article  CAS  PubMed  Google Scholar 

  56. Vassilev, L.T. et al. In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 303, 844–848 (2004).

    CAS  PubMed  Google Scholar 

  57. Moerke, N.J. et al. Small-molecule inhibition of the interaction between the translation initiation factors eIF4E and eIF4G. Cell 128, 257–267 (2007).

    CAS  PubMed  Google Scholar 

  58. He, M.M. et al. Small-molecule inhibition of TNF-alpha. Science 310, 1022–1025 (2005).

    CAS  PubMed  Google Scholar 

  59. Braisted, A.C. et al. Discovery of a potent small molecule IL-2 inhibitor through fragment assembly. J. Am. Chem. Soc. 125, 3714–3715 (2003).

    CAS  PubMed  Google Scholar 

  60. Jhoti, H., Cleasby, A., Verdonk, M. & Williams, G. Fragment-based screening using X-ray crystallography and NMR spectroscopy. Curr. Opin. Chem. Biol. 11, 485–493 (2007).

    CAS  PubMed  Google Scholar 

  61. Neumann, T., Junker, H.D., Schmidt, K. & Sekul, R. SPR-based fragment screening: advantages and applications. Curr. Top. Med. Chem. 7, 1630–1642 (2007).

    CAS  PubMed  Google Scholar 

  62. Neduva, V. et al. Systematic discovery of new recognition peptides mediating protein interaction networks. PLoS Biol. 3, e405 (2005).

    PubMed  PubMed Central  Google Scholar 

  63. Edwards, R.J. et al. Bioinformatic discovery of novel bioactive peptides. Nat. Chem. Biol. 3, 108–112 (2007).

    CAS  PubMed  Google Scholar 

  64. Stein, A. & Aloy, P. Contextual specificity in peptide-mediated protein interactions. PLoS ONE 3, e2524 (2008).

    PubMed  PubMed Central  Google Scholar 

  65. van der Greef, J. & McBurney, R.N. Innovation: rescuing drug discovery: in vivo systems pathology and systems pharmacology. Nat. Rev. Drug Discov. 4, 961–967 (2005).

    CAS  PubMed  Google Scholar 

  66. Apic, G., Ignjatovic, T., Boyer, S. & Russell, R.B. Illuminating drug discovery with biological pathways. FEBS Lett. 579, 1872–1877 (2005).

    CAS  PubMed  Google Scholar 

  67. Xu, J.J. et al. Cellular imaging predictions of clinical drug-induced liver injury. Toxicol. Sci. 105, 97–105 (2008).

    CAS  PubMed  Google Scholar 

  68. Saez-Rodriguez, J. et al. Flexible informatics for linking experimental data to mathematical models via DataRail. Bioinformatics 24, 840–847 (2008).

    CAS  PubMed  Google Scholar 

  69. Kuhn, M., von Mering, C., Campillos, M., Jensen, L.J. & Bork, P. STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. 36, D684–D688 (2008).

    CAS  PubMed  Google Scholar 

  70. Borisy, A.A. et al. Systematic discovery of multicomponent therapeutics. Proc. Natl. Acad. Sci. USA 100, 7977–7982 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Lee, M.S. et al. The novel combination of chlorpromazine and pentamidine exerts synergistic antiproliferative effects through dual mitotic action. Cancer Res. 67, 11359–11367 (2007).

    CAS  PubMed  Google Scholar 

  72. Lehár, J. et al. Chemical combination effects predict connectivity in biological systems. Mol. Syst. Biol. 3, 80 (2007).

    PubMed  PubMed Central  Google Scholar 

  73. Zimmermann, G.R., Lehar, J. & Keith, C.T. Multi-target therapeutics: when the whole is greater than the sum of the parts. Drug Discov. Today 12, 34–42 (2007).

    CAS  PubMed  Google Scholar 

  74. Lindsay, M.A. Finding new drug targets in the 21st century. Drug Discov. Today 10, 1683–1687 (2005).

    CAS  PubMed  Google Scholar 

  75. Altschul, S.F. et al. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Matilla-Dueñas, A. The highly heterogeneous spinocerebellar ataxias: from genes to targets for therapeutic intervention. Cerebellum 7, 97–100 (2008).

    PubMed  Google Scholar 

  77. Suthram, S., Sittler, T. & Ideker, T. The Plasmodium protein network diverges from those of other eukaryotes. Nature 438, 108–112 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. Kelley, B.P. et al. Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc. Natl. Acad. Sci. USA 100, 11394–11399 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. Koyutürk, M. et al. Pairwise alignment of protein interaction networks. J. Comput. Biol. 13, 182–199 (2006).

    PubMed  Google Scholar 

  80. Flannick, J., Novak, A., Srinivasan, B.S., McAdams, H.H. & Batzoglou, S. Graemlin: general and robust alignment of multiple large interaction networks. Genome Res. 16, 1169–1181 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Sharan, R. & Ideker, T. Modeling cellular machinery through biological network comparison. Nat. Biotechnol. 24, 427–433 (2006).

    CAS  PubMed  Google Scholar 

  82. Kiemer, L. & Cesareni, G. Comparative interactomics: comparing apples and pears? Trends Biotechnol. 25, 448–454 (2007).

    CAS  PubMed  Google Scholar 

  83. Pawson, T. Organization of cell-regulatory systems through modular-protein-interaction domains. Philos. Transact. A Math Phys. Eng. Sci. 361, 1251–1262 (2003).

    CAS  Google Scholar 

  84. Kashtan, N. & Alon, U. Spontaneous evolution of modularity and network motifs. Proc. Natl. Acad. Sci. USA 102, 13773–13778 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Yeh, B.J. & Lim, W.A. Synthetic biology: lessons from the history of synthetic organic chemistry. Nat. Chem. Biol. 3, 521–525 (2007).

    CAS  PubMed  Google Scholar 

  86. Bashor, C.J., Helman, N.C., Yan, S. & Lim, W.A. Using engineered scaffold interactions to reshape MAP kinase pathway signaling dynamics. Science 319, 1539–1543 (2008).

    CAS  PubMed  Google Scholar 

  87. Yokobayashi, Y., Weiss, R. & Arnold, F.H. Directed evolution of a genetic circuit. Proc. Natl. Acad. Sci. USA 99, 16587–16591 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Ro, D.K. et al. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 440, 940–943 (2006).

    CAS  PubMed  Google Scholar 

  89. Keasling, J.D. & Chou, H. Metabolic engineering delivers next-generation biofuels. Nat. Biotechnol. 26, 298–299 (2008).

    CAS  PubMed  Google Scholar 

  90. Atsumi, S., Hanai, T. & Liao, J.C. Non-fermentative pathways for synthesis of branched-chain higher alcohols as biofuels. Nature 451, 86–89 (2008).

    CAS  PubMed  Google Scholar 

  91. de Lorenzo, V. Blueprint of an oil-eating bacterium. Nat. Biotechnol. 24, 952–953 (2006).

    CAS  PubMed  Google Scholar 

  92. Gómez, M.J., Pazos, F., Guijarro, F.J., de Lorenzo, V. & Valencia, A. The environmental fate of organic pollutants through the global microbial metabolism. Mol. Syst. Biol. 3, 114 (2007).

    PubMed  PubMed Central  Google Scholar 

  93. Steinmetz, L.M. et al. Systematic screen for human disease genes in yeast. Nat. Genet. 31, 400–404 (2002).

    CAS  PubMed  Google Scholar 

  94. Isalan, M. et al. Evolvability and hierarchy in rewired bacterial gene networks. Nature 452, 840–845 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. Basu, S., Gerchman, Y., Collins, C.H., Arnold, F.H. & Weiss, R. A synthetic multicellular system for programmed pattern formation. Nature 434, 1130–1134 (2005).

    CAS  PubMed  Google Scholar 

  96. Chen, M.T. & Weiss, R. Artificial cell-cell communication in yeast Saccharomyces cerevisiae using signaling elements from Arabidopsis thaliana. Nat. Biotechnol. 23, 1551–1555 (2005).

    CAS  PubMed  Google Scholar 

  97. Bulter, T. et al. Design of artificial cell-cell communication using gene and metabolic networks. Proc. Natl. Acad. Sci. USA 101, 2299–2304 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Isalan, M., Lemerle, C. & Serrano, L. Engineering gene networks to emulate Drosophila embryonic pattern formation. PLoS Biol. 3, e64 (2005).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank the Institute for Research in Biomedicine (IRB Barcelona) and the Banco Bilbao Vizcaya Argentaria Foundation (BBVA Foundation) for organizing the Barcelona Biomed Conference “Targeting and Tinkering with Interaction Networks.” We also thank R. Weiss (Princeton University) for providing the images for Figure 3 and A. Zanzoni (IRB Barcelona) for help with Figure 1. P.A. acknowledges the financial support received from the Spanish Ministerio de Educación y Ciencia through the grants PSE-010000-2007-1 and BIO2007-62426. We are both supported by the grant 3D-Repertoire from the European Commission under FP6 contract LSHG-CT-2005-512028.

Author information

Authors and Affiliations

  1. European Molecular Biology Laboratory, Meyerhofstrasse 1, 69117, Heidelberg, Germany

    Robert B Russell

  2. Institute for Research in Biomedicine and Barcelona Supercomputing Center, c/ Baldiri i Reixac 10-12, 08028, Barcelona, Spain

    Patrick Aloy

  3. Institució Catalana de Recerca i Estudis Avançats, Pg Lluís Companys 23, 08010, Barcelona, Spain

    Patrick Aloy

Authors
  1. Robert B Russell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Patrick Aloy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Robert B Russell or Patrick Aloy.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Russell, R., Aloy, P. Targeting and tinkering with interaction networks. Nat Chem Biol 4, 666–673 (2008). https://doi.org/10.1038/nchembio.119

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nchembio.119

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing