Inferring Mechanisms of Compensation from E-MAP and SGA Data Using Local Search Algorithms for Max Cut

  • Mark D. M. Leiserson
  • Diana Tatar
  • Lenore J. Cowen
  • Benjamin J. Hescott
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6577)


A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.


Genetic Interaction Jaccard Index Synthetic Lethality Bipartite Subgraph Synthetic Lethal Interaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Adams, C., Kamakaka, R.: Chromatin assembly: biochemical identities and genetic redundancy. Current Opinion in Genetics and Development 9, 185–190 (1999)CrossRefGoogle Scholar
  2. 2.
    Bandyopadhyay, S., Kelley, R., Krogan, N.: Functional maps of protein complexes from quantitative genetic interaction data. PLoS Computational Biology (January 2008)Google Scholar
  3. 3.
    Berriz, G.F., King, O.D., Bryant, B., Sander, C., Roth, F.P.: Characterizing gene sets with FuncAssociate. Bioinformatics 19(18), 2502–2504 (2003)CrossRefGoogle Scholar
  4. 4.
    Boone, C., Bussey, H., Andrews, B.J.: Exploring genetic interactions and networks with yeast. Nature Reviews Genetics 8, 437–449 (2007)CrossRefGoogle Scholar
  5. 5.
    Brady, A., Maxwell, K., Daniels, N., Cowen, L.: Fault tolerance in protein interaction networks: Stable bipartite subgraphs and redundant pathways. PLoS ONE 4(4), e5364 (2009)CrossRefGoogle Scholar
  6. 6.
    Callebaut, I., Mornon, J.-P.: From BRCA1 to RAP1: A widespread BRCT module closely associated with DNA repair. FEBS Letters 400, 25–30 (1997)CrossRefGoogle Scholar
  7. 7.
    Carr, A.: DNA structure dependent checkpoints as regulators of DNA repair. DNA Repair 1, 983–994 (2002)CrossRefGoogle Scholar
  8. 8.
    Collins, S., Miller, K., Maas, N., Roguev, A.: Functional dissection of protein complexes involved in yeast chromosome biology using a genetic interaction map. Nature (January 2007)Google Scholar
  9. 9.
    Costanzo, M., Baryshnikova, A., Bellay, J., Kim, Y., Spear, E.D., Sevier, C.S., Ding, H., Koh, J.L.Y., Toufighi, K., Mostafavi, S., Prinz, J., Onge, R.P.S., VanderSluis, B., Makhnevych, T., Vizeacoumar, F.J., Alizadeh, S., Bahr, S., Brost, R.L., Chen, Y., Cokol, M., Deshpande, R., Li, Z., Lin, Z., Liang, W., Marback, M., Paw, J., Luis, B.S., Shuteriqi, E., Tong, A.H.Y., van Dyk, N., Wallace, I.M., Whitney, J.A., Weirauch, M.T., Zhong, G., Zhu, H., Houry, W.A., Brudno, M., Ragibizadeh, S., Papp, B., Pál, C., Roth, F.P., Giaever, G., Nislow, C., Troyanskaya, O.G., Bussey, H., Bader, G.D., Gingras, A., Morris, Q.D., Kim, P.M., Kaiser, C.A., Myers, C.L., Andrews, B.J., Boone, C.: The genetic landscape of a cell. Science 327(5964), 425–431 (2010)CrossRefGoogle Scholar
  10. 10.
    D’Amours, D., Jackson, S.: The MRE11 complex: at the crossroads of DNA repair and checkpoint signalling. Nature Reviews Molecular Cell Biology 3, 317–327 (2002)CrossRefGoogle Scholar
  11. 11.
    Fiedler, D., Braberg, H., Mehta, M., Chechik, G., Cagney, G.: Functional organization of the S. cerevisiae phosphorylation network. Cell (January 2009)Google Scholar
  12. 12.
    Green, E., Antcsak, A., Bailey, A., Franco, A., Wu, K., Yates, J., Kaufman, P.: Replication-independent histone deposition by the HIR complex and asf1. Current Biology 15, 2044–2049 (2005)CrossRefGoogle Scholar
  13. 13.
    Hescott, B.J., Leiserson, M.D.M., Slonim, D.K., Cowen, L.J.: Evaluating between-pathway models with expression data. Journal of Computational Biology 17(3), 477–487 (2010)CrossRefGoogle Scholar
  14. 14.
    Jaccard, P.: Nouvelles recherches sur la distribution florale. Bull. Soc. Vaudoise Sci. Nat. 44, 223–270 (1908)Google Scholar
  15. 15.
    Jaimovich, A., Rinott, R., Schuldiner, M., Margalit, H., Friedman, N.: Modularity and directionality in genetic interaction maps. Bioinformatics 26(12), i228–i236 (2010)CrossRefGoogle Scholar
  16. 16.
    Kelley, D., Kingsford, C.: Extracting between-pathway models from E-MAP interactions using expected graph compression. In: Berger, B. (ed.) RECOMB 2010. LNCS, vol. 6044, pp. 248–262. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Kelley, R., Ideker, T.: Systematic interpretation of genetic interactions using protein networks. Nature Biotechnology 23(5), 561–566 (2005), doi:10.1038/nbt1096 PMID:15877074 CrossRefGoogle Scholar
  18. 18.
    Krogan, N., Keogh, M.-C., Datta, N., Sawa, C., Ryan, O., Ding, H., Haw, R., Pootoolal, J., Tong, A., Canadien, V., Richards, D., Wu, X., Emili, A., Hughes, T., Buratowski, S., Greenblatt, J.: A Snf2 family ATPase complex required for the recruitment of the histone H2A variant Htz1. Molecular Cell 12, 1565–1576 (2003)CrossRefGoogle Scholar
  19. 19.
    Loebl, M.: Efficient maximal cubic graph cuts. In: Leach Albert, J., Monien, B., Rodríguez-Artalejo, M. (eds.) ICALP 1991. LNCS, vol. 510, pp. 351–362. Springer, Heidelberg (1991)CrossRefGoogle Scholar
  20. 20.
    Ma, X., Tarone, A., Li, W.: Mapping genetically compensatory pathways from synthetic lethal interactions in yeast. PLoS One 3(4), e1922 (2008), doi:10.1371/journal.pone.0001922 PMCID: PMC2275788CrossRefGoogle Scholar
  21. 21.
    Pan, X., Ye, P., Tuan, D., Wang, X., Bader, J., Boeke, J.: A DNA integrity network in the yeast Saccharomyces cerevisiae. Cell 124, 1069–1081 (2006)CrossRefGoogle Scholar
  22. 22.
    Poljak, S.: Integer linear programs and local search for max-cut. SIAM J. Comput. 24(4), 822–839 (1995)MathSciNetCrossRefMATHGoogle Scholar
  23. 23.
    Real, R., Vargas, J.: The probabilistic basis of Jaccard’s index of similarity. Syst. Biol. 45(3), 380–385 (1996)CrossRefGoogle Scholar
  24. 24.
    Roguev, A., Bandyopadhyay, S., Zofall, M., Zhang, K., Fischer, T., Collins, S.R., Qu, H., Shales, M., Park, H., Hayles, J., Hoe, K., Kim, D., Ideker, T., Grewal, S.I., Weissman, J.S., Krogan, N.J.: Conservation and rewiring of functional modules revealed by an epistasis map in fission yeast. Science 322(5900), 405–410 (2008)CrossRefGoogle Scholar
  25. 25.
    Schäffer, A., Yannakakis, M.: Simple local search problems that are hard to solve. SIAM J. Comput. 20, 56–87 (1991)MathSciNetCrossRefMATHGoogle Scholar
  26. 26.
    Schuldiner, M., Collins, S.R., Thompson, N.J., Denic, V., Bhamidipati, A., Punna, T., Ihmels, J., Andrews, B., Boone, C., Greenblatt, J.F., Weissman, J.S., Krogan, N.J.: Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile. Cell 123(3), 507–519 (2005)CrossRefGoogle Scholar
  27. 27.
    Stark, C., Breitkreutz, B.-J., Reguly, T., Boucher, L., Breitkreutz, A., Tyers, M.: BioGRID: a general repository for interaction datasets. Nucleic Acids Research 34(suppl 1), D535–D539 (2005)Google Scholar
  28. 28.
    Taipale, M., Jarosz, D., Lindquist, S.: HSP90 at the hub of protein homeostasis: emerging mechanistic insights. Nature Reviews Molecular Cell Biology 11, 515–528 (2010)CrossRefGoogle Scholar
  29. 29.
    Tong, A.H.Y., Lesage, G., Bader, G.D., Ding, H., Xu, H., Xin, X., Young, J., Berriz, G.F., Brost, R.L., Chang, M., Chen, Y., Cheng, X., Chua, G., Friesen, H., Goldberg, D.S., Haynes, J., Humphries, C., He, G., Hussein, S., Ke, L., Krogan, N., Li, Z., Levinson, J.N., Lu, H., Menard, P., Munyana, C., Parsons, A.B., Ryan, O., Tonikian, R., Roberts, T., Sdicu, A.-M., Shapiro, J., Sheikh, B., Suter, B., Wong, S.L., Zhang, L.V., Zhu, H., Burd, C.G., Munro, S., Sander, C., Rine, J., Greenblatt, J., Peter, M., Bretscher, A., Bell, G., Roth, F.P., Brown, G.W., Andrews, B., Bussey, H., Boone, C.: Global mapping of the yeast genetic interaction network. Science 303(5659), 808–813 (2004)CrossRefGoogle Scholar
  30. 30.
    Ulitsky, I., Krogan, N., Shamir, R.: Towards accurate imputation of quantitative genetic interactions. Genome Biology (January 2009)Google Scholar
  31. 31.
    Ulitsky, I., Shamir, R.: Pathway redundancy and protein essentiality revealed in the S. cerevisiae interaction networks. Molecular Systems Biology 3(104) (2007), PMCID: PMC1865586Google Scholar
  32. 32.
    Ulitsky, I., Shlomi, T., Kupiec, M., Shamir, R.: From E-MAPs to module maps: dissecting quantitative genetic interactions using physical interactions. Molecular Systems Biology (January 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mark D. M. Leiserson
    • 1
  • Diana Tatar
    • 1
  • Lenore J. Cowen
    • 1
  • Benjamin J. Hescott
    • 1
  1. 1.Department of Computer ScienceTufts UniversityMedfordUSA

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