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International Conference on Algorithms for Computational Biology

AlCoB 2015: Algorithms for Computational Biology pp 53–67Cite as

StreaM - A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9199))

Abstract

Determining the occurrence of motifs yields profound insight for many biological systems, like metabolic, protein-protein interaction, and protein structure networks. Meaningful spatial protein-structure motifs include enzyme active sites and ligand-binding sites which are essential for function, shape, and performance of an enzyme. Analyzing their dynamics over time leads to a better understanding of underlying properties and processes. In this work, we present StreaM, a stream-based algorithm for counting undirected 4-vertex motifs in dynamic graphs. We evaluate StreaM against the four predominant approaches from the current state of the art on generated and real-world datasets, a simulation of a highly dynamic enzyme. For this case, we show that StreaM is capable to capture essential molecular protein dynamics and thereby provides a powerful method for evaluating large molecular dynamics trajectories. Compared to related work, our approach achieves speedups of up to 2, 300 times on real-world datasets.

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Notes

  1. 1.

    https://github.com/BenjaminSchiller/DNA.

  2. 2.

    http://theinf1.informatik.uni-jena.de/~wernicke/motifs/.

  3. 3.

    http://lbb.ut.ac.ir/Download/LBBsoft/Kavosh/.

  4. 4.

    http://www.dcc.fc.up.pt/gtries/.

  5. 5.

    http://www.ft.unicamp.br/docentes/meira/accmotifs/.

References

  1. Albert, I., Albert, R.: Conserved network motifs allow protein-protein interaction prediction. Bioinformatics 20(18), 3346–52 (2004)

    Article  Google Scholar 

  2. Alder, B.J., Wainwright, T.E.: Studies in molecular dynamics. J. Chem. Phys. 31(2), 459–466 (1959)

    Article  MathSciNet  Google Scholar 

  3. Alon, N., et al.: Biomolecular network motif counting and discovery by color coding. Bioinformatics 24(13), 241–249 (2008)

    Article  Google Scholar 

  4. Atilgan, A.R., et al.: Anisotropy of fluctuation dynamics of proteins with an elastic network model. Biophys. J. 80(1), 505–515 (2001)

    Article  Google Scholar 

  5. Biemann, C., et al.: Quantifying semantics using complex network analysis. In: COLING (2012)

    Google Scholar 

  6. Chakraborty, S., Biswas, S.: Approximation algorithms for 3-D common substructure identification in drug and protein molecules. In: Dehne, F., Gupta, A., Sack, J.-R., Tamassia, R. (eds.) WADS 1999. LNCS, vol. 1663, pp. 253–264. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  7. Chen, J., et al.: Nemofinder: Dissecting genome-wide protein-protein interactions with meso-scale network motifs. In: ACM SIGKDD (2006)

    Google Scholar 

  8. Chen, J., et al.: Labeling network motifs in protein interactomes for protein function prediction. In: IEEE ICDE (2007)

    Google Scholar 

  9. Colak, R., et al.: Dense graphlet statistics of protein interaction and random networks. In: Pacific Symposium on Biocomputing (2009)

    Google Scholar 

  10. Duan, Y., et al.: A point-charge force field for molecular mechanics simulations of proteins based on condensed-phase quantum mechanical calculations. J. Comput. Chem. 24(16), 1999–2012 (2003)

    Article  Google Scholar 

  11. Ediger, D., et al.: Massive streaming data analytics: a case study with clustering coefficients. In: IEEE IPDPSW (2010)

    Google Scholar 

  12. Feldman, D., Shavitt, Y.: Automatic large scale generation of internet pop level maps. In: IEEE GLOBECOM (2008)

    Google Scholar 

  13. Feldman, D., et al.: A structural approach for pop geo-location. Comput. Netw. 56, 1029–1040 (2012)

    Article  Google Scholar 

  14. Gonen, M., Shavitt, Y.: Approximating the number of network motifs. Internet Math. 6(3), 349–372 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  15. Hales, D., Arteconi, S.: Motifs in evolving cooperative networks look like protein structure networks. Netw. Heterogen. Media 3(2), 239–249 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  16. Hutchinson, E.G., Thornton, J.M.: Promotif– program to identify and analyze structural motifs in proteins. Protein Sci. 5(2), 212–220 (1996)

    Article  Google Scholar 

  17. Jurgens, D., Lu, T.: Temporal motifs reveal the dynamics of editor interactions in wikipedia. In: ICWSM (2012)

    Google Scholar 

  18. Kalir, S., et al.: Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science 292(5524), 2080–2083 (2001)

    Article  Google Scholar 

  19. Kashani, Z.R.M., et al.: Kavosh: a new algorithm for finding network motifs. BMC Bioinformatics, 10(1) (2009)

    Google Scholar 

  20. Kashtan, N., et al.: Mfinder tool guide. Technical report (2002)

    Google Scholar 

  21. Kim, J., et al.: Coupled feedback loops form dynamic motifs of cellular networks. Biophys. J. 94(2), 359–365 (2008)

    Article  Google Scholar 

  22. Kleywegt, D.J.: Recognition of spatial motifs in protein structures. J. Mol. Biol. 285(4), 1887–1897 (1999)

    Article  Google Scholar 

  23. Kovanen, L., et al.: Temporal motifs in time-dependent networks. Journal of Statistical Mechanics: Theory and Experiment (2011)

    Google Scholar 

  24. Krieger, E., et al.: Increasing the precision of comparative models with YASARA NOVA-a self-parameterizing force field. Proteins 47, 393–402 (2002)

    Article  Google Scholar 

  25. Krieger, E., et al.: Fast empirical pKa prediction by Ewald summation. Journal of molecular graphics & modelling (2006)

    Google Scholar 

  26. Krumov, L., et al.: Leveraging network motifs for the adaptation of structured peer-to-peer-networks. In: IEEE GLOBECOM (2010)

    Google Scholar 

  27. Maslov, S., Sneppen, K.: Specificity and stability in topology of protein networks. Science 296, 910–913 (2002)

    Article  Google Scholar 

  28. Meira, L.A.A., et al.: acc-motif detection tool (2012). arXiv:1203.3415

  29. Michels, A., et al.: Verwendung von esterasen zur spaltung von kunststoffen (2011)

    Google Scholar 

  30. Milenkoviæ, T., Pržulj, N.: Uncovering biological network function via graphlet degree signatures. Cancer Inform. 6, 257–273 (2008)

    Google Scholar 

  31. Milo, R., et al.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)

    Article  Google Scholar 

  32. Miyazawa, S., Jernigan, R.L.: Residue-residue potentials with a favorable contact pair term and an unfavorable high packing density term, for simulation and threading. J. Mol. Biol. 256, 623–644 (1996)

    Article  Google Scholar 

  33. Panni, S., Rombo, S.E.: Searching for repetitions in biological networks: methods, resources and tools. Briefings Bioinform. 16(1), 118–136 (2015)

    Article  Google Scholar 

  34. Rauch, M., et al.: Computing on data streams. In: DIMACS Workshop External Memory and Visualization (1999)

    Google Scholar 

  35. Ribeiro, P., Silva, F.: G-tries: an efficient data structure for discovering network motifs. In: ACM Symposium on Applied Computing (2010)

    Google Scholar 

  36. Royer, L., et al.: Unraveling protein networks with power graph analysis. PLoS Comput. Biol. 4(7) (2008)

    Google Scholar 

  37. Schatz, M., et al.: Parallel network motif finding. University of Maryland, Technical report (2008)

    Google Scholar 

  38. Schiller, B., Strufe, T.: Dynamic network analyzer building a framework for the graph-theoretic analysis of dynamic networks. In: SummerSim (2013)

    Google Scholar 

  39. Schreiber, F., Schwöbbermeyer, H.: Mavisto: a tool for the exploration of network motifs. Bioinformatics, 21( 9 ), 2076–2082 (2005)

    Google Scholar 

  40. Shen-Orr, S.S., et al.: Network motifs in the transcriptional regulation network of escherichia coli. Nature Genet 31, 64–68 (2002)

    Article  Google Scholar 

  41. Tran, N., et al.: Counting motifs in the human interactome. Nature Communications (2013)

    Google Scholar 

  42. Wernicke, S.: Efficient detection of network motifs. IEEE ACM TCBB 3(4), 321–322 (2006)

    Google Scholar 

  43. Wernicke, S., Rasche, F.: Fanmod: a tool for fast network motif detection. Bioinformatics 22(9), 1152–1153 (2006)

    Article  Google Scholar 

  44. Zhao, Z., et al.: Subgraph enumeration in large social contact networks using parallel color coding and streaming. In: ICPP (2010)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Privacy and Data Security, Department of Computer Science, TU Dresden, Dresden, Germany

    Benjamin Schiller & Thorsten Strufe

  2. Computational Biology and Simulation, Department of Biology, TU Darmstadt, Darmstadt, Germany

    Sven Jager & Kay Hamacher

  3. Department of Physics, Department of Computer Science, TU Darmstadt, Darmstadt, Germany

    Kay Hamacher

Authors
  1. Benjamin Schiller
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  2. Sven Jager
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  3. Kay Hamacher
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  4. Thorsten Strufe
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Corresponding author

Correspondence to Benjamin Schiller .

Editor information

Editors and Affiliations

  1. Research Group on Mathematical Linguistics, Rovira i Virgili University, Tarragona, Spain

    Adrian-Horia Dediu

  2. Faculty of Science, National Autonomous University of Mexico - UNAM, Mexico City, Mexico

    Francisco Hernández-Quiroz

  3. Research Group on Mathematical Linguistics, Rovira i Virgili University, Tarragona, Spain

    Carlos Martín-Vide

  4. Institute for Research in Applied Mathematics and Systems – IIMAS, National Autonomous University of Mexico - UNAM, Mexico City, Mexico

    David A. Rosenblueth

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© 2015 Springer International Publishing Switzerland

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Schiller, B., Jager, S., Hamacher, K., Strufe, T. (2015). StreaM - A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs. In: Dediu, AH., Hernández-Quiroz, F., Martín-Vide, C., Rosenblueth, D. (eds) Algorithms for Computational Biology. AlCoB 2015. Lecture Notes in Computer Science(), vol 9199. Springer, Cham. https://doi.org/10.1007/978-3-319-21233-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-21233-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-21232-6

  • Online ISBN: 978-3-319-21233-3

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