Abstract
Motifs in a network are small connected subnetworks that occur in significantly higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Kashtan et al. [Bioinformatics, 2004] proposed a sampling algorithm for efficiently performing the computationally challenging task of detecting network motifs. However, among other drawbacks, this algorithm suffers from sampling bias and is only efficient when the motifs are small (3 or 4 nodes). Based on a detailed analysis of the previous algorithm, we present a new algorithm for network motif detection which overcomes these drawbacks. Experiments on a testbed of biological networks show our algorithm to be orders of magnitude faster than previous approaches. This allows for the detection of larger motifs in bigger networks than was previously possible, facilitating deeper insight into the field.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Albert, I., Albert, R.: Conserved network motifs allow protein-protein interaction prediction. Bioinformatics 20(18), 3346–3352 (2004)
Artzy-Randrup, Y., Fleishman, S.J., Ben-Tal, N., Stone, L.: Comment on network motifs: Simple building blocks of complex networks and superfamilies of designed and evolved networks. Science 305, 1007c (2004)
Bender, E.A.: The asymptotic number of non-negative matrices with given row and column sums. Disc. Appl. Math. 10, 217–223 (1974)
Bender, E.A., Canfield, E.R.: The asymptotic number of labeled graphs with given degree sequences. J. Comb. Theor. A 24, 296–307 (1978)
Berg, J., Lässig, M.: Local graph alignment and motif search in biological networks. PNAS 101(41), 14689–14694 (2004)
Duke, R.A., Lefmann, H., Rödl, V.: A fast approximation algorithm for computing the frequencies of subgraphs in a given graph. SIAM J. Comp. 24(3), 598–620 (1995)
Itzkovitz, S., Levitt, R., Kashtan, N., et al.: Coarse-graining and self-dissimilarity of complex networks. Phys. Rev. E 71(016127) (2005)
Itzkovitz, S., Milo, R., Kashtan, N., et al.: Subgraphs in random networks. Phys. Rev. E 68(26127) (2003)
Kashtan, N., Itzkovitz, S., Milo, R., Alon, U.: Efficient sampling algorithm for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20(11), 1746–1758 (2004)
Knuth, D.E.: Estimating the efficiency of backtrack programs. In: Selected papers on Analysis of Algorithms. Stanford Junior University, Palo Alto (2000)
Lee, T.I., Rinaldi, N.J., Robert, F., et al.: Transcriptional regulatory networks in Saccharomyces Cerevisiae. Science 298, 799–804 (2002)
McKay, B.D.: Practical graph isomorphism. Congr. Numer. 30, 45–87 (1981)
Milo, R., Itzkovitz, S., Kashtan, N., et al.: Response to comment on network motifs: Simple building blocks of complex networks and superfamilies of designed and evolved networks. Science 305, 1007d (2004)
Milo, R., Itzkovitz, S., Kashtan, N., et al.: Superfamilies of designed and evolved networks. Science 303(5663), 1538–1542 (2004)
Milo, R., Shen-Orr, S.S., Itzkovitz, S., et al.: Network motifs: Simple building blocks of complex networks. Science 298(5594), 824–827 (2002)
Newman, M.E.J., Strogatz, S.H., Watts, D.J.: Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64(026118) (2001)
Ott, S., Hansen, A., Kim, S., Miyano, S.: Superiority of network motifs over optimal networks and an application to the revelation of gene network evolution. Bioinformatics 21(2), 227–238 (2005)
Shen-Orr, S.S., Milo, R., Mangan, S., Alon, U.: Network motifs in the transcriptional regulation network of Escherichia Coli. Nature Gen. 31(1), 64–68 (2002)
Vázquez, A., Dobrin, R., Sergi, D., et al.: The topological relationship between the large-scale attributes and local interaction patterns of complex networks. PNAS 101(52), 17940–17945 (2004)
Vespignani, A.: Evolution thinks modular. Nature Gen 35(2), 118–119 (2003)
Williams, R.J., Martinez, N.D.: Simple rules yield complex food webs. Nature 404, 180–183 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wernicke, S. (2005). A Faster Algorithm for Detecting Network Motifs. In: Casadio, R., Myers, G. (eds) Algorithms in Bioinformatics. WABI 2005. Lecture Notes in Computer Science(), vol 3692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11557067_14
Download citation
DOI: https://doi.org/10.1007/11557067_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29008-7
Online ISBN: 978-3-540-31812-5
eBook Packages: Computer ScienceComputer Science (R0)