Abstract
Little is known about the design principles1,2,3,4,5,6,7,8,9,10 of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis2,11,12, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams1,2,3,4,5,6,7,8,9,10,13, we sought to break down such networks into basic building blocks2. We generalize the notion of motifs, widely used for sequence analysis, to the level of networks. We define 'network motifs' as patterns of interconnections that recur in many different parts of a network at frequencies much higher than those found in randomized networks. We applied new algorithms for systematically detecting network motifs to one of the best-characterized regulation networks, that of direct transcriptional interactions in Escherichia coli3,6. We find that much of the network is composed of repeated appearances of three highly significant motifs. Each network motif has a specific function in determining gene expression, such as generating temporal expression programs and governing the responses to fluctuating external signals. The motif structure also allows an easily interpretable view of the entire known transcriptional network of the organism. This approach may help define the basic computational elements of other biological networks.
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References
Bray, D. Protein molecules as computational elements in living cells. Nature 376, 307–312 (1995).
Hartwell, L.H., Hopfield, J.J., Leibler, S. & Murray, A.W. From molecular to modular cell biology. Nature 402, C47–52 (1999).
Thieffry, D., Huerta, A.M., Perez-Rueda, E. & Collado-Vides, J. From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli. Bioessays 20, 433–440 (1998).
McAdams, H.H. & Arkin, A. Simulation of prokaryotic genetic circuits. Annu. Rev. Biophys. Biomol. Struct. 27, 199–224 (1998).
McAdams, H.H. & Shapiro, L. Circuit simulation of genetic networks. Science 269, 650–656 (1995).
Savageau, M. & Neidhart, F.C. Regulation beyond the operon. in Escherichia coli and Salmonella: Cellular and Molecular Biology (ed. Neidhart, F.C.) 1310–1324 (American Society for Microbiology, Washington D.C., 1996).
Strogatz, S.H. Exploring complex networks. Nature 410, 268–276 (2001).
Rao, C.V. & Arkin, A.P. Control motifs for intracellular regulatory networks. Annu. Rev. Biomed. Eng. 3, 391–419 (2001).
Kauffman, S.A. Metabolic stability and epigenesis in randomly constructed genetic nets. J. Theor. Biol. 22, 437–467 (1969).
Barabasi, A.L. & Albert, R. Emergence of scaling in random networks. Science 286, 509–512 (1999).
Hughes, J.D., Estep, P.W., Tavazoie, S. & Church, G.M. Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae. J. Mol. Biol. 296, 1205–1214 (2000).
Hartemink, A.J., Gifford, D.K., Jaakkola, T.S. & Young, R.A. Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks. Pac. Symp. Biocomput., 422–433 (2001).
Newman, M.E., Strogatz, S.H. & Watts, D.J. Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E 64, 026118 (2001).
Salgado, H. et al. RegulonDB (version 3.2): transcriptional regulation and operon organization in Escherichia coli K-12. Nucleic Acids Res. 29, 72–74 (2001).
Schleif, R. Regulation of the L-arabinose operon of Escherichia coli. Trends Genet. 16, 559–565 (2000).
Hengge-Aronis, R. Survival of hunger and stress: the role of rpoS in early stationary phase gene regulation in E. coli. Cell 72, 165–168 (1993).
Yuh, C.H., Bolouri, H. & Davidson, E.H., Genomic cis-regulatory logic: experimental and computational analysis of a sea urchin gene. Science 279, 1896–1902 (1998).
Kalir, S. et al. Ordering genes in a flagella pathway by analysis of expression kinetics from living bacteria. Science 292, 2080–2083 (2001).
Jeong, H., Tombor, B., Albert, R., Oltvai, Z.N. & Barabasi, A.L. The large-scale organization of metabolic networks. Nature 407, 651–654 (2000).
Duda, R.O. & Hart, P.E. Pattern Classification and Scene Analysis (Wiley, New York, 1973).
Kannan, R., Tetali, P. & Vempala, S., Markov-chain algorithms for generating bipartite graphs and tournaments. Random Structures and Algorithms 14, 293–308 (1999).
Acknowledgements
We thank J. Collado-Vides and the RegulonDB team for making their invaluable database available. We thank A. Arkin, H.C. Berg, J. Doyle, M. Elowitz, S. Leibler, S. Quake, J. Shapiro, M.G. Surette, B. Shilo, E. Winfree and all members of our lab for discussions. This work was supported by the Israel Science Foundation and the Minerva Foundation.
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Shen-Orr, S., Milo, R., Mangan, S. et al. Network motifs in the transcriptional regulation network of Escherichia coli. Nat Genet 31, 64–68 (2002). https://doi.org/10.1038/ng881
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DOI: https://doi.org/10.1038/ng881
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