1,310
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Network motifs in the transcriptional regulation network of Escherichia coli

      ,   , ,
      Nature Genetics
      Springer Science and Business Media LLC

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Little is known about the design principles of transcriptional regulation networks that control gene expression in cells. Recent advances in data collection and analysis, however, are generating unprecedented amounts of information about gene regulation networks. To understand these complex wiring diagrams, we sought to break down such networks into basic building blocks. 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 coli. 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.

          Related collections

          Most cited references20

          • Record: found
          • Abstract: found
          • Article: not found

          Emergence of Scaling in Random Networks

          Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mechanisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Exploring complex networks.

            S Strogatz (2001)
            The study of networks pervades all of science, from neurobiology to statistical physics. The most basic issues are structural: how does one characterize the wiring diagram of a food web or the Internet or the metabolic network of the bacterium Escherichia coli? Are there any unifying principles underlying their topology? From the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems-be they neurons, power stations or lasers-will behave collectively, given their individual dynamics and coupling architecture. Researchers are only now beginning to unravel the structure and dynamics of complex networks.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Computational identification of cis-regulatory elements associated with groups of functionally related genes in Saccharomyces cerevisiae.

              AlignACE is a Gibbs sampling algorithm for identifying motifs that are over-represented in a set of DNA sequences. When used to search upstream of apparently coregulated genes, AlignACE finds motifs that often correspond to the DNA binding preferences of transcription factors. We previously used AlignACE to analyze whole genome mRNA expression data. Here, we present a more detailed study of its effectiveness as applied to a variety of groups of genes in the Saccharomyces cerevisiae genome. Published functional catalogs of genes and sets of genes grouped by common name provided 248 groups, resulting in 3311 motifs. In conjunction with this analysis, we present measures for gauging the tendency of a motif to target a given set of genes relative to all other genes in the genome and for gauging the degree to which a motif is preferentially located in a certain distance range upstream of translational start sites. We demonstrate improved methods for comparing and clustering sequence motifs. Many previously identified cis-regulatory elements were found. We also describe previously unidentified motifs, one of which has been verified by experiments in our laboratory. An extensive set of AlignACE runs on randomly selected sets of genes and on sets of genes whose upstream regions contain known transcription factor binding sites serve as controls. Copyright 2000 Academic Press.
                Bookmark

                Author and article information

                Journal
                Nature Genetics
                Nat Genet
                Springer Science and Business Media LLC
                1061-4036
                1546-1718
                May 2002
                April 22 2002
                May 2002
                : 31
                : 1
                : 64-68
                Article
                10.1038/ng881
                11967538
                93da6d89-e3f1-4bca-97b1-42f97d93c749
                © 2002

                http://www.springer.com/tdm

                History

                Comments

                Comment on this article