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      How biologically relevant are interaction-based modules in protein networks?

      research-article
      1 , , 2
      Genome Biology
      BioMed Central

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          Abstract

          The authors present a method to identify modules within protein-interaction networks. Phylogenetic profiles are used to determine the biological relevance of the modules.

          Abstract

          By applying a graph-based algorithm to yeast protein-interaction networks we have extracted modular structures and show that they can be validated using information from the phylogenetic conservation of the network components. We show that the module cores, the parts with the highest intramodular connectivity, are biologically relevant components of the networks. These constituents correlate only weakly with other levels of organization. We also discuss how such structures could be used for finding targets for antimicrobial drugs.

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          Most cited references23

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          Network motifs in the transcriptional regulation network of Escherichia coli

          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.
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            A gene-coexpression network for global discovery of conserved genetic modules.

            To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
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              • Article: not found

              Transcriptional regulatory networks in Saccharomyces cerevisiae.

              We have determined how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells. Just as maps of metabolic networks describe the potential pathways that may be used by a cell to accomplish metabolic processes, this network of regulator-gene interactions describes potential pathways yeast cells can use to regulate global gene expression programs. We use this information to identify network motifs, the simplest units of network architecture, and demonstrate that an automated process can use motifs to assemble a transcriptional regulatory network structure. Our results reveal that eukaryotic cellular functions are highly connected through networks of transcriptional regulators that regulate other transcriptional regulators.
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                Author and article information

                Journal
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1465-6906
                1465-6914
                2004
                1 November 2004
                : 5
                : 11
                : R93
                Affiliations
                [1 ]Evolutionary Systems Biology Initiative, Structural and Computational Biology Program, Spanish National Cancer Center (CNIO), Melchor Fernández Almagro 3, 28029 Madrid, Spain
                [2 ]Department of Biology and Biochemistry, University of Bath, Bath BA2 7AY, UK
                Article
                gb-2004-5-11-r93
                10.1186/gb-2004-5-11-r93
                545784
                15535869
                f82ada29-cd8c-4eb2-ac0f-416d9b97ba15
                Copyright © 2004 Poyatos and Hurst; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 17 July 2004
                : 31 August 2004
                : 1 October 2004
                Categories
                Method

                Genetics
                Genetics

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