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Báo cáo y học: "Functional architecture of Escherichia coli: new insights provided by a natural decomposition approach"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Minireview cung cấp cho các bạn kiến thức về ngành y đề tài: IFunctional architecture of Escherichia coli: new insights provided by a natural decomposition approach. | Open Access Functional architecture of Escherichia coli new insights provided by a natural decomposition approach Julio A Freyre-Gonzalez José A Alonso-Pavón Luis G Trevino-Quintanilla and Julio Collado-Vides Address Programa de Genómica Computational Centro de Ciencias Genómicas Universidad Nacional Autónoma de México. Av. Universidad s n Col. Chamilpa 62210 Cuernavaca Morelos México. Correspondence Julio A Freyre-González. Email jfreyre@ccg.unam.mx. Julio Collado-Vides. Email collado@ccg.unam.mx Published 27 October 2008 Received 28 September 2008 Genome Biology 2008 9 R154 doi 10.1186 gb-2008-9- 10-r 154 Accepted 27 October 2008 The electronic version of this article is the complete one and can be found online at http genomebiology.com 2008 9 10 R154 2008 Freyre-González et al. 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. Abstract Background Previous studies have used different methods in an effort to extract the modular organization of transcriptional regulatory networks. However these approaches are not natural as they try to cluster strongly connected genes into a module or locate known pleiotropic transcription factors in lower hierarchical layers. Here we unravel the transcriptional regulatory network of Escherichia coll by separating it into its key elements thus revealing its natural organization. We also present a mathematical criterion based on the topological features of the transcriptional regulatory network to classify the network elements into one of two possible classes hierarchical or modular genes. Results We found that modular genes are clustered into physiologically correlated groups validated by a statistical analysis of the enrichment of the functional classes. Hierarchical genes encode .