Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions. | Open Access Researc h Reverse-engineering the Arabidopsis thaliana transcriptional network under changing environmental conditions Javier CarreraH Guillermo Rodrigo Alfonso Jaramillo and Santiago F Elena Addresses Institute de Biología Molecular y Celular de Plantas Consejo Superior de Investigaciones Científicas-UPV Ingeniero Fausto Elio s n 46022 Valencia Spain. TTACA Universidad Politécnica de Valencia Ingeniero Fausto Elio s n 46022 Valencia Spain. Laboratoire de Biochimie École-Polytechnique-CNRS UMR7654 Route de Saclay 91128 Palaiseau France. Epigenomics Project Genopole-Université d Évry Val d Essonne-CNRS UPS3201 523 Terrasses de l Agora 91034 Évry France. The Santa Fe Institute Hyde Park Road Santa Fe NM 87501 USA. n These authors contributed equally to this work. Correspondence Santiago F Elena. Email sfelena@ Published 15 September 2009 Genome Biology 2009 10 R96 doi gb-2009- 10-9-r96 The electronic version of this article is the complete one and can be found online at http 2009 10 9 R96 Received 10 July 2009 Revised 1 September 2009 Accepted 15 September 2009 2009 Carrera et al. licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License http licenses by which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Abstract Background Understanding the molecular mechanisms plants have evolved to adapt their biological activities to a constantly changing environment is an intriguing question and one that requires a systems biology approach. Here we present a network analysis of genome-wide expression data combined with reverse-engineering network modeling to dissect the transcriptional control of Arabidopsis thaliana. The regulatory network is inferred by using an assembly of microarray data containing steady-state RNA expression levels from several growth .
