Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/10495/28380
Título : Development and implementation of a methodology for the inference of a Streptomyces coelicolor gene regulatory network from genomic and transcriptomic data
Autor : Zorro Aranda, Dolly Andrea
metadata.dc.contributor.advisor: Freyre González, Julio Augusto
metadata.dc.subject.*: Streptomyces coelicolor
Redes reguladoras de genes
Gene Regulatory Networks
Redes
Inferencia
Fecha de publicación : 2022
Resumen : ABSTRACT : Streptomyces coelicolor A3(2) is a model microorganism for the study of Streptomycetes, antibiotic production, and secondary metabolism in general. However, little effort to globally study its transcription has been made even though S. coelicolor has an outstanding variety of regulators among bacteria. In this work, we aim to reconstruct a Gene Regulatory Network (GRN) for S. coelicolor. For this, we manually curated experimentally validated gene regulatory interactions from which we reconstruct a curated network. Next, based on this curation, we inferred a complete regulation network applying different mathematical methods from two different approaches. One approach was motif detection in DNA sequences and the other one was an inference from transcriptomic data. Further, we analyze the structural properties and functional architecture of both curated and inferred networks. And we compared them to assess the reliability of the predictions. From this analysis, we proposed the functional annotation and biological function for some genes of S. coelicolor. Moreover, we proposed the Natural Decomposition Approach as a methodology for the assessment of GRN inference. Finally, we present applications for the curated and inferred networks. The curated networks were deposited in the Abasy Atlas database while the inferences and additional information are available in the supplementary file.
Aparece en las colecciones: Doctorados de la Facultad de Ingeniería

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ZorroDolly_2022_InferenciaRedScoelicolor.pdfTesis doctoral5.44 MBAdobe PDFVisualizar/Abrir
Anexo1.xlsxAnexo1.84 MBMicrosoft Excel XMLVisualizar/Abrir


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