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    Home > Biochemistry News > Biotechnology News > Characterization of Complex Regulatory Networks and Identification of Promoter Regulatory Elements in Yeast: In Silico and Wet-Lab Approaches

    Characterization of Complex Regulatory Networks and Identification of Promoter Regulatory Elements in Yeast: In Silico and Wet-Lab Approaches

    • Last Update: 2021-01-07
    • Source: Internet
    • Author: User
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    Transcription is the first step in the flow of biological information from genome to proteome and its tight regulation is a crucial checkpoint in most biological processes occurring in all living organisms. In eukaryotes, one of the most important mechanisms of transcriptional regulation relies on the activity of transcription factors which, upon binding to specific nucleotide motifs (consensus) present in the promoter region of target genes, modulate the activity of RNA polymerase II activating and/or repressing gene transcription. The identification of binding sites for these transcription factors is crucial to the understanding of transcriptional regulation at the molecular level and to the prediction of putative target genes for each transcription factor. However, transcription regulation cannot simply be reduced to transcription factor-gene associations. Frequently, the transcript level of a given gene is determined by a multitude of activators and/or repressors resulting in intertwined and complex regulatory networks. Two case studies dedicated to the study of transcriptional regulation in the experimental model
    Saccharomyces cerevisiae
    are presented in this chapter. The computational tools available in YEASTRACT information system are explored in both studies, to identify the regulatory elements that serve as functional
    DNA
    -binding sites for a transcription factor (Rim101p), and to characterize the regulatory network underlying the transcriptional regulation of a given yeast gene (
    FLR1
    ). A set of easily accessible experimental approaches that can be used to confirm the predictions of the bioinformatic analysis is also detailed.
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