Browsing by Subject "binding sites"
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Item A parallel simulation of the evolution of transcription factor binding sites(2011-05-27) Forder, RobertThe analysis of transcription factor binding motifs may aid in understanding the process by which transcription factors recognize their binding sites. We wish to investigate the likelihood that transcription factors use correlation between positions in potential binding sites as a criteria for recognition. We implement a genetic algorithm in parallel using a simple server-client organization to simulate the evolution of these binding sites. We then evaluate the performance of this application and conclude that exhibits excellent speedup and efficiency.Item Informational Requirements for Transcriptional Regulation(Mary Ann Liebert, 2014-05-01) O'Neill, Patrick K.; Forder, Robert; Erill, IvanTranscription factors (TFs) regulate transcription by binding to specific sites in promoter regions. Information theory provides a useful mathematical framework to analyze the binding motifs associated with TFs but imposes several assumptions that limit their applicability to specific regulatory scenarios. Explicit simulations of the co-evolution of TFs and their binding motifs allow the study of the evolution of regulatory networks with a high degree of realism. In this work we analyze the impact of differential regulatory demands on the information content of TF-binding motifs by means of evolutionary simulations. We generalize a predictive index based on information theory, and we validate its applicability to regulatory scenarios in which the TF binds significantly to the genomic background. Our results show a logarithmic dependence of the evolved information content on the occupancy of target sites and indicate that TFs may actively exploit pseudo-sites to modulate their occupancy of target sites. In regulatory networks with differentially regulated targets, we observe that information content in TF-binding motifs is dictated primarily by the fraction of total probability mass that the TF assigns to its target sites, and we provide a predictive index to estimate the amount of information associated with arbitrarily complex regulatory systems. We observe that complex regulatory patterns can exert additional demands on evolved information content, but, given a total occupancy for target sites, we do not find conclusive evidence that this effect is because of the range of required binding affinities.