J. Comput. Biol. - Informational requirements for transcriptional regulation.

Tópicos

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Resumo

Transcription 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.

Resumo Limpo

transcript factor tfs regul transcript bind specif site promot region inform theori provid use mathemat framework analyz bind motif associ tfs impos sever assumpt limit applic specif regulatori scenario explicit simul coevolut tfs bind motif allow studi evolut regulatori network high degre realism work analyz impact differenti regulatori demand inform content tfbind motif mean evolutionari simul general predict index base inform theori valid applic regulatori scenario tf bind signific genom background result show logarithm depend evolv inform content occup target site indic tfs may activ exploit pseudosit modul occup target site regulatori network differenti regul target observ inform content tfbind motif dictat primarili fraction total probabl mass tf assign target site provid predict index estim amount inform associ arbitrarili complex regulatori system observ complex regulatori pattern can exert addit demand evolv inform content given total occup target site find conclus evid effect rang requir bind affin

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