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Fig. 4 | Genome Biology

Fig. 4

From: RNAs competing for microRNAs mutually influence their fluctuations in a highly non-linear microRNA-dependent manner in single cells

Fig. 4

Retroactivity increases cell-to-cell variability. a, b mCherry total noise, quantified by its coefficient of variation (CV, a proxy for \({CV}_{p_{1}}\) in Fig. 1 c), is plotted against eYFP (a proxy for the constitutive expression p 0 in the model). The black arrow identifies the model-predicted threshold shown in Fig. 2. Error bars are evaluated on the biological replicates. CV globally increases on increasing the number of mCherry MREs (a) while it decreases on increasing the number of mCerulean MREs (b). The competition between these two strengths results in lowering the noise even if the expected repression from the rough number of mCherry MREs is high. Histograms in the lower panels show mCherry data distributions for the shaded regions in (a) and (b). A strong miRNA target repression strength increases cell-to-cell variability with the eventual appearance of different phenotypes (bimodal distributions). Purple and cyan circles in legends represent the plasmids coding for mCherry and mCerulean fluorophores, respectively. a.u. arbitrary units, CV coefficient of variation, eYFP enhanced yellow fluorescent protein, MRE miRNA regulatory element

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