10779/crick.12619970.v1 Marco Del Giudice Marco Del Giudice Stefano Bo Stefano Bo Silvia Grigolon Silvia Grigolon Carla Bosia Carla Bosia On the role of extrinsic noise in microRNA-mediated bimodal gene expression The Francis Crick Institute 2020 Computational Biology Computer Simulation Gene Expression Regulation Gene Regulatory Networks Humans MicroRNAs Models, Genetic Models, Statistical Normal Distribution Phenotype Protein Biosynthesis Protein Stability RNA, Messenger Stochastic Processes Transcription, Genetic Salbreux FC001317 06 Biological Sciences 08 Information and Computing Sciences 01 Mathematical Sciences Bioinformatics 2020-07-15 10:54:10 Journal contribution https://crick.figshare.com/articles/journal_contribution/On_the_role_of_extrinsic_noise_in_microRNA-mediated_bimodal_gene_expression/12619970 Several studies highlighted the relevance of extrinsic noise in shaping cell decision making and differentiation in molecular networks. Bimodal distributions of gene expression levels provide experimental evidence of phenotypic differentiation, where the modes of the distribution often correspond to different physiological states of the system. We theoretically address the presence of bimodal phenotypes in the context of microRNA (miRNA)-mediated regulation. MiRNAs are small noncoding RNA molecules that downregulate the expression of their target mRNAs. The nature of this interaction is titrative and induces a threshold effect: below a given target transcription rate almost no mRNAs are free and available for translation. We investigate the effect of extrinsic noise on the system by introducing a fluctuating miRNA-transcription rate. We find that the presence of extrinsic noise favours the presence of bimodal target distributions which can be observed for a wider range of parameters compared to the case with intrinsic noise only and for lower miRNA-target interaction strength. Our results suggest that combining threshold-inducing interactions with extrinsic noise provides a simple and robust mechanism for obtaining bimodal populations without requiring fine tuning. Furthermore, we characterise the protein distribution's dependence on protein half-life.