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.