Buffering gene expression noise by microRNA based feedforward regulation

Abstract

Cells use various regulatory motifs, including feedforward loops, to control the intrinsic noise that arises in gene expression at low copy numbers. Here we study one such system, which is broadly inspired by the interaction between an mRNA molecule and an antagonistic mi-croRNA molecule encoded by the same gene. The two reaction species are synchronously produced, individually degraded, and the second species (microRNA) exerts an antagonistic pressure on the first species (mRNA). Using linear-noise approximation, we show that the noise in the first species, which we quantify by the Fano factor, is sub-Poissonian, and exhibits a nonmonotonic response both to the species lifetime ratio and to the strength of the antagonistic interaction. Additionally, we use the Chemical Reaction Network Theory to prove that the first species distribution is Poissonian if the first species is much more stable than the second. Finally, we identify a special parametric regime, supporting a broad range of behaviour, in which the distribution can be analytically described in terms of the confluent hypergeometric limit function. We verify our analysis against large-scale kinetic Monte Carlo simulations. Our results indicate that, subject to specific physiological constraints, optimal parameter values can be found within the mRNA-microRNA motif that can benefit the cell by lowering the gene-expression noise.

From Endogenous to Synthetic microRNA-Mediated Regulatory Circuits: An Overview

“…On the theoretical side, the work in [48] deeply studied the role of the iFFL in buffering gene expression fluctuations and compared it to other topologies by stochastic modelling and numerical simulations. Further works followed the one by Osella and colleagues [112,124,125,135,136]. More specifically, Duk and collaborators [124] confirmed the ability of the miRNA mediated iFFL to respond to a sudden change in the quantity of TF regulator with only a small deviation from the steady state.…”

microRNA-mediated noise processing in cells: A fight or a game?

“…Indeed gene expression may gain precision, thereby stabilising the identity of individual cells, through miRNA-mediated noise filtering [42]. The way miRNAs act as noise buffers is through network motifs [43]. As mentioned, interaction networks where transcription factors control the expression of the miRNAs as well as their targets (miRNA-mediated feed forward loops) are efficient in maintaining a desired expression level besides changes in gene dosage or fluctuations at the level of the master transcription factor.…”