Quantifying the noise in bursty gene expression under regulation by small RNAs

Abstract

Gene expression is a fundamental process in a living system. The small RNAs (sRNAs) is widely observed as a global regulator in gene expression. The inherent nonlinearity in this regulatory process together with the bursty production of messenger RNA (mRNA), sRNA and protein make the exact solution for this stochastic process intractable. This is particularly the case when quantifying the protein noise level, which has great impact on multiple cellular processes. Here, we propose an approximate yet reasonably accurate solution for the gene expression noise with infrequent burst and strong regulation by sRNAs. This analytical solution allows us to better analyze the noise and stochastic deviation of protein level. We find that the regulation amplifies the noise, reduces the protein level. The stochasticity in the regulation generates more proteins than what if the stochasticity is removed from the system. The sRNA level is most important to the relationship between the noise and stochastic deviation. The results provide analytical tools for more general studies of gene expression and strengthen our quantitative understandings of post-transcriptional regulation in controlling gene expression processes.

A generalized linear threshold model for an improved description of the spreading dynamics

“…To efficiently simulate the GLT model, we apply the Gillespie algorithm 36,37 . It is an efficient simulation method for the stochastic process and was heavily used to investigate the interactions of molecules in chemical systems 44,45 or the cellular growth and division in biological systems [46][47][48] . It can also be used to simulate the epidemic spreading such as SI and SIR model 38,39,49 .…”