"Reducing gene expression noise: The role of RNA stem-loops in translation regulation"
Stochastic modelling is key to understanding the dynamics of intracellular events in most biochemical systems, including gene-expression models. The stochasticity in the levels of gene products, e.g., messenger RNA (mRNA) and protein, is referred to as noise, which leads to cell-to-cell variability. The contributions to noise can emerge from different sources, such as structural elements. Recent studies have demonstrated that mRNA structure can be more complex than the most straightforward assumptions. Here, we study a structuration/generalisation of a stochastic gene-expression model in which mRNA molecules can be found in one of its finite number of different states and can transition among these states. In addition to characterising and deriving non-trivial analytical expressions for the steady-state protein distribution, we provide two different examples, which can be readily obtained from the structured/generalised model. The main example pertains to the formation of stem-loops; here, we reinterpret previous data and provide additional insights. Our analysis reveals that stem loops that restrict translation can reduce noise.
Additional authors: Pavol Bokes, Department of Applied Mathematics and Statistics, Comenius University, 84248 Bratislava, Slovakia; Abhyudai Singh, Department of Electrical and Computer Engineering, University of Delaware, 19716 Newark, USA