Exponential sensitivity of noise-driven switching in genetic networks

TL;DR

This paper shows that noise-driven gene expression switching is exponentially sensitive to physiological parameters, which limits robustness, but short mRNA lifetimes allow simplified protein-only models to accurately simulate the process.

Contribution

It reveals the exponential sensitivity of noise-driven switching to physiological parameters and demonstrates the validity of protein-only models under certain conditions.

Findings

Switching is dominated by distribution tails and is exponentially sensitive to parameters.

Protein-only models can accurately simulate switching when mRNA lifetimes are short.

Exponential sensitivity poses challenges for reliable cellular switching mechanisms.

Abstract

Cells are known to utilize biochemical noise to probabilistically switch between distinct gene expression states. We demonstrate that such noise-driven switching is dominated by tails of probability distributions and is therefore exponentially sensitive to changes in physiological parameters such as transcription and translation rates. However, provided mRNA lifetimes are short, switching can still be accurately simulated using protein-only models of gene expression. Exponential sensitivity limits the robustness of noise-driven switching, suggesting cells may use other mechanisms in order to switch reliably.