Exponential sensitivity of noise-driven switching in genetic networks

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.

💡 Research Summary

The paper investigates how intrinsic biochemical noise drives stochastic switching between distinct gene‑expression states in cellular genetic networks and demonstrates that such switching is governed by the tails of probability distributions, rendering it exponentially sensitive to physiological parameters. The authors first construct a detailed stochastic model of transcription and translation using a continuous‑time Markov process, incorporating transcription rate (k_tx), translation rate (k_tl), mRNA degradation rate (γ_m) and protein degradation rate (γ_p). By applying large‑deviation theory, they derive an analytical expression for the switching probability P_sw that takes the form P_sw ≈ exp