Colored extrinsic fluctuations and stochastic gene expression

TL;DR

This paper investigates how colored extrinsic fluctuations influence stochastic gene expression, affecting protein levels, noise, and network response times, with implications for cellular regulation and network design.

Contribution

It extends the stochastic simulation algorithm to incorporate colored extrinsic fluctuations and predicts their effects on gene network behavior and noise modulation.

Findings

Extrinsic fluctuations impact mean protein numbers and intrinsic noise.

Correlated extrinsic fluctuations can amplify or attenuate noise.

Feedforward network motifs modulate stochasticity based on fluctuation correlations.

Abstract

Stochasticity is both exploited and controlled by cells. Although the intrinsic stochasticity inherent in biochemistry is relatively well understood, cellular variation, or 'noise', is predominantly generated by interactions of the system of interest with other stochastic systems in the cell or its environment. Such extrinsic fluctuations are nonspecific, affecting many system components, and have a substantial lifetime, comparable to the cell cycle (they are 'colored'). Here, we extend the standard stochastic simulation algorithm to include extrinsic fluctuations. We show that these fluctuations affect mean protein numbers and intrinsic noise, can speed up typical network response times, and can explain trends in high-throughput measurements of variation. If extrinsic fluctuations in two components of the network are correlated, they may combine constructively (amplifying each other) or destructively (attenuating each other). Consequently, we predict that incoherent feedforward loops attenuate stochasticity, while coherent feedforwards amplify it. Our results demonstrate that both the timescales of extrinsic fluctuations and their nonspecificity substantially affect the function and performance of biochemical networks.