Noise control in gene regulatory networks with negative feedback

TL;DR

This paper applies Wiener-Kolmogorov filtering theory to biological gene regulatory networks with negative feedback, providing a quantitative framework to understand and optimize noise suppression in cellular processes.

Contribution

It introduces a formalism based on WK theory to analyze noise control in biological circuits, demonstrating optimal filtering in certain gene regulatory systems.

Findings

Negative feedback circuits can behave as WK filters.

Hill-like nonlinear functions achieve optimal noise reduction.

The approach aligns with known noise suppression scaling laws.

Abstract

Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise.