Decomposing Noise in Biochemical Signalling Systems Highlights the Role of Protein Degradation

TL;DR

This paper introduces a new method to quantify how individual reactions contribute to noise in biochemical systems, revealing that protein degradation significantly impacts variability and can be targeted for noise reduction.

Contribution

The authors develop a novel analytical approach to decompose noise sources in biochemical networks, highlighting the critical role of degradation in system variability.

Findings

Degradation of output proteins accounts for about half of the noise in open conversion systems.

The methodology uncovers reaction-specific contributions to stochastic variability.

Degradation feedback can effectively suppress noise in biochemical systems.

Abstract

The phenomena of stochasticity in biochemical processes have been intriguing life scientists for the past few decades. We now know that living cells take advantage of stochasticity in some cases and counteract stochastic effects in others. The source of intrinsic stochasticity in biomolecular systems are random timings of individual reactions, which cumulatively drive the variability in outputs of such systems. Despite the acknowledged relevance of stochasticity in the functioning of living cells no rigorous method have been proposed to precisely identify sources of variability. In this paper we propose a novel methodology that allows us to calculate contributions of individual reactions into the variability of a system's output. We demonstrate that some reactions have dramatically different effects on noise than others. Surprisingly, in the class of open conversion systems that serve as an approximate model of signal transduction, the degradation of an output contributes half of the total noise. We also demonstrate the importance of degradation in other relevant systems and propose a degradation feedback control mechanism that has the capability of an effective noise suppression. Application of our method to some well studied biochemical systems such as: gene expression, Michaelis-Menten enzyme kinetics, and the p53 system indicates that our methodology reveals an unprecedented insight into the origins of variability in biochemical systems. For many systems an analytical decomposition is not available; therefore the method has been implemented as a Matlab package and is available from the authors upon request.