Frequency spectrum of biological noise: a probe of reaction dynamics in living cells

TL;DR

This paper introduces a theoretical framework linking the power spectrum of biomolecular fluctuations to reaction dynamics, enabling inference of intracellular processes from protein noise data.

Contribution

It provides a simple, general equation to relate the power spectrum of product fluctuations to reaction mechanisms, facilitating analysis of gene expression noise.

Findings

The theory allows extraction of mRNA power spectrum from protein data.

Application to luciferase gene network demonstrates practical utility.

Analysis reveals effects of non-Poisson dynamics and heterogeneity on noise spectra.

Abstract

Even in the steady-state, the number of biomolecules in living cells fluctuates dynamically; and the frequency spectrum of this chemical fluctuation carries valuable information about the mechanism and the dynamics of the intracellular reactions creating these biomolecules. Although recent advances in single-cell experimental techniques enable the direct monitoring of the time-traces of the biological noise in each cell, the development of the theoretical tools needed to extract the information encoded in the stochastic dynamics of intracellular chemical fluctuation is still in its adolescence. Here, we present a simple and general equation that relates the power-spectrum of the product number fluctuation to the product lifetime and the reaction dynamics of the product creation process. By analyzing the time traces of the protein copy number using this theory, we can extract the power spectrum of the mRNA number, which cannot be directly measured by currently available experimental techniques. From the power spectrum of the mRNA number, we can further extract quantitative information about the transcriptional regulation dynamics. Our power spectrum analysis of gene expression noise is demonstrated for the gene network model of luciferase expression under the control of the Bmal 1a promoter in mouse fibroblast cells. Additionally, we investigate how the non-Poisson reaction dynamics and the cell-to-cell heterogeneity in transcription and translation affect the power-spectra of the mRNA and protein number.