Measuring the Degree of Modularity from Gene Expression Noise in Gene Regulatory Circuits

TL;DR

This paper explores how stochastic fluctuations in gene expression can be used to measure the modularity of gene regulatory circuits, linking noise characteristics to retroactivity and frequency response.

Contribution

It introduces a method to quantify retroactivity in gene circuits through analysis of stochastic noise and autocorrelation functions, connecting stochastic and deterministic frameworks.

Findings

Noise correlation time increases when circuits are connected.

Narrower spectral density indicates higher retroactivity.

Different signals are preferable for circuit description depending on frequency.

Abstract

Gene regulatory circuits show significant stochastic fluctuations in their circuit signals due to the low copy number of transcription factors. When a gene circuit component is connected to an existing circuit, the dynamic properties of the existing circuit can be affected by the connected component. In this paper, we investigate modularity in the dynamics of the gene circuit based on stochastic fluctuations in the circuit signals. We show that the noise in the output signal of the existing circuit can be affected significantly when the output is connected to the input of another circuit component. More specifically, the output signal noise can show significantly longer correlations when the two components are connected. This equivalently means that the noise power spectral density becomes narrower. We define the relative change in the correlation time or the spectrum bandwidth by stochastic retroactivity, which is shown to be directly related to the retroactivity defined in the deterministic framework by del Vecchio et al. This provides an insight on how to measure retroactivity, by investigating stochastic fluctuations in gene expression levels, more specifically, by obtaining an autocorrelation function of the fluctuations. We also provide an interesting aspect of the frequency response of the circuit. We show that depending on the magnitude of operating frequencies, different kinds of signals need to be preferably chosen for circuit description in a modular fashion: at low enough frequency, expression level of transcription factor that are not bound to their specific promoter region needs to be chosen, and at high enough frequency, that of the total transcription factor, both bound and unbound, does.