Intrinsic noise in gene regulatory networks

Abstract

Cells are intrinsically noisy biochemical reactors: low reactant numbers can lead to significant statistical fluctuations in molecule numbers and reaction rates. Here we use an analytic model to investigate the emergent noise properties of genetic systems. We find for a single gene that noise is essentially determined at the translational level, and that the mean and variance of protein concentration can be independently controlled. The noise strength immediately following single gene induction is almost twice the final steady-state value. We find that fluctuations in the concentrations of a regulatory protein can propagate through a genetic cascade; translational noise control could explain the inefficient translation rates observed for genes encoding such regulatory proteins. For an autoregulatory protein, we demonstrate that negative feedback efficiently decreases system noise. The model can be used to predict the noise characteristics of networks of arbitrary connectivity. The general procedure is further illustrated for an autocatalytic protein and a bistable genetic switch. The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin.

Hybrid modeling and simulation of stochastic effects on progression through the eukaryotic cell cycle

“…All of these reactions were absent from the three-variable model and were added to it later to account for stochastic effects of transcription-translation coupling. [21][22][23] If (as we have shown for Kar et al's model) the gene and mRNA reactions are primarily responsible for intrinsic noise, it may not be necessary to unpack the original deterministic model for the protein regulatory network. One only needs to apply the hybrid method on a naturally partitioned model, where the SSA regime includes all newly added stochastic reactions at the gene expression level, while the ODE regime includes the ODE set from the original deterministic model at the protein level.…”

A Compositional Approach to the Stochastic Dynamics of Gene Networks

“…A quantitative understanding of their role is thus needed to understand gene regulation. Regulatory functions can indeed work to eliminate stochastic effects [2], or to even exploit them [3].…”

Distinct noise-controlling roles of multiple negative feedback mechanisms in a prokaryotic operon system

“…Two most important characteristics are the mean and the variance of the number of molecules of each species within the networks. The advantages of these system properties are that they are fundamental and simple to understand, provide clear interpretations and more importantly, they are easily accessible experimentally [27]. Thattai and Oudenaarden [27] suggested to use the Fano factor (ratio of the variance to the mean) to measure the relative size of noise in gene expression.…”

“…The advantages of these system properties are that they are fundamental and simple to understand, provide clear interpretations and more importantly, they are easily accessible experimentally [27]. Thattai and Oudenaarden [27] suggested to use the Fano factor (ratio of the variance to the mean) to measure the relative size of noise in gene expression. However, the Fano factor can be misleading for multivariate random processes and only works well for univariate discrete random processes [26].…”