Connecting protein and mRNA burst distributions for stochastic models of gene expression

Abstract

The intrinsic stochasticity of gene expression can lead to large variability in protein levels for genetically identical cells. Such variability in protein levels can arise from infrequent synthesis of mRNAs which in turn give rise to bursts of protein expression. Protein expression occurring in bursts has indeed been observed experimentally and recent studies have also found evidence for transcriptional bursting, i.e. production of mRNAs in bursts. Given that there are distinct experimental techniques for quantifying the noise at different stages of gene expression, it is of interest to derive analytical results connecting experimental observations at different levels. In this work, we consider stochastic models of gene expression for which mRNA and protein production occurs in independent bursts. For such models, we derive analytical expressions connecting protein and mRNA burst distributions which show how the functional form of the mRNA burst distribution can be inferred from the protein burst distribution. Additionally, if gene expression is repressed such that observed protein bursts arise only from single mRNAs, we show how observations of protein burst distributions (repressed and unrepressed) can be used to completely determine the mRNA burst distribution. Assuming independent contributions from individual bursts, we derive analytical expressions connecting means and variances for burst and steady-state protein distributions. Finally, we validate our general analytical results by considering a specific reaction scheme involving regulation of protein bursts by small RNAs. For a range of parameters, we derive analytical expressions for regulated protein distributions that are validated using stochastic simulations. The analytical results obtained in this work can thus serve as useful inputs for a broad range of studies focusing on stochasticity in gene expression.

First-passage time approach to controlling noise in the timing of intracellular events

“…The translation burst approximation is based on assuming short-lived mRNAs, that is, each mRNA degrades instantaneously after producing a burst of B protein molecules. In agreement with experimental and theoretical studies (38)(39)(40), B is assumed to follow a geometric distribution…”

Stochastic single-gene autoregulation

“…Throughout the paper, we assumed the simplest form for these bursts. Extending these results, in particular the validity of the timescale separation approximation, to the case of more complex mRNA and protein production statistics, see [29], will be the subject of future work. .…”

“…Although the concept of bursts and the mechanisms underlying them are still open to discussion, see for example [28,29], a basic description stems from two simple ideas. First, if transcription/translation events are widely spaced compared to their duration, it is reasonable to speak of burst events.…”

Far-from-Equilibrium Time Evolution between two Gamma Distributions

“…Examples are found not only in turbulence in astrophysical and laboratory plasmas, but also in forest fires, the stock market, and biological ecosystems [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. Specifically, anomalous (much larger than average values) transport associated with large fluctuations in fusion plasmas can degrade the confinement, potentially even terminating fusion operation [6].…”

“…Furthermore, gene expression and protein productions, which used to be thought of as smooth processes, have also been observed to occur in bursts (e.g. [19][20][21][22][23]. Such rare events of large amplitude (called intermittency) can dominate the entire transport even if they occur infrequently [8,24].…”