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.

References in corpus (6)

• Analytical distributions for stochastic gene expression
• Efficient Degradation and Expression Prioritization with Small RNAs
• Applications of Little's Law to stochastic models of gene expression
• Post-transcriptional regulation of noise in protein distributions during gene expression
• Quantifying mRNA synthesis and decay rates using small RNAs
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• Optimal regulation of protein degradation to schedule cellular events with precision
• Quantifying the noise in bursty gene expression under regulation by small RNAs
• Stochastic modeling of gene expression: application of ensembles of trajectories
• Limit theorems for generalized density-dependent Markov chains and bursty stochastic gene regulatory networks