Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression

Andrew G. Nicoll, Juraj Szavits-Nossan, Martin R. Evans and Ramon Grima ()
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Andrew G. Nicoll: University of Edinburgh
Juraj Szavits-Nossan: University of Edinburgh
Martin R. Evans: University of Edinburgh
Ramon Grima: University of Edinburgh

Nature Communications, 2025, vol. 16, issue 1, 1-18

Abstract: Abstract What features of transcription can be learnt by fitting mathematical models of gene expression to mRNA count data? Given a suite of models, fitting to data selects an optimal one, thus identifying a probable transcriptional mechanism. Whilst attractive, the utility of this methodology remains unclear. Here, we sample steady-state, single-cell mRNA count distributions from parameters in the physiological range, and show they cannot be used to confidently estimate the number of inactive gene states, i.e. the number of rate-limiting steps in transcriptional initiation. Distributions from over 99% of the parameter space generated using models with 2, 3, or 4 inactive states can be well fit by one with a single inactive state. However, we show that for many minutes following induction, eukaryotic cells show an increase in the mean mRNA count that obeys a power law whose exponent equals the sum of the number of states visited from the initial inactive to the active state and the number of rate-limiting post-transcriptional processing steps. Our study shows that estimation of the exponent from eukaryotic data can be sufficient to determine a lower bound on the total number of regulatory steps in transcription initiation, splicing, and nuclear export.

Date: 2025
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DOI: 10.1038/s41467-025-58127-4

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