A geometric analysis of fast-slow models for stochastic gene expression

Nikola Popovic*, Marr, Carsten, Peter Swain

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Stochastic models for gene expression frequently exhibit dynamics on several different scales. One potential time-scale separation is caused by significant differences in the lifetimes of mRNA and protein; the ratio of the two degradation rates gives a natural small parameter in the resulting Chemical Master Equation, allowing for the application of perturbation techniques. Here,
we develop a framework for the analysis of a family of `fast-slow' models for gene expression that is based on geometric singular perturbation theory. We illustrate our approach by giving a complete characterisation of a standard two-stage model which assumes transcription, translation, and
degradation to be first-order reactions. In particular, we present a systematic expansion procedure for the probability-generating function that can in principle be taken to any order in the perturbation parameter, allowing for an approximation of the corresponding propagator probabilities to that
same order. For illustrative purposes, we perform this expansion explicitly to first order, both on the fast and the slow time-scales; then, we combine the resulting asymptotics into a composite fast-slow expansion that is uniformly valid in time. In the process, we extend, and prove rigorously, results
previously obtained by Shahrezaei and Swain [51] and Bokes et al. [8, 9]. We verify our asymptotics by numerical simulation, and we explore its practical applicability and the effects of a variation in the system parameters and the time-scale separation. Focussing on biologically relevant parameter regimes that induce translational bursting, as well as those in which mRNA is frequently transcribed, we find that the first-order correction can significantly improve the steady-state probability distribution. Similarly, in the time-dependent scenario, inclusion of the first-order fast asymptotics results in a uniform approximation for the propagator probabilities that is superior to the slow dynamics alone. Finally, we discuss the generalisation of our geometric framework to models for regulated gene expression that involve additional stages.
Original languageEnglish
JournalJournal of Mathematical Biology
Early online date2 Apr 2015
Publication statusPublished - 2 Apr 2015


  • Asymptotic expansion
  • Chemical master equation
  • Generating function
  • Geometric singular perturbation theory
  • Propagator probabilities
  • Stochastic gene expression
  • Two-stage model


Dive into the research topics of 'A geometric analysis of fast-slow models for stochastic gene expression'. Together they form a unique fingerprint.

Cite this