PuSH - Publikationsserver des Helmholtz Zentrums München: A geometric analysis of fast-slow models for stochastic gene expression.

Navigation

Startseite

English

Recherche

Erweiterte Suche

Durchblättern nach ...

... Zeitschriften

... Publikationstypen

... Forschungsdaten

... Erscheinungsjahr

Publikationen im Überblick

Hilfe & Kontakt

Ansprechpartner

Hilfe

Datenschutz

Popovic, N.* ; Marr, C. ; Swain, P.S.*

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

J. Math. Biol. 72, 87-122 (2015)

DOI

Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.

Abstract
Metriken
Zusatzinfos

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 (Proc Natl Acad Sci USA 105(45):17256–17261, 2008) and Bokes et al. (J Math Biol 64(5):829–854, 2012; J Math Biol 65(3):493–520, 2012). 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.

Altmetric

Weitere Metriken?

[➜Einloggen]