Mean-field versus stochastic models for transcriptional regulation

R. Blossey*, C. V. Giuraniuc

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

We introduce a minimal model description for the dynamics of transcriptional regulatory networks. It is studied within a mean-field approximation, i.e., by deterministic ODE's representing the reaction kinetics, and by stochastic simulations employing the Gillespie algorithm. We elucidate the different results that both approaches can deliver, depending on the network under study, and in particular depending on the level of detail retained in the respective description. Two examples are addressed in detail: The repressilator, a transcriptional clock based on a three-gene network realized experimentally in E. coli, and a bistable two-gene circuit under external driving, a transcriptional network motif recently proposed to play a role in cellular development.

Original languageEnglish
Article number031909
JournalPhysical Review E - Statistical, Nonlinear, and Soft Matter Physics
Volume78
Issue number3
DOIs
Publication statusPublished - 10 Sep 2008

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Transcriptional Regulation
Mean-field Model
Stochastic Model
Reaction Kinetics
Gene Networks
Regulatory Networks
Mean-field Approximation
Minimal Model
Stochastic Simulation
genes
Escherichia Coli
Gene
clocks
reaction kinetics
approximation
simulation

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability
  • Condensed Matter Physics

Cite this

Mean-field versus stochastic models for transcriptional regulation. / Blossey, R.; Giuraniuc, C. V.

In: Physical Review E - Statistical, Nonlinear, and Soft Matter Physics, Vol. 78, No. 3, 031909, 10.09.2008.

Research output: Contribution to journalArticle

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