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Open Access Research article

On the spontaneous stochastic dynamics of a single gene: complexity of the molecular interplay at the promoter

Antoine Coulon123*, Olivier Gandrillon13 and Guillaume Beslon23

Author Affiliations

1 Université de Lyon, Université Lyon 1, Centre de Génétique Moléculaire et Cellulaire (CGMC), CNRS UMR5534, F-69622 Lyon, France

2 Université de Lyon, INSA-Lyon, Laboratoire d'InfoRmatique en Image et Systemes d'information (LIRIS), CNRS UMR5205, F-69621 Lyon, France

3 Rhône-Alpes Complex Systems Institute (IXXI), F-69007 Lyon, France

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BMC Systems Biology 2010, 4:2  doi:10.1186/1752-0509-4-2

Published: 8 January 2010

Abstract

Background

Gene promoters can be in various epigenetic states and undergo interactions with many molecules in a highly transient, probabilistic and combinatorial way, resulting in a complex global dynamics as observed experimentally. However, models of stochastic gene expression commonly consider promoter activity as a two-state on/off system. We consider here a model of single-gene stochastic expression that can represent arbitrary prokaryotic or eukaryotic promoters, based on the combinatorial interplay between molecules and epigenetic factors, including energy-dependent remodeling and enzymatic activities.

Results

We show that, considering the mere molecular interplay at the promoter, a single-gene can demonstrate an elaborate spontaneous stochastic activity (eg. multi-periodic multi-relaxation dynamics), similar to what is known to occur at the gene-network level. Characterizing this generic model with indicators of dynamic and steady-state properties (including power spectra and distributions), we reveal the potential activity of any promoter and its influence on gene expression. In particular, we can reproduce, based on biologically relevant mechanisms, the strongly periodic patterns of promoter occupancy by transcription factors (TF) and chromatin remodeling as observed experimentally on eukaryotic promoters. Moreover, we link several of its characteristics to properties of the underlying biochemical system. The model can also be used to identify behaviors of interest (eg. stochasticity induced by high TF concentration) on minimal systems and to test their relevance in larger and more realistic systems. We finally show that TF concentrations can regulate many aspects of the stochastic activity with a considerable flexibility and complexity.

Conclusions

This tight promoter-mediated control of stochasticity may constitute a powerful asset for the cell. Remarkably, a strongly periodic activity that demonstrates a complex TF concentration-dependent control is obtained when molecular interactions have typical characteristics observed on eukaryotic promoters (high mobility, functional redundancy, many alternate states/pathways). We also show that this regime results in a direct and indirect energetic cost. Finally, this model can constitute a framework for unifying various experimental approaches. Collectively, our results show that a gene - the basic building block of complex regulatory networks - can itself demonstrate a significantly complex behavior.