During my PhD, we have studied the variability (or stochasticity) of gene expression assuming that the stochastic signal it produces carries information about the process of gene expression itself. The stochasticity of gene expression can be characterized by the observed variation in the number of proteins produced either by different isogenic cells (cells that have the same genome) at a given time or within a single cell over time. First, we showed experimentally that the level of stochasticity of a gene changes according to its locus (its position on the genome). We have also shown that, for a given locus, the level of stochasticity could be influenced by global chromatin-state modifier agents. Then, we analyzed how the chromatin dynamics can influence the stochasticity of gene expression. This analysis was conducted by using a modeling and simulation approach, the results of which being in turn compared to biological data. Using a two-states model allowed me to show that the activity of a promoter is characterized by long periods during which the chromatin prevents transcription, interspersed by brief periods when transcription can occur in the form of intense bursts. Finally, we identified characteristic genomic elements that, when in the neighbourhood of a gene, may influence its level of stochasticity. In particular, we have shown that when the reporter gene is integrated close to a neighbour gene, its stochasticity is significantly increased. This work allowed me to unravel a link between the chromatin dynamics, the genomic environment and the stochasticity of gene expression. This link confers evolutionary perspectives to the cell by allowing it to regulate stochasticity, which allows for the selection of an appropriate level of stochasticity.
