STOCHAGENE

The stochastic nature of gene expression at the cellular level is clearly established (for recent reviews, see [1]; and [2]). It should be noted that stochasticity does not by any means imply complete randomness; rather, constrained randomness, intermediate between rigid determinism and complete disorder is what is usually seen experimentally. Although it has been shown that stochasticity can play a very constructive role in physical phenomena, the biological role of stochasticity in gene expression (SGE) still has to be formally demonstrated, especially during a differentiation process in higher eukaryotic cells. The situation is somewhat different in prokaryotes, especially in B. subtilis where a recent paper has recently demonstrated that SGE was clearly used by the micro-organism in order to augment its fitness in an uncertain environment [3]. This seminal work has demonstrated that in order to unravel the biological role of SGE, one must be able to manipulate its level experimentally. This in turn requires the understanding of the molecular mechanisms at stake. In short, we want to be able to manipulate SGE with the same ease with which one has known for years how to manipulate the mean expression level (through cDNA-based overexpression or shRNA-based inhibition). This is therefore the goal of the present project to explore the role played by the chromatin context in the generation and control of SGE, by a joint modeling/experimental approach, that is rendered necessary by the fundamentally dynamic nature of noise generation and control. For this we will: 1. Develop quantitative real time analyses of gene expression at the single cell level in order to evaluate the inter- and intra-cellular variability, using noise-reporter cell lines (both human and avian) which will allow to assess the role played by the chromatin context. For this we will rely upon site-directed recombination using the CRE/Lox recombination system [4]. 2. Model the molecular causes of the stochasticity in gene expression, with a special focus on the spatial aspects underlying chromatin dynamics. 3. Confront models and measures to iterate the virtuous circle at the heart of a system’s biology approach. For this, each acquisition level will be complemented with a relevant modeling approach since it is our believe (and the results of years of transdisciplinary work for all of us) that the “virtuous circle” between modeling and experiments is not simply a single step in the project but is more a continuous process within the project. The main success of the project would be to be able at the end of the project to correctly model some of the main molecular causes of gene expression stochasticity, to experimentally manipulate those causes and to demonstrates that we indeed can experimentally modulate the level of SGE in higher eukaryotic cells. 1. McCullagh, E., et al. Nat Chem Biol, 2009. 5: p. 699-704. 2. Eldar, A. and M.B. Elowitz. Nature, 2010. 467: p. 167-73. 3. Cagatay, T., et al. Cell, 2009. 139: p. 512-22. 4. Desprat, R. and E.E. Bouhassira. PLoS One, 2009. 4: p. e5956.