New statistical models for the inference of cell-to-cell variance in gene expression - and their application to single-cell transcriptomes
New evolutionary models of the evolution of stochastic gene expression and their application to wild yeasts single-cell transcriptomes
Stochastic gene expression and evolution of gene network: a modeling approach
While for long simplified as a deterministic process, gene expression is intrinsically stochastic, owing to the involved molecular players being in typically small numbers within each cell. This can be studied via the recent development of single-cell RNA sequencing. In these projects, we want to understand how evolution has shaped stochastic gene expression (aka "expression noise"), and reciprocally, how this stochasticity between the genotype and phenotype levels impact the evolution of organisms.
Concrete examples of possible projects include:
- The generation of a scRNASeq dataset of yeast cells and the development of new statistical models to estimate the component of expression noise, and assess which selective forces shape them.
- Developing new models of expression noise evolution and inference procedures in order to study the evolution of expression noise. These models will be applied to a comparative dataset of scRNASeq between several yeast species.
- Creating new evolutionary models of gene networks accounting for expression noise, and theoretically study the impact of stochastic gene expression on the evolution of these networks.