Séance Séminaire

Séminaire de Probabilités et Statistique

Stochastic gene expression: modelling and inference from lineage trees.

Starting from a transcription and translation model of gene expression, we propose a stochastic model for the evolution of gene expression dynamics in a population of dividing cells. Based on this model, we develop a method for the direct quantification of inheritance and variability of kinetic gene expression parameters from single-cell gene expression and lineage data. We demonstrate that our approach provides unbiased estimates of mother?daughter inheritance parameters, whereas indirect approaches using lineage information only in the post-processing of individual-cell parameters underestimate inheritance. Finally, we show on yeast osmotic shock response data that daughter cell parameters are largely determined by the mother, thus confirming the relevance of our method for the correct assessment of the onset of gene expression variability and the study of the transmission of regulatory factors. This is a joint work with Marc Lavielle and Eugenio Cinquemani. We also extended our modelling and identification approach to explicitly account for stochasticity of promoter activation and demonstrate via simulation the performance of the method and the improvement relative to the original approach where this source of noise is not accounted for. WEBINAIRE ouvert à toutes et tous : https://umontpellier-fr.zoom.us/j/85813807839