Séance Séminaire

Séminaire de Probabilités et Statistique

Investigating cell-to-cell variability with Bayesian model selection and approximate likelihood

At the molecular level every cell is unique and differences between cells of the same type can have profound biomedical implications. The mechanisms driving this variability can be divided into two classes: within-cell variability arising from stochasticity in the biochemical reactions and cell-to-cell fluctuations in biochemical reaction rates. Given the growing abundance of ?omics data resolved at the single-cell level, it is becoming increasingly important to include these sources of noise in mathematical models. In this talk, I will present a general modelling and Bayesian inference methodology for single-cell data that explicitly takes into account both intrinsic and extrinsic noise. Using quantitative image cytometry (QIC) single-cell proteomics data, the methodology is applied to the study of the MEK/ERK phosphorylation dynamics.