A. Llamosi, A.M. Gonzalez-Vargas, C. Versari, E. Cinquemani, G. Ferrari-Trecate, P. Hersen, and G. Batt. What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast. PLoS Computational Biology, doi:10.1371/journal.pcbi.1004706 , 12(2):e1004706, 2016.
Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations.Consequently, parameters of models of intracellular processes, usually fitted to population-averageddata, should rather be fitted to individual cells to obtain a population of models ofsimilar but non-identical individuals. Here, we propose a quantitative modeling frameworkthat attributes specific parameter values to single cells for a standard model of gene expression.We combine high quality single-cell measurements of the response of yeast cells torepeated hyperosmotic shocks and state-of-the-art statistical inference approaches formixed-effects models to infer multidimensional parameter distributions describing the population,and then derive specific parameters for individual cells. The analysis of single-cellparameters shows that single-cell identity (e.g. gene expression dynamics, cell size, growthrate, mother-daughter relationships) is, at least partially, captured by the parameter valuesof gene expression models (e.g. rates of transcription, translation and degradation). Ourapproach shows how to use the rich information contained into longitudinal single-cell datato infer parameters that can faithfully represent single-cell identity