Title :

What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast

Author(s) :

Llamosi, Artémis [Auteur]
Computational systems biology and optimization [Lifeware]
Matière et Systèmes Complexes [MSC]
Gonzalez, Andres [Auteur]
Dipartimento di Informatica e Sistemistica [DIS]
Versari, Cristian [Auteur]
BioComputing
Cinquemani, Eugenio [Auteur]
Modeling, simulation, measurement, and control of bacterial regulatory networks [IBIS]
Ferrari-Trecate, Giancarlo [Auteur]
Dipartimento di Informatica e Sistemistica [DIS]
Hersen, Pascal [Auteur]
Matière et Systèmes Complexes [MSC]
Mechanobiology Institute [Singapore] [MBI]
Batt, Gregory [Auteur]
Computational systems biology and optimization [Lifeware]

Journal title :

PLoS Computational Biology

Pages :

e1004706

Publisher :

PLOS

Publication date :

2016-02-09

ISSN :

1553-734X

HAL domain(s) :

Informatique [cs]/Bio-informatique [q-bio.QM]

English abstract : [en]

Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models of intracellular processes, usually fitted to population-averaged data, should rather be ...
Show more >Significant cell-to-cell heterogeneity is ubiquitously observed in isogenic cell populations. Consequently, parameters of models of intracellular processes, usually fitted to population-averaged data, should rather be fitted to individual cells to obtain a population of models of similar but non-identical individuals. Here, we propose a quantitative modeling framework that 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 to repeated hyperosmotic shocks and state-of-the-art statistical inference approaches for mixed-effects models to infer multidimensional parameter distributions describing the population, and then derive specific parameters for individual cells. The analysis of single-cell parameters shows that single-cell identity (e.g. gene expression dynamics, cell size, growth rate, mother-daughter relationships) is, at least partially, captured by the parameter values of gene expression models (e.g. rates of transcription, translation and degradation). Our approach shows how to use the rich information contained into longitudinal single-cell data to infer parameters that can faithfully represent single-cell identity.Show less >

Language :

Anglais

Popular science :

Non

ANR Project :

Des modèles de population aux populations de modèles: observation, modélisation et contrôle de l'expression génique au niveau de la cellule unique
Determinants de l'Identité : de la molécule à l'individu

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL