• English
    • français
  • Help
  •  | 
  • Contact
  •  | 
  • About
  •  | 
  • Login
  • HAL portal
  •  | 
  • Pages Pro
  • EN
  •  / 
  • FR
View Item 
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
  •   LillOA Home
  • Liste des unités
  • Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

What population reveals about individual ...
  • BibTeX
  • CSV
  • Excel
  • RIS

Document type :
Article dans une revue scientifique
DOI :
10.1371/journal.pcbi.1004706
Title :
What population reveals about individual cell identity: Single-cell parameter estimation of models of gene expression in yeast
Author(s) :
Llamosi, Artémis [Auteur]
Matière et Systèmes Complexes [MSC (UMR_7057)]
Computational systems biology and optimization [Lifeware]
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]
Mechanobiology Institute [Singapore] [MBI]
Matière et Systèmes Complexes [MSC (UMR_7057)]
Batt, Gregory [Auteur]
Computational systems biology and optimization [Lifeware]
Journal title :
PLoS Computational Biology
Pages :
e1004706
Publisher :
Public Library of Science
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
Peer reviewed article :
Oui
Audience :
Internationale
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
Files
Thumbnail
  • https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1004706&type=printable
  • Open access
  • Access the document
Université de Lille

Mentions légales
Université de Lille © 2017