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Projet clos

Des modèles de population aux populations de modèles: observation, modélisation et contrôle de l'expression génique au niveau de la cellule unique (Iceberg)


Action : Bio-informatique


N° de convention : 10-BINF-0006

Informations générales

  • Référence projet : 10-BINF-0006
  • RST : Gregory BATT
  • Etablissement Coordinateur : INRIA Saclay
  • Région du projet : Île-de-France
  • Discipline : 5 - Bio Med
  • Aide allouée : 1 240 000 €
  • Date de début du projet : 01/10/2011
  • Date de fin du projet : 30/09/2017
  • Site web du projet : http://contraintes.inria.fr/~batt/iceberg/home.html
  • Mots clés : control of cellular processes; automated microfluidic platform; quantitative models; stochasticity; cell population models

Résumé du projet

In 2012, we obtained a significant first result since the proof of concept of the approach has been published in PNAS for yeast cells. Since then, the research has followed two directions. The first direction deals with experimental developments in biology (cell lines) and experimentation (platform) for the higher eukaryotic systems. The second direction deals with methodological and modeling developments based on a yeast system for which data is readily available. On the first aspect, quantitative characterization of the originally-proposed system (control of gene expression) has shown that it might not be feasible to obtain highly-accurate control of gene expression in mammalian cells, in contrast to the results we obtained recently in yeast. We therefore focus on controlling signal transduction taking inspiration from recently-published optogenetic systems. Achieving real-time control with such systems is highly challenging. However, we have recently made significant progress on (1) the development of a library of genetic elements needed to study optogenetically-stimulated signal-transduction pathways and (2) the development of a light control microscopy platform enabling optogenetic control. Thanks to a one-year extension that was granted and the fact that we still have sufficient resources to work on this problem until Sept 2017, we expect to achieve our novel objective. On the second aspect, we obtained (and published in early 2016) a central result with respect to the project objectives: we showed that parameters can be attributed to single cells and that biologically-relevant information can be inferred by harnessing cell-to-cell parameter variability.

(L'auteur de ce résumé est le coordinateur du projet, qui est responsable du contenu de ce résumé. L'ANR décline par conséquent toute responsabilité quant à son contenu.)