control of cellular processes; automated microfluidic platform; quantitative models; stochasticity; cell population models

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

Bio-informatique (BINF)

Bio-informatique

Informations générales

Référence projet : 10-BINF-0006
RST : Gregory BATT
Etablissement Coordinateur : INRIA_Centre Saclay Ile-de-France
Région du projet : Île-de-France
Discipline : 5 - Bio Med

Aide de l'ANR 1 240 000 euros
Investissement couvrant la période de septembre 2011 à septembre 2017

Résumé de soumission

The dual ambition of Iceberg is summarized in the project title: From population models to model populations: observation, modeling and control of gene expression at the single cell level. The first objective is to explicitly represent cell heterogeneity in biomolecular process models. Indeed, within a cell culture, significant differences exist between cells, despite the fact that they all have the same genome. In models that aim to understand how cells function at the molecular level, this heterogeneity is often overlooked. This poses a fundamental problem since the average behavior of a set of individuals is generally not the behavior of an average individual (assuming that it exists). One possible solution is to model each cell in the population and therefore to reason with a set of models. The possibility to use this approach for modelling cell populations and its relevance had not been demonstrated and was one of the main objective of the project. The second objective was the development of an experimental platform combining microscopy and software for real-time observation and control of intracellular processes at the level of the individual cell. We focused on controlling a fundamental cellular process: gene expression. Regarding the first objective, we considered mixed-effect models, a class of models using parameter distributions to capture the heterogeneity present in a given population and individual parameters for each member of the population. Using gene expression data in yeast, we demonstrated that it was indeed possible to attribute biologically-relevant parameters to individual cells. For our second objective, we initially considered controlling gene expression in multicellular eukaryotes, using notably the well-established Tet-On system. However, experimental results were relatively disappointing, showing slow induction dynamics and high cell-to-cell and day-to-day variability. In contrast, we have been able to demonstrate that using real time control one could obtain in yeast a control of gene expression with a precision that was hitherto unachievable. Also, we could demonstrate that real time control approaches allow driving biological systems in configurations that are not naturally encountered by dynamically maintaining bacteria in the unstable configuration of the synthetic gene network that they harbored.  Other important contributions of Iceberg include the development of a recombinase-based genome editing method for engineering multicellular eukaryotic cells, the functionnal characterization of the stochasticity of gene expression in multicellular eukaryotic cells and in connection with chromatin dynamics, the analysis of the functional impact of in-frame AKT1 duplications in juvenile granulosa cell tumors, of a method for approximate analysis of stochastic systems using symmetry-based model reductions, of controllers for stochastic gene expression systems, and of a tool for yeast image segmentation and cell tracking.

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.

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