Massively parallel sequencing applied to single cells allows us to investigate new questions that were out of reach for classical bulk genomics. Cell-to-cell variability is central in gene regulation or cell differentiation, as it provides information on the underlying molecular networks. Consequently, single cell expression profiling has the promise of revolutionizing our understanding of genomes regulation.
Nevertheless, the specific characteristics of single cell data as well as their dimensionality calls for new mathematical models and computational tools. The goal of this project is to develop new methodologies to investigate cell identity and the dynamics of cell differentiation, by integrating single cell expression and epigenomic data. For that purpose, our consortium gathers unique combined expertises in statistics, machine learning, optimal transport and systems biology, and an extended network of collaborators on single-cell (medical) genomics in France and abroad.
Consortium
Researchers
Joon Kwon
AgroParisTech/INRAE
Mathématiques & Informatique Appliquées Paris
CR INRAE, Machine Learning
Julien Chiquet
AgroParisTech/INRAE
Mathématiques & Informatique Appliquées Paris
PI, DR INRAE, Statistical learning
Olivier Gandrillon
ENS de Lyon/LBMC
Laboratoire de Biologie de Modélisation de la Cellule
PI, DR CNRS, Systems Biology
Stéphane Robin
AgroParisTech/INRAE
Mathématiques & Informatique Appliquées Paris
DR INRAE, Statistics
Students
Elias Ventre
Inria/ENS de Lyon
Laboratoire de Biologie de Modélisation de la Cellule
Dracula, INRIA
PhD Student
