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Research

Restore hosts permanent research teams as well as collaborative projects with external teams.

Permanent teams

Team. 1 : STROMAGICS​​

Repair/Regenerate via the circulation of mesenchymal stromal cells between organs

Team. 2 : FLAMES​​

Inflammaging control by dynamic msc / myeloid cell crosstalk

Team. 3 : METABOLINK

Metabolic interplays and age-related loss of function

Team. 4 : GOT-IT​​

Guided self-organization of tissue for innovative therapeutics

partner teams

IMFT
Reactive Flow Modeling (IMFT)
The team 'Reactive flow modeling' has an established interdisciplinary experience in the domain of image/model coupling from cell to organ-scale such as flow modeling and its relationship with metabolic issues, from cell to entire-organ scale, either inside in-silico experiments or ex-vivo/in-vivo tissues. The team is concern by gathering together relevant 3D images (e.g optical, acoustical) related to the intimate tissue physiology with meso-scale modeling in order to reach a better understanding of integrated couplings, in various organs. The goal inside RESTORE is to develop this expertise to help understanding tissue heterogeneity, identify relevant couplings, so as to help understanding the tissue remodeling and aging. Furthermore, the expression of flux modeling with metabolomic analysis is a topic of interest for future research inside RESTORE, based upon the idea that cells response are coupled to their local (possibly complex) environment. The team uses and develop dedicated image-processing workflow to address practical issues related to (possibly huge) 3D image post-processing either with the aim of reaching state of the art methods.
F. Plouraboué (DR CNRS ), G. Debenest (PR INPT ), P. Keemmel (post-doc ), J. Duchamp (PhD)
IRIT
SIG (IRIT)
The SIG team (Systèmes d’Informations Généralisés / General Information Systems) focuses on the automatic integration of heterogeneous data and knowledge, the modeling and manipulation of big data (data warehouses / data marts, data lakes, multi-stores noSQL), and the analysis of data (big data, and complex data such as multimodal data), in particular through data mining approaches, machine learning and deep learning (artificial neural network) and the development of analysis workflow recommendation systems (meta-mining). The research work of the SIG team thus covers the entire data processing chain, from raw data to elaborate data accessible to users seeking information, wishing to visualize it and perform decisional and predictive analyzes. This work is divided into four main research topics: - Automatic integration of heterogeneous data, - Management of unconventional databases, - User-oriented data, - Analysis, learning and prediction in massive data.
J. ALIGON (Associate Professor UT1), I. MEGDICHE-BOUSARSAR (Associate Professor UT3 ), J. MOTHE (Full Professor UT2), F. RAVAT (Full Professor UT1), N. SOUF (Associate Professor UT3), C. SOULE-DUPUY (Full Professor UT1), O. TESTE (Full Professor UT2)
LHFA
AMoTIS (LHFA)
The projects developed by the AMoTIS team are related to its expertise: Organic Synthesis. Thus the goal of our projects is the design of new ways to generate molecules in general with a special emphasis on fluorescent compounds and isotopic labelling. The latter is focused on radiofluorination within the PiR2 consortium (EG in charge of the radiochemical facilities). Non radioactive isotopic labelling design to enhance NMR signals (for example) is also foreseen. Toward these goals we are developing synthetic approaches and new tools to ease the transfer from the chemistry bench to biological and medical applications. Our chemical research is thus providing the different teams of RESTORE with new imaging tools via a direct interaction with the R&D structure of the Technological Facilities of RESTORE.
E. GRAS (CR CNRS ), M. Danel (IR – UPS), N. Jean Bart (PhD), J. Beuscart (PhD).
LAAS
ELiA (LAAS)
ELiA’s team aims at developing interdisciplinary research projects at the interface between Micro/Nanotechnologies, biology, biophysics and medical applications. ELiA investigates cutting edge technologies for the study and fabrication of: Bio-interfaces, 2D/3D biomolecular and cellular architectures, and analytical devices. Applications target the study of sub-cellular mechanisms, the development of microdevices for the detection and analysis of circulating biomarkers, as well as the fabrication of cell culture micro-environments and tissue models.
L. Malaquin (DR CNRS), C. Vieu (Pr INSA), C. Thibault (MdC INSA), G. Eke (Post-Doc), R. RIesco (PhD), C. Blatché (IR)
IMT
Mathematics for Imaging (IMT)
The primary objective of the members of this team is to advance the foundations of applied mathematics in order to answer practical questions that are still beyond the reach of current theories As part of the RESTORE project, our team could provide support for the following questions such as Automated image analysis, Modelling of biological systems or Improvement of imaging systems.
P. Weiss (CR CNRS), J. Kahn (CR CNRS), F. de Gournay (MCF), E. Soubies (CR CNRS)
IRIT
REVA (IRIT)
The team has longstanding expertise in artificial life to develop bio-inspired artificial intelligence algorithms (evolutionary methods, neural networks, gene regulatory networks, etc.) and application using agent-based models for the simulation of the cellular growth of virtual creatures populating virtual worlds. We propose models and tools to develop agent-based models with the aim to simulate in a near future a piece of living tissue with all its complexity. We develop an integrative model starting from homeostasis condition, then destabilized by an exterior stress in order to recover to the initial homeostatic state. We integrate all components of the tissue: stroma, inflammation and metabolism, which makes this project transversal to the institute. Then, once the model in development, we will be able to validate or refute certain hypothesis proposed by biologists about their comprehension of the system. And finally, with a calibrated model, our simulation tools will be instrumental for biologists to explore and optimize their experimental protocols before actually implementing them in wet labs. We propose all along the development of models to measure its complexity, realisms (capacity to fit to biological data) and hardest to calibrate (number of parameters and complexity of experimental protocols to obtain data) in order to evaluate the possible outcomes of the final models.
S. Cussat-Blanc (MCF UT1C), H. Luga (Professeur UT2J), D. Simoncini (MCF UT1C)

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