Presentation

Team Morpheme: Computational morphometry and morphodynamics of cellular and supracellular structures

Joint team between Inria, CNRS and Université Côte d’Azur
Joint team affiliated with Inria SAMComputer Science+Signals+Systems Laboratory (I3S) and Institute of Biology Valrose (iBV)
History: Creation process started in 2010; Officialization as an « Equipe-Projet Commune (EPC) » in 2013; Renewal in 2017

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UMR 7271

UMR 7277

Objectives

Motivation: The morphology and topology of mesoscopic structures do have a key influence on the functional behavior of organs

Main Goal: Be at the interface between computational science and biology

Objectives: Characterize and model the morphological & topological properties, and the development of biological structures; in particular:

  • Extract quantitative parameters to characterize morphology over time and across samples
  • Then statistically analyze the shape of complex structures to identify relevant markers and define classification tools
  • Finally, propose models explaining the temporal evolution of the observed samples

Framework:

  • Scale: from cell to supracellular scale
  • Data: in vivo imaging: 2D, 2D+t, 3D or 3D+t images from various microscopy systems (confocal, 2-photon, phase-contrast, video, micro-tomography)
  • Tools: image processing, statistical learning and computational modeling

In the long term: This should allow for a better understanding of the development of normal tissues and a characterization at the supracellular level of different pathologies such as the Fragile X syndrome, Alzheimer or diabetes.

4 Research Axes

  • Image acquisition. Includes:
    (1) For a given biological question, definition of studied phenomena (experimental conditions) and preparation of samples
    (2) Optimization of the acquisition protocol (staining, imaging…) and definition of relevant quantitative characteristics
    (3) Reconstruction/restoration of native data to improve the image readability and interpretation
  • Structure extraction
    Detection and delineation of the biological structures of interest in images, which includes the use of previously defined models for improving the detection. Two main challenges are the variability of biological structures and the huge size of datasets
  • Interpretation/Classification. Includes:
    (1) Inference of parameters associated with the model that has been used to extract the biological structure under study
    (2) Definition of classification schemes for characterizing the different populations based either on the model parameters or on some specific metric between the extracted structures. The aim is to provide biological information characterizing the different populations
  • Modeling. Back-and-forth approach:
    (1) Forth approach: modeling biological phenomena such as axon growth or network topology in different contexts using image-based information to calibrate/validate the models
    (2) Back approach: using a prior model to extract relevant information from images

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