The Project

Morpheme: Computational Morphometry and Morphodynamics of Cellular and Supracellular Structures

    

Our Objectives

The scientific objectives of MORPHEME are to characterize and model the development and the morphological properties of biological structures from the cell to the supra-cellular scale. Being at the interface between computational science and biology, we plan to understand the morphological changes that occur during development combining in vivo imaging, image processing and computational modeling.

The morphology and topology of mesoscopic structures, indeed, do have a key influence on the functional behavior of organs. Our goal is to characterize different populations or development conditions based on the shape of cellular and supra-cellular structures, including micro-vascular networks and dendrite/axon networks. Using 2D, 2D+t, 3D or 3D+t images (acquired with confocal microscopy, video-microscopy, 2-photon microscopy or micro-tomography), we plan to extract quantitative parameters to characterize morphometry over time and in different samples. We will then statistically analyze shapes and complex structures to identify relevant markers and define classification tools. Finally, we will propose models explaining the temporal evolution of the observed samples. With this, we hope to better understand the development of normal tissues, but also characterize at the supra-cellular level different pathologies such as the Fragile X Syndrome, Alzheimer or diabetes.

Morpheme will be structured along the following axes:

• Image acquisition: this includes i) from the biological question, definition of studied populations (experimental conditions) and preparation of samples. ii) optimization of the acquisition protocols (staining, imaging,...) and definition of relevant quantitative characteristics. iii) reconstruction/restoration of native data to improve the image readability and interpretation.
• Feature extraction: this consists in detecting and delineating the biological structures of interest from images. This 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 data sets.
• Classification/Interpretation: considering a database of images containing different populations, we can infer the parameters associated with a given model on each dataset from which the biological structure under study has been extracted. We plan to define classification schemes for characterizing the different population based either on the model parameters or on some specific metric between the extracted structures. This characterization will provide biological information defining the different classes.
• Modeling: Two aspects are considered. This first consists in modeling biological phenomena such as axon growth or network topology in different contexts. One main advantage of our team is the possibility to use the image information for calibrating and/or validating the models. Calibration induces parameter inference as a main challenge. The second aspect consists in using prior model for extracting relevant information from images.