Inferring the mechanics of living tissue from statistical analysis of its deformation

Appel à candidature pour un stage à effectuer au Laboratoire Interdisciplinaire de Physique, Université Grenoble Alpes.
Mots clés : Biophysique, fluides complexes, mécanique des milieux continus, biologie du développement, rhéologie, recherche interdisciplinaire.
Directeur de stage :
Jocelyn Étienne, Jocelyn.Etienne@Univ-Grenoble-Alpes.fr
http://www-liphy.ujf-grenoble.fr/jocelyn-etienne

In order to address the complex behaviour of living tissue, we will rely on a very large set of live microscopy data of a very active one observed during the early stages of embryogenesis. Using a mechanical model and a statistical approach of the real data, we will infer both the bulk properties of the tissue and the out-of-equilibrium dynamics conferred by biological activity.

The dynamical and mechanical properties of active matter is a very intense field of research, both theoretically and experimentally [Marchetti et al., 2013]. Biological tissues represent a class of dense active matter, where living cells interact directly and hydrodynamic interactions via a solvent play a minor role. The theoretical description of these systems is either based on a microscopic discrete [Petrolli & Balland, 2019] or on a coarse-grained phenomenological approach [Dicko et al, 2017]. Both cases relie on adhoc assumptions to include active biological effects. We aim at establishing a more realistic mechanical framework for the modeling of active matter, by preserving however a physicist’s reductionist approach. Thereby a first step consists in elucidating the material bulk properties and the resulting microscopic and macroscopic dynamics.
Analysis of real data extracted from video microscopy of a living system will provide the basis for our theoretical developments. We will focus on a 2D model system of living tissues, namely embryonic epithelial tissues. Within the framework of a long term collaboration with developmental biologists of the University of Cambridge, we have access to a large data set of cellular motions (cell divisions and displacements) within monolayers of embryonic tissues. Preliminary work (Master’s project in 2019) shows that this dataset is sufficiently large to allow us to use statistical approaches. This system is driven by cell divisions, cell-cell interactions, and out-of-equilibrium fluctuations transmitted by the cellular cytoskeleton and molecular motors.
The major objective of this Master’s project will be to characterize the bulk properties and dynamics of the tissue using experimental data and to quantify the active materials in terms of macroscopic constitutive laws.

Figure: Illustration of the data available. (a) The membranes of cells in a living tissue are visalised while one of the cells undergoes cell division. As it does so, it deforms strongly neighbouring tissue. Biologist collaborators have analysed dozens of such movies to record the movements of all cells surrounding the dividing one over 40 $\mu$m. Hundreds of cell divisions are visible in any movie, providing a very large dataset. (b) Radial Distribution Function of volume center of cells surrounding a dividing cell at 3 different times into the division process.

Profil de l'étudiant :

• Physique, mécanique ou sciences des données intéressé par le comportement de matériaux complexes.
• Pas de connaissances préalables en biologie requises.

• Stage au LIPHY (Grenoble) sous la supervision de Jocelyn Etienne
• Indemnité forfaitaire de 543,60 euros par mois.
• Durée 5 à 6 mois, prolongement possible.
• Poursuite en thèse souhaitée.

Le Laboratoire Interdisciplinaire de Physique se trouve à Grenoble. Initialement centré sur la physique et l'instrumentation, le Laboratoire est désormais fortement interdisciplinaire, et compte parmi ses membres des chercheurs et doctorants en mécanique, mathématiques appliquées et biologie.

Références

Geometry can provide long-range mechanical guidance for embryogenesis. Dicko M, Saramito P, Blanchard GB, Lye CM, Sanson B, Étienne J. PLoS Comput Biol. 2017