Missions

The successful candidate will work at the interface of biological physics, agent-based simulations and machine learning to turn quantitative imaging data into a mechanistic, testable model of spindle positioning. In particular, we expect automatic Cytosim parameters inference from quantitative imaging data to produce a quantitative model that tests the mechanistic hypothesis and guides the search for molecular players.

Activités

(1) The chosen candidate will build a comprehensive 3‑D agent‑based Cytosim model of neural‑stem‑cell (NSC) mitotic spindle positioning, incorporating all mechanisms identified and suggested by experiments. It will involve extending the core of Cytosim to apply external forces, allow time-dependent parameter changes, and condition-based parameter switches to simulate genetic knockdowns and laser-ablation experiments. (2) Develop AI‑driven model calibration by designing a deep‑learning pipeline mapping data extracted from experiments onto force fields and use these predictions to initialise Cytosim simulations. (3) Use the simulation to sort out the regulation over space and time of forces that position and orient the spindle under different geometries and perturbations. (4) Contribute to writing the articles and to documenting the code to release a publicly available Cytosim extension and parameter inference.

Compétences

PhD in biological physics, computational physics or computer science with proven experience in biophysics.
Demonstrable experience in numerical simulations and machine learning/deep learning. Experience in image processing, statistics, data science, or C++ coding is appreciated.
Motivated by a multidisciplinary environment and enjoying working in a team.
Motivated to produce open‑source tools under FAIR principles.
Excellent communication skills in English (scientific writing, presentations). Speaking French is not mandatory.

Contexte de travail

Project:
During cell division, the correct orientation of the mitotic spindle is essential for maintaining tissue integrity and homeostasis, as it regulates the distribution of cell fate determinants in asymmetric divisions. In the model system Drosophila neural stem cells (NSCs), it has been established that cortical pulling orients the spindle. Régis Giet's laboratory has expertise in this model system 1-4. In contrast to predictions, his team's recent findings have revealed that the mitotic spindles of these stem cells tend to position toward the basal cortex, despite the cortical traction forces present on the apical side. These results suggest that spindle positioning mechanisms are not solely dependent on apical pulling forces.

Meanwhile, Jacques Pecreaux Lab has investigated the forces positioning the spindle in the C. elegans zygote, through an approach combining image processing, modelling and simulations 5,6. Both labs teamed up to map the forces involved in spindle positioning in the NSCs, characterising the molecular components, including novel microtubule-associated proteins or post-translational modifications, and recapitulating quantitatively the mechanism into a model in the shape of an agent-centred simulation. The computer simulation will use cytosim, a framework mastered in Pecreauxlab 6,7, to propose and validate a new spindle positioning model. Finding such previously uncharacterised mechanisms is expected to have far-reaching implications for understanding cell fate determination, tissue development, especially in stem cells.

Environment
Work will be carried out within the CeDRE team led by Jacques Pécréaux, in close collaboration with Régis Giet's team at the Institut de Génétique et Développement de Rennes (IGDR). The IGDR is a fundamental research institute under the supervision of the CNRS, the University of Rennes and INSERM. Its research covers a wide range of disciplines, including cell and developmental biology, biophysics, genetics and genomics, bioinformatics and advanced microscopy. The CeDRE 'Reverse Engineering of Cell Division' team is interdisciplinary, with specialists in biology, physics, image processing and artificial intelligence studying cell division using a cellular biophysics approach. We aim to understand the robustness of cell division by measuring and modelling the mechanical interactions between the molecular players.

Informations complémentaires

See offer https://igdr.univ-rennes.fr/sites/igdr.univ-rennes.fr/files/medias/files/Anonce%20Postdoc_spin-up_v0p5c_EN_E%2B.pdf