Pattern formation through cell movement: how do cells self-organize?
Multiciliated cells (MCCs) of the Xenopus embryonic epidermis exhibit a regular pattern of distribution, which is largely established through random movement and homotypic repulsion, prior to their insertion in the surface epithelium. The goal of this project is to produce a mathematical model that would recapitulate the process observed in vivo and identify key parameters that influence MCC pattern formation. To this end, the successful applicant will combine quantitative analysis of MCC trajectories obtained by tracking from videomicroscopy movies, performed using stochastic inference techniques, with analysis of cell patterns over time from fixed samples, to infer the relevant parameters in the system (forces, influence of non-MCCs, tissue geometry...). LK's lab will provide existing datasets and may generate new ones, while PR's lab will supervise computational and theoretical approaches. The importance of the identified parameters will be experimentally tested in LK's lab.
Pattern formation, cell motility, self-organization, stochastic inference, trajectory analysis
1. Analyse traces of MCCs from movies, using Stochastic Force Inference (SFI, ), to learn a dynamical model
2. Calibrate and adapt SFI to the specific needs of these datasets to extract intelligible models with few parameters
3. Study of pattern formation in the inferred model, comparison with observed morphologies and study of the biological determinants of its parameters.
A strong profile in theoretical/computational statistical physics and stochastic processes, with interest in applying these to biological problems. Experience in working with experimental biological data is a strong plus. Good programming skills (Python preferably).
In the case of an existing project, please explain the links between the two projects (5 lines)
2 to 5 references related to the project Chuyen, ..., Kodjabachian* and Pasini*. 2021. The Scf/Kit pathway implements self-organized epithelial patterning. Dev. Cell. 56:795-810.
 Boutin and Kodjabachian, 2019. Biology of multiciliated cells. Curr. Opin. Genet. Dev. 56, 1-7.
 Learning force fields from stochastic trajectories. Frishman and Ronceray. PRX 10, 021009 (2020).
 Learning the dynamics of cell-cell interactions in confined cell migration. Brückner et al. PNAS 118, 7 (2021).
3 main publications from each PI over the last 5 years
- Nommick, A., Boutin, C., Rosnet, O., Schirmer, C., Bazellieres, E., Thome, V., Loiseau, E., Viallat, A. and Kodjabachian, L. 2022. Lrrcc1 and Ccdc61 are conserved effectors of multiciliated cell function. JOURNAL OF CELL SCIENCE. 135(4):jcs258960.
- Chuyen, A., Rulquin, C., Daian, F., Thome, V., Clement, R., Kodjabachian, L*$. and Pasini, A*. 2021. The Scf/Kit pathway implements self-organized epithelial patterning. DEVELOPMENTAL CELL. 56:795-810. *Corresponding authors, $Lead contact.
- Revinski, DR†., Zaragosi, L-E†., Boutin, C†., Ruiz Garcia, S., Deprez, M., Rosnet, O., Thomé, V., Mercey, O., Paquet, A., Pons, N., Marcet, B*., Kodjabachian, L*. and Barbry, P*. 2018. CDC20B is required for deuterosome-mediated centriole production in multiciliated cells. equal contribution†, corresponding authors*. NATURE COMMUNICATIONS. 9:4668.
- Nucleation landscape of biomolecular condensates
Shunsuke F. Shimobayashi, Pierre Ronceray, David W. Sanders, Mikko P. Haataja, Clifford P. Brangwynne
Nature 599, 503-506 (2021).
- Learning force fields from stochastic trajectories
Anna Frishman 1) and Pierre Ronceray 1)
Physical Review X 10, 021009 (2020).
- Cell contraction induces long-ranged stress stiffening in the extracellular matrix
Yu Long Han 1 , Pierre Ronceray 1 , Guoqiang Xu, Andrea Malandrino, Roger Kamm, Martin Lenz, Chase P
Broedersz and Ming Guo
Proc. Nat. Acad. Sci. USA, 115, 16, 4075-4080 (2018).