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Welcome to Pierre Ronceray's research group web page. We study living matter, from proteins to cells to tissues, and aim at unfolding simple physical laws governing the assembly, mechanical and dynamical properties of these complex materials. We use a dual theoretical approach for this: on the one hand, we invent and study simple models to explain experimental results and predict new mechanisms. On the other, we design novel inference methods to measure the dynamical properties of these materials, such as force, stress and diffusivity fields, from experimental data.
We are part of the Turing Centre for Living Systems (CENTURI), a vibrant community of biologists, biological physicists and mathematicians. We are located in the Physics and Engineering for Living Systems department of the Centre Interdisciplinaire de Nanosciences de Marseille (CINAM), in Marseille, France, in the beautiful campus of Luminy.
Research
Research
Stochastic inference: learning physical models from dynamical data
Stochastic inference: learning physical models from dynamical data
The dynamics of biological systems, from proteins to cells to organisms, is complex and stochastic. To decipher their physical laws, we need tools to bridge between experimental observations and theoretical modeling. Our main research goal is to design, develop and improve such inference algorithms that can learn physically interpretable models with high precision from limited experimental trajectories. By collaborating with experimental groups, we then make use of these methods to discover new physics in a variety of biological and soft matter systems.
This research project is funded by a 2023 ERC Starting Grant titled "SuperStoc - Super-resolved stochastic inference: learning the dynamics of soft biological matter", starting Oct 2023. We have open positions for postdocs and PhD students - inquiries welcome!
Other research directions, past and present, include...
Other research directions, past and present, include...
Fiber networks mechanics
Fiber networks mechanics
How do biopolymer networks deform under stress and transmit forces?
Frustrated self-assembly
Frustrated self-assembly
How do complex, low-symmetry objects such as proteins generically self-assemble?
Biomolecular condensates
Biomolecular condensates
Intracellular phase separation leads to the formation of liquid protein droplets. Like oil in water?
People
People
João Valeriano
João Valeriano
PhD student
Reya Negi
Reya Negi
PhD student
Pedro Harunari
Pedro Harunari
Postdoc
Stefano Pagnin
Stefano Pagnin
Master student
Alumni
Alumni
Arthur Coët, PhD student 2022-2026
Andonis Gerardos, PhD student 2021-2025
Florian Goirand, Postdoc 2024-2025
Yirui Zhang, Postdoc 2021-2023
Ludivine Chaix, Master student 2021
Resources
Resources
Stochastic Force Inference (SFI): a method to infer the force and diffusion fields from noisy trajectories of overdamped systems, developed with Anna Frishman. Check out the 2020 Physical Review X paper and the GitHub package (in Python). Next version coming soon! Underdamped Langevin Inference, a similar inverse method for the underdamped case, developed with David Brückner and Chase Broedersz (2020 Physical Review Letters paper) was merged into the same package.
Where to find us
Where to find us
Office G4.23
CINaM, Bâtiment TPR1
163 Avenue de Luminy
13009 Marseille France
Contact
Contact
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