Measuring and Modeling Soft Living Matter

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) and 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.

We have an open position for fully funded PhD (proposal). Contact me to apply!

Research

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.

Other research directions, past and present, include...

Fiber networks mechanics

How do biopolymer networks deform under stress and transmit forces?

Frustrated self-assembly

How do complex, low-symmetry objects such as proteins generically self-assemble?

Biomolecular condensates

Intracellular phase separation leads to the formation of liquid protein droplets. Like oil in water?

People

Pierre Ronceray

Principal Investigator. CV

Andonis Gerardos

PhD student

Yirui Zhang

Postdoc

Arthue Coët

PhD student

Co-supervised with Mar Benavides, MIO

Alumni

Ludivine Chaix, Master student

Resources

Stochastic Force Inference: 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).

Underdamped Langevin Inference: a similar inverse method for the underdamped case, developed with David Brückner and Chase Broedersz. Check out the 2020 Physical Review Letters paper and the Github package in Python.

Contact

Email: pierre.ronceray (guesswhat) univ-amu.fr

Office/mail: Bureau G4.23, CINaM, Bâtiment TPR1, 163 Avenue de Luminy, 13009 Marseille France

The Parc National des Calanques, a short walk from the Luminy campus...