Séminaires passés

Tissue folding is a fundamental process in animal organ development, yet the mechanical mechanisms governing fold stability and unfolding remain incompletely understood. In this seminar, I will discuss the evolution of fold morphology and mechanics during Drosophila wing disc development. I will introduce the techniques we developed to study the apical surface of the tissue and present measurements of fold depth and width that characterize the evolution of folds throughout development. The folding process is closely associated with the presence of a fibrous apical extracellular matrix (aECM) that physically connects opposing sides of the folds. By combining a lateral vertex model incorporating aECM-mediated adhesion with genetic perturbations, we show that the aECM contributes to fold stability and that its removal is necessary for proper unfolding. Furthermore, we demonstrate that perturbations of the aECM during larval development leads to morphological defects in the adult wing. Time permitting, I will also introduce a computational library currently under development that aims to extend the study of folded structures interactively for general surfaces/tissues.

In this work in collaboration with Michael Weinstein, we study wave propagation in continuum two-dimensional honeycomb structures with a non-commensurate line defect or edge. Our model is a Schrodinger operator which interpolates, across the edge, between two distinct bulk (asymptotic) Hamiltonians with a common spectral gap about the Dirac point of an unperturbed honeycomb operator. We seek edge states, eigenstates that are plane wave-like parallel to the edge, and decaying in the transverse direction. For non-commensurate edges, the rigorous definition of these states is nontrivial due to the lack of translation invariance along the edge. To address this, we exploit quasiperiodicity along the edge by expressing the Hamiltonian as the restriction of a 3D (degenerate elliptic) Hamiltonian describing a 3D medium with a 2D interface within which there is periodicity. Using perturbation theory, we construct edge states in this 3D setting and obtain by restriction 2D edge states which are quasiperiodic along the irrational edge. These edge states are seeded by eigenfunctions of an effective Dirac operator, which has an infinite block-diagonal structure due to the non-commensurate geometry. A consequence is that infinitely many edge state eigenpairs arise, whose energies are dense in the perturbed bulk spectral gap. The construction of the edge states requires an omnidirectional non-resonance (no-fold) condition on the dispersion functions of the unperturbed honeycomb Hamiltonian, as well as a Diophantine condition on the edge slope.

Can a mobile agent intercept a drifting target while both are transported by a turbulent flow? I will begin with this Lagrangian control problem for a point agent with limited manoeuvrability. Given full knowledge of the evolving velocity field, optimal control yields minimum-time interception paths despite chaotic dispersion [1]. I will compare these solutions with approximated heuristic policies based on local only hydrodynamic cues and discuss extensions to model-free reinforcement learning strategies [2]. Leaving the Lagrangian control problem aside, I will then turn to the motion of more realistic and complex particles, such as rigid fibers. How do their physical properties, namely length, aspect ratio and inertia, influence their dynamics [3]? Answering this question is important for understanding how particles distribute themselves in a turbulent environment and provides a starting point for designing Lagrangian control strategies for realistic particles, with the aim of controlling specific properties of the advecting Eulerian turbulent flow, such as drag reduction, extreme events or energy dissipation.

[1] Calascibetta, Biferale, Borra, Celani & Cencini. Commun. Phys. 6, 256 (2023).
[2] Calascibetta, Biferale, Borra, Celani & Cencini. Eur. Phys. J. E 46, 9 (2023).
[3] Calascibetta, Allende & Bec. Small-scale alignment and clusters of heavy inertial rods in turbulence (in preparation, 2026).

Thermodynamic trade-offs between speed, accuracy, and dissipation have recently attracted considerable interest in the field of stochastic thermodynamics. In mesoscopic systems, stochastic currents are often the most accessible observables, and such trade-off relations --- formulated in terms of current fluctuations and entropy production in the systems --- help probe the nonequilibrium nature of the dynamics by providing lower bounds on the rate of entropy production.

These results are conventionally formulated in terms of current fluctuations at fixed times. However, events of interest in noisy mesoscopic systems often occur at random times. In this talk I introduce a formulation of such trade-off relations in terms of observables at random times --- particularly first-passage times for fluctuating currents --- and show that first-passage observables can encode thermodynamic information equivalently to current fluctuations. This formulation is powerfully general and while complementary to the conventional approach based on current fluctuations, helps paints a unifying picture of existing results.

Face-to-face interactions reveal recurring patterns, such as broad contact distributions, suggesting the possibility of shared underlying mechanisms. To unveil these mechanisms, we present an original modeling framework for generating temporal networks, designed to incorporate the critical role of spatial constraints in shaping interaction patterns. To do this, we study walkers evolving in a two-dimensional space following simple pedestrian dynamics and extract the temporal network of interactions between them, to be compared against empirical time-varying networks of contact. Starting with the simplest case, the two-dimensional random walk, and gradually incorporating layers of complexity, I will show to what extent empirical observations can be explained with simple dynamics and where deeper sociological understanding and complex mechanisms are required.

Quantifying the irreversibility and dissipation of non-equilibrium processes is crucial to understanding their behavior, assessing their possible capabilities, and characterizing their efficiency. 

We introduce a physical quantity that quantifies the irreversibility of stochastic Langevin systems from the observation of individual molecules’ displacements. Categorizing these displacements into a few groups based on their initial and final position allows us to measure irreversibility precisely without the need to know the forces and magnitude of the fluctuations acting on the system. Our model-free estimate of irreversibility is related to entropy production by a conditional fluctuation theorem and provides a lower bound to the average entropy production. 

We validate the method on single-molecule force spectroscopy experiments of proteins subject to force ramps. We show that irreversibility is sensitive to detailed features of the energy landscape underlying the protein folding dynamics. This suggests that our method can be employed to unveil key properties of protein folding processes and to fingerprint mechanosensitive proteins.