Active Matter deals with the study of microscopic agents able to exert self-propulsion forces on their medium. These microscopic agents can model various entities present in a large range of scales in Nature; from bacterias and flying birds to man-made self-phoretic colloids. The presence of self-propulsion drives the active agents out of equilibrium and allows for the emergence of landmark phenomena, both at the level of a single agent and at the collective level in ensembles of agents. In this presentation, I will first characterize such nonequilibrium phenomena for a single active particle. I will then move to the characterization of different collective behaviors as a function of the microscopic interactions between the active agents. In particular, I will assess how topological, repulsive and nonreciprocal interactions interplay with the emergence of collective motion.

We consider the replication of a polymer template via the active template-directed assembly and disassembly of monomeric building blocks, which consumes fuel energy (ATP). This process competes with the passive spontaneous assembly and disassembly of monomers, which produces random polymer sequences. We obtain a phase diagram for the accuracy of the polymer copies, measured by their mean monomer error fraction. A non-equilibrium phase transition between accurate and inaccurate copies of the template is observed when the driving fuel energy is varied. In this transition, the fuel energy threshold depends on the complexity (entropy) of the polymer and is thus akin to Landauer’s principle. A cost-accuracy trade-off is observed as a function of the number of monomer types, which could be a small step towards understanding why the genetic alphabet uses four letters.

BiFeO3 is a multiferroic material featuring ferroelectricity and noncollinear antiferromagnetism. Dynamic and efficient control of the characteristic spin texture of BiFeO3 is attractive for emerging quantum devices. Crystal-field d → d excitations localised on Fe atomic sites in BiFeO3 induce a complex interplay among the spin, charge and lattice degrees of freedom, making them relevant for manipulation of the spin texture. In this work, ab initio methods based on the GW approximation and the Bethe-Salpeter equation are used to characterize localised spin-flip excitations within Fe-3d shell. These excitations are strongly bound and appear deep within the electronic gap. Their spin-content and strong localisation are protected by the d5 antiferromagnetic ordering. The underlying crystal symmetry gives rise to chiral spin-flip exciton states localized on distinct Fe centers which possess net angular momentum of ±h/2π. These chiral excitons couple selectively to light of a particular circular polarisation and are confined to a particular Fe magnetic sub-lattice. As a consequence, net spin-magnetisation can be achieved using circularly polarised light coupling with exciton of desired chirality, thereby modulating the antiferromagnetic texture and giving rise to transient ferrimagnetism.

We investigate the information-theoretical limits of inference tasks in epidemic spreading on graphs, in the large-size limit. The typical inference tasks consist in computing observables of the posterior distribution of the epidemic model given observations taken from a ground truth (sometimes called planted) random trajectory. We give theoretical predictions on the posterior probability distribution of the trajectory of each individual, conditioned to observations on the state of individuals at given times, focusing on the Susceptible Infectious (SI) model. The epidemic dynamics induces non-trivial long-range correlations among individuals' states, resulting in non-local correlated quenched disorder which unfortunately is typically hard to handle. To overcome this difficulty, we divide the dynamical process into two sets of variables: a set of stochastic independent variables (representing transmission delays), plus a set of correlated variables (the infection times) that depend deterministically on the first. Treating the former as quenched variables and the latter as dynamic ones, computing disorder average becomes feasible by means of the Replica Symmetric cavity method. In the Bayes-optimal condition, i.e. when true dynamic parameters are known, the inference task is expected to fall in the Replica Symmetric regime. We indeed provide predictions for the information theoretic limits of various inference tasks, in form of phase diagrams. We also identify a region, in the Bayes-Optimal setting, with strong hints of Replica Symmetry Breaking. When true parameters are unknown, we show how a maximum-likelihood procedure is able to recover them with mostly unaffected performance.

Stochastic processes have been in the last decades very efficient for modelling biological systems, essentially because of the natural randomness that governs them. Genetic network regulation, molecular transport inside cells, and metabolic processes all share high randomness and variability, taking place at finite temperature. Life itself is an emergent process that came out of randomness. This is why complex systems physics have a central role for understanding living systems. In this seminar I will describe three biological processes where a physics approach has helped in the understanding of the phenomenon and to interesting generalizations.
The first one concerns avalanches of fractures. Many glassy and amorphous materials, such as martensites, exhibit characteristic behaviours during constraint-induced fractures. These fractures manifest as avalanche processes, with statistics often following a power-law distribution, reminiscent of collective behaviour and self-organized criticality. Similar avalanches of fractures occur in living systems, resembling a glassy network with a frozen structure, notably observed in the actin cytoskeleton (CSK). The CSK comprises microfilaments organized into higher-order structures via dynamic assembly-disassembly mechanisms with cross-linkers. Experiments indicate that cells respond to external constraints with a cascade of random and abrupt ruptures of their CSK. These behaviours are analysed in experimental data on CD34+ cells from healthy and leukemic bone marrows, revealing log-normal distribution of rupture events. To interpret this behaviour, a minimal (1D) stochastic model is proposed, explaining energy released along rupture events as a sum of a multiplicative cascade process. The model distinguishes between brittle failures, corresponding to irreversible ruptures, and ductile failures, resulting from dynamic cross-linker unbindings. Brittle failures are relatively more prominent in leukemic cells, suggesting greater fragility and different CSK architecture. Further analysis explores the distribution of a sum of correlated random variables from a multiplicative process, highlighting regions of log-normal and power-law distributions in a phase diagram.
The second one is a chemical reaction network, the dual phosphorylation/dephosphorylation cycle, which allows the cell to switch between active and inactive states. I introduce the Random Walk Approximation (RWA), a new method to approximate the stationary solution of master equations describing stochastic processes taking place on graphs. Our approach is suitable for non-linear master equations with conserved entities, common also in other biological systems like gene regulatory networks. The RWA allows having a simple analytical, even though approximated, form of the solution, which is global and easier to deal with than the standard System Size Expansion (SSE). Here, I give some theoretically sufficient conditions for the validity of the RWA and show its interest in biological predictions.
The third one is about the concept of Synthetic Lethality to treat blood cancer (AML). Differential expression analysis of RNA-Seq data provide primary molecular markers that are propagated in protein-protein interaction networks, to identify secondary not altered candidate genes of Synthetic Lethality. I present the physical foundation of this method, which enables to rank the gene based on their “diffusive power”. The identified markers are first reduced by the ranking given by the diffusion and by the centrality of the gene inside the network and then DNA Damage Response (DDR)-related genes for which a drug inhibitor is available are selected. The resulting drugs will be screened ex-vivo on primary leukemic cells. Results showing the efficacy of the method in selecting potentially successful therapy on tumoral cell lines will be presented.
Finally, the importance of physical modelling in biological systems is mainly embodied by the creation of phase diagrams which create possible unexplored scenario to be experimentally tested.

The history of the universe between inflation and the onset of BBN - covering half its lifetime on a log scale - is essentially unconstrained by observations. In this talk, I will discuss how general features of String Theory motivate an unusual cosmology in this period, (co-)dominated by the energy density of scalar fields such as moduli and axions. After reviewing the physics of moduli and axions in realistic string compactifications, I will describe how exotic epochs such as kination and trackers can arise in this setting, and present a complete, alternative history of the early universe based on such considerations. In the second part of the talk, I will discuss some observational consequences, and in particular show how perturbation growth in these stringy cosmologies is substantially enhanced compared to the standard ones. If time permits, I will conclude with some applications to baryogenesis, dark matter and gravitational wave production.

Based on 2311.12429, 2401.04064 + work in progress

In 2022, the CNRS Institute of Physics (now CNRS-Physics) commissioned G. Debregeas and myself to write a text on the impact of the ecological, energy and digital transitions on research in the laboratories (sobriety). This text would then be used for the CNRS-Physics 2030 outlook. So we set up a bureau with, in addition to G. Debregeas and myself, Laëtitia Marty (I. Neel), Delphine Debarre (Liphy), Guillaume Roux (LPTMS), Sylvain Capponi (LPT Toulouse) and Christophe Arnold (Gemac - Versailles) for a group made up of researchers, teachers and research support staff. The idea was also to try to involve the community as much as possible in this work.

So I propose to present the approach we have chosen. I will then outline the 12-page text we produced in July 2023, which concerns changes in our profession that we think will be important in the medium term. Finally, I’d like to conclude with a discussion of the approach underway in my laboratory, the INSP, for shorter-term actions aimed at trying to reduce our laboratory’s Carbon Footprint.

This talk will be followed by a discussion on this topic led by the Labo 1.5 team.

Distinguishing edited cells from non-edited/off-target edits would transform the manufacturing of cell editing products. Visualizing the separation of bacterial colony function would open up our fundamental understanding of microbiology. Identifying chemical cues in pap smear results before the appearance of cells with odd morphologies could lead to earlier identification of dysplasia, improving patient outcomes and treatment options. In short, imaging the dynamic chemical landscape within cells and tissues would revolutionize biological discovery and medicine.
Molecular vibrational imaging techniques detect the small oscillations between bonded atoms, providing dense spectral information about composition and state without the addition of fluorophores or dyes. Technologies, such as Raman and infrared microscopies, have offered this capability for over half a century, but significant limitations in speed, resolution, or sample preparation have prevented their ubiquity.
In this presentation, I will present our developments at NIST of broadband coherent anti-Stokes Raman scattering (BCARS), a coherent Raman imaging method with an unprecedented combination of speed, sensitivity, and spectral breadth; and our development of IR microscopy for live-cell imaging, which is enabling time-course IR imaging of cell cultures at the benchtop rather than synchrotron sources.

Symmetries are a powerful tool in the physicist’s toolkit, and when it comes to symmetric functions, rarely can one find functions as symmetric as automorphic forms. What makes certain automorphic forms powerful is their connection to arithmetic, via the theory of L functions. They are core to some of the most challenging and ambitious open problems in modern mathematics, and played a central role for example in the proof of Fermat’s last theorem. These arithmetic structures are valuable to physicists too. The “how” will be addressed in this talk. I will go over what automorphic forms are, how they give rise to arithmetic structures in string theory and quantum field theory, and some applications. Due to the technical nature of this subject, most of it will be an overview with a few new insights and WIP towards the end.