arXiv subjects

Maxime Deforet

Publications and source records attributed to Maxime Deforet.

4 recordsLinked to original sources

arXiv2025

Closures of moment expansion of anisotropic active Brownian particles

We study analytically the dynamics of anisotropic active Brownian particles (ABPs), and more precisely their intermediate scattering function (ISF). To this end, we develop a systematic closure scheme for the moment expansion of their Fokker-Planck equation. Starting from the coupled evolution of translational and orientational degrees of freedom, we derive equations for the density, polarization, and nematic tensor fields, which naturally generate an infinite hierarchy of higher-order moments. To obtain explicit solutions, we investigate truncation strategies and analyze closures at different orders. While the closure at lowest order yields Gaussian dynamics with an effective translational diffusion, closures at higher orders incorporate orientational correlations and reproduce non-Gaussian features in the ISF. By confronting these approximations with exact solutions based on spheroidal wave functions and with Brownian dynamics simulations, we identify their range of validity in terms of P\'eclet number, wavenumber, and observation timescales. An advantage of this method is its ability to yield approximate yet explicit expressions not only for the ISF but also for polarization and nematic fields, which are often neglected but relevant in scattering experiments and theoretical modeling. Beyond providing a practical guide to select the appropriate closure according to the spatiotemporal regime, our framework highlights the efficiency of moment-based approaches compared to exact yet implicit formulations. This strategy can be systematically extended to more complex situations, such as propulsion switching, confinement, or external fields, where functional bases for exact solutions are generally unavailable.

cond-mat.soft↗

arXiv2025

Bacterial Glass Transition

Bacterial assemblies exhibit rich collective behaviors that control their biological functions, making them a relevant object of study from an active matter physics perspective. Dense bacterial suspensions self-organize into distinct physical phases with intriguing dynamical properties. Here, we study dense two-dimensional films of swimming bacteria using advanced imaging techniques and machine learning. By varying density, we uncover a bacterial glass transition, a direct active matter analogue of equilibrium glass transitions in colloidal and molecular fluids. The transition is marked by a dramatic slowdown of dynamics with minimal structural change. Strong dynamic heterogeneity emerges in space and time, leading to an anomalous violation of the Stokes-Einstein relation and a growing dynamic correlation length, universally observed across five bacterial strains. Our results establish that bacterial colonies exhibit glassy dynamics, but their living, active nature gives them unique properties, paving the way for new research regarding how non-equilibrium physics impacts biology.

cond-mat.soft↗

arXiv2023

DistNet2D: Leveraging long-range temporal information for efficient segmentation and tracking

Extracting long tracks and lineages from videomicroscopy requires an extremely low error rate, which is challenging on complex datasets of dense or deforming cells. Leveraging temporal context is key to overcoming this challenge. We propose DistNet2D, a new deep neural network (DNN) architecture for 2D cell segmentation and tracking that leverages both mid- and long-term temporal information. DistNet2D considers seven frames at the input and uses a post-processing procedure that exploits information from the entire video to correct segmentation errors. DistNet2D outperforms two recent methods on two experimental datasets, one containing densely packed bacterial cells and the other containing eukaryotic cells. It is integrated into an ImageJ-based graphical user interface for 2D data visualization, curation, and training. Finally, we demonstrate the performance of DistNet2D on correlating the size and shape of cells with their transport properties over large statistics, for both bacterial and eukaryotic cells.

cs.CV↗

arXiv2019

Evolution at the edge of expanding populations

Predicting evolution of expanding populations is critical to control biological threats such as invasive species and cancer metastasis. Expansion is primarily driven by reproduction and dispersal, but nature abounds with examples of evolution where organisms pay a reproductive cost to disperse faster. When does selection favor this 'survival of the fastest?' We searched for a simple rule, motivated by evolution experiments where swarming bacteria evolved into an hyperswarmer mutant which disperses $ \sim 100\%$ faster but pays a growth cost of $\sim 10 \%$ to make many copies of its flagellum. We analyzed a two-species model based on the Fisher equation to explain this observation: the population expansion rate ($v$) results from an interplay of growth ($r$) and dispersal ($D$) and is independent of the carrying capacity: $v=2\sqrt{rD}$. A mutant can take over the edge only if its expansion rate ($v_2$) exceeds the expansion rate of the established species ($v_1$); this simple condition ($v_2 > v_1$) determines the maximum cost in slower growth that a faster mutant can pay and still be able to take over. Numerical simulations and time-course experiments where we tracked evolution by imaging bacteria suggest that our findings are general: less favorable conditions delay but do not entirely prevent the success of the fastest. Thus, the expansion rate defines a traveling wave fitness, which could be combined with trade-offs to predict evolution of expanding populations.

q-bio.PE↗