arXiv subjects

Nicolas Desprat

Publications and source records attributed to Nicolas Desprat.

5 recordsLinked to original sources

arXiv2025

Pure hydrodynamic instabilities in active jets of "puller" microalgae

Active fluids can develop spontaneous flow instabilities and complex patterns. However, spatio-temporal control of active particles has remained challenging, despite its relevance in biological and applied contexts. Here, we harnessed phototaxis to steer millions of swimming ``puller" Chlamydomonas reinhardtii algae to create active jets and control both pearling and buckling instabilities through the preferential orientation of the cells. Our experiments, supported by a full analytical model and simulations, confirm long-standing predictions that self-generated flows can lead to jet destabilization. Our results further indicate that pullers can behave analogously to pushers when their orientation is properly tuned, and demonstrate how light enables efficient control of active fluids.

cond-mat.soft↗

arXiv2025

Hydrodynamic Instabilities of Active Jets

Using a combination of theory, experiments, and numerical simulations, we investigate the stability of coherent structures in a suspension of strongly aligned active swimmers. We show that a dilute jet of pullers undergoes a pearling instability, while a jet of pushers exhibits a helical (or, in two dimensions, zigzag) instability. We further characterise the nonlinear evolution of these instabilities, deriving exact and approximate solutions for the spreading and mutual attraction of puller clusters, as well as the wavelength coarsening of the helical instability. Our theoretical predictions closely match the experimentally observed wavelengths, timescales, and flow fields in suspensions of photophobic algae, as well as results from direct numerical simulations. These findings reveal the intrinsic instability mechanisms of aligned active suspensions and demonstrate that coherent structures can be destabilised by the flows they generate.

cond-mat.soft↗

arXiv2024

Light-induced phase separation with finite wavelength selection in photophobic micro-algae

As for many motile micro-algae, the freshwater species Chlamydomonas reinhardtii can detect light sources and adapt its motile behavior in response. Here, we show that suspensions of photophobic cells can be unstable to density fluctuations, as a consequence of shading interactions mediated by light absorption. In a circular illumination geometry this mechanism leads to the complete phase separation of the system into transient branching patterns, providing the first experimental evidence of finite wavelength selection in an active phase-separating system without birth and death processes. The finite wavelength selection, that can be captured in a simple drift-diffusion framework, is a consequence of a vision-based interaction length scale set by the illumination geometry and depends on global cell density, light intensity and medium viscosity. Finally we show that this active phase separation shields individual cells from the deleterious effects of high light intensity, demonstrating that phototaxis can efficiently contribute to photoprotection through collective behaviors on short timescales.

cond-mat.soft↗

arXiv2015

Inferring Epigenetic Dynamics from Kin Correlations

Populations of isogenic embryonic stem cells or clonal bacteria often exhibit extensive phenotypic heterogeneity which arises from stochastic intrinsic dynamics of cells. The internal state of the cell can be transmitted epigenetically in cell division, leading to correlations in the phenotypic states of cells related by descent. Therefore, a phenotypic snapshot of a collection of cells with known genealogical structure, contains information on phenotypic dynamics. Here we use a model of phenotypic dynamics on a genealogical tree to define an inference method which allows to extract an approximate probabilistic description of phenotypic dynamics based on measured correlations as a function of the degree of kinship. The approach is tested and validated on the example of Pyoverdine dynamics in P. aeruginosa colonies. Interestingly, we find that correlations among pairs and triples of distant relatives have a simple but non-trivial structure indicating that observed phenotypic dynamics on the genealogical tree is approximately conformal - a symmetry characteristic of critical behavior in physical systems. Proposed inference method is sufficiently general to be applied in any system where lineage information is available.

q-bio.PE↗

arXiv2006

Power laws in microrheology experiments on living cells: comparative analysis and modelling

We compare and synthesize the results of two microrheological experiments on the cytoskeleton of single cells. In the first one, the creep function J(t) of a cell stretched between two glass plates is measured after applying a constant force step. In the second one, a micrometric bead specifically bound to transmembrane receptors is driven by an oscillating optical trap, and the viscoelastic coefficient $G_e(ω)$ is retrieved. Both $J(t)$ and $G_e(ω)$ exhibit power law behavior: $J(t)= A(t/t_0)^α$ and $\bar G_e(ω)\bar = G_0 (ω/ω_0)^α$, with the same exponent $α\approx 0.2$. This power law behavior is very robust ; $α$ is distributed over a narrow range, and shows almost no dependance on the cell type, on the nature of the protein complex which transmits the mechanical stress, nor on the typical length scale of the experiment. On the contrary, the prefactors $A_0$ and $G_0$appear very sensitive to these parameters. Whereas the exponents $α$ are normally distributed over the cell population, the prefactors $A_0$ and $G_0$ follow a log-normal repartition. These results are compared with other data published in the litterature. We propose a global interpretation, based on a semi-phenomenological model, which involves a broad distribution of relaxation times in the system. The model predicts the power law behavior and the statistical repartition of the mechanical parameters, as experimentally observed for the cells. Moreover, it leads to an estimate of the largest response time in the cytoskeletal network: $τ_m \approx 1000$ s.

physics.bio-ph↗