Biology subjects

Vernerey, F. J.

Publications and source records attributed to Vernerey, F. J..

4 recordsLinked to original sources

bioRxiv2026

Stress-Induced Mechanical Memory in Respiratory Mucus: Anisotropy, Network Reorganization, and Directional Transport

Mucus transport is essential for lung health, as ciliated cells constantly propel mucus outward to clear bacteria, viruses, and particles. This defense relies on a material that must be elastic enough to maintain ciliary traction, but capable of reorganizing under sustained directional loading. How the mucin network reconciles these demands, and whether it retains a memory of the stresses it experiences, remains poorly understood. Here we show that lung mucus develops a persistent, direction-dependent mechanical asymmetry under physiologically relevant stress--a mechanical memory encoded in the slow scaffold of the network. Using bulk rheology, we find that directional pre-stress produces a residual anisotropy that grows with stress magnitude and persists long after the load is removed. A transient network model attributes this memory to a separation of timescales between transient bonds and a long-lived crosslink scaffold, and particle-tracking microrheology confirms that the memory reorganizes the network geometry at the scale of biological particles, biasing tracer diffusion along the axis of applied stress. The stresses required to induce memory are within the range generated by ciliary beating and remain below the mucus yield threshold, suggesting that mucociliary clearance operates in a regime where directional alignment accumulates without compromising the coherence of the mucus layer. This proximity to yield may not be coincidental, it allows mucus to accumulate mechanical memory under physiological forcing while remaining poised to flow during clearance events such as coughing.

biophysics↗

bioRxiv2025

A Morphodynamic Network Model to Describe Cell Organization and Nematic Ordering in Monolayers

To robustly model nematic ordering in cell monolayers, it is crucial to accurately capture cell geometries, which in turn direct the formation of patterns in cell alignment. In this regard, existing agent-based models (ABMs), such as vertex and Voronoi models, lack generality. Current models often fail to accommodate complex cell geometries (such as elongated or non-convex cells) or implement thermodynamically well-posed rules for cell rearrangements. These features are essential for describing the biophysics of morphogenetic processes where irregular cell shapes and dynamic cell rearrangements play central roles. To address the limitations of existing models, we present a novel computational framework, termed the morphodynamic network model (MNM), for simulating confluent cell monolayers. The MNM is a cell center-based ABM in which the cell centers (nodes) are connected to form a dynamic triangulation (network). The node positions and network structure, recording connections between neighboring cells, evolve according to a split-step approach. First, node positions are updated with the network structure (or topology) fixed, then the network topology is updated with node positions fixed. We apply the MNM to study the morphodynamic behavior of cell monolayers, including fluidization, elasto-visco-plastic responses, nematic ordering, and substrate-induced cell alignment. The MNM is designed to capture complex behaviors and morphologies, offering a flexible and robust tool for studying multicellular phenomena.

biophysics↗

bioRxiv2021

Enhanced diffusion by reversible binding to active polymers

Cells are known to use reversible binding to active biopolymer networks to allow diffusive transport of particles in an otherwise impenetrable mesh. We here determine the motion of a particle that experiences random forces during binding and unbinding events while being constrained by attached polymers. Using Monte-Carlo simulations and a statistical mechanics model, we find that enhanced diffusion is possible with active polymers. However, this is possible only under optimum conditions that has to do with the relative length of the chains to that of the plate. For example, in systems where the plate is shorter than the chains, diffusion is maximum when many chains have the potential to bind but few remain bound at any one time. Interestingly, if the chains are shorter than the plate, we find that diffusion is maximized when more active chains remain transiently bound. The model provides insight into these findings by elucidating the mechanisms for binding-mediated diffusion in biology and design rules for macromolecular transport in transient synthetic polymers. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=92 SRC="FIGDIR/small/428690v1_ufig1.gif" ALT="Figure 1"> View larger version (20K): org.highwire.dtl.DTLVardef@1f09992org.highwire.dtl.DTLVardef@f26b4borg.highwire.dtl.DTLVardef@162368corg.highwire.dtl.DTLVardef@eb2b45_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2021

Computational exploration of treadmilling and protrusion growth observed in fire ant rafts

Condensed active matter systems regularly achieve cooperative emergent functions that individual constituents could not accomplish alone. The rafts of fire ants (Solenopsis invicta) are often studied in this context for their ability to create structures comprised entirely of their own bodies, including tether-like protrusions that facilitate exploration of flooded environments. While similar protrusions are observed in cytoskeletons and cellular aggregates, they are generally dependent on morphogens or external gradients leaving the isolated role of local interactions poorly understood. Here we demonstrate through an ant-inspired, agent-based numerical model how protrusions in ant rafts may emerge spontaneously due to local interactions and how phases of exploratory protrusion growth may be induced by increased ant activity. These results provide an example in which functional morphogenesis of condensed active matter may emerge purely from locally-driven collective motion and may provide a source of inspiration for the development of autonomous active matter and swarm robotics.

biophysics↗