Villie, A. | bioRxiv Science

bioRxiv2025

Active bacterial pattern formation in evaporating droplets

Bacteria living on surfaces are often confined to droplets. When these droplets evaporate, the motion of the liquid-air interface and the associated internal capillary flow confine the bacteria. Here we study how E. coli bacteria interact with this capillary confinement and agglomerate at the droplets contact line. We identify three different types of bacterial pattern formation that depend on the bacterial activity and the environmental conditions imposed by the evaporating droplet. When the evaporation is fast, the bacteria are slow or the suspension is dilute, a uniform contact-line deposit forms. However, when the capillary confinement concentrates the bacteria at the contact line beyond a critical number density, localized collective motion spontaneously emerges. In that case, the bacteria induce a local stirring of the liquid that allows them to self-organize into periodic patterns and enables them to collectively escape from the contact line. At very high number densities, these periodic patterns get destabilized by bacterial turbulence in the bulk of the droplet resulting in the formation of mobile bacterial plumes at the contact line. Our results show how the subtle interplay between the bacteria and the capillary flow inside the droplet that surrounds them governs their dispersal. Significance StatementAn evaporating sessile droplet is a common natural habitat to bacteria. Bacteria that live inside the droplet are exposed to a confinement caused by the moving liquidair interface, and an evaporation-driven capillary flow that agglomerates them at the contact line. Here we show how bacteria interact with this confining flow. We identify three vastly different types of bacterial self-organization that depend on the bacterial activity and the environmental conditions imposed by the droplet. Our work is a first step towards understanding how the interplay between motile bacteria and the interfacial flows that exist in evaporating droplets affects their deposition onto surfaces, which is key to their future survival.

biophysics↗

bioRxiv2022

Neural Networks beyond explainability: Selective inference for sequence motifs

Over the past decade, neural networks have been successful at making predictions from biological sequences, especially in the context of regulatory genomics. As in other fields of deep learning, tools have been devised to extract features such as sequence motifs that can explain the predictions made by a trained network. Here we intend to go beyond explainable machine learning and introduce SEISM, a selective inference procedure to test the association between these extracted features and the predicted phenotype. In particular, we discuss how training a one-layer convolutional network is formally equivalent to selecting motifs maximizing some association score. We adapt existing sampling-based selective inference procedures by quantizing this selection over an infinite set to a large but finite grid. Finally, we show that sampling under a specific choice of parameters is sufficient to characterize the composite null hypothesis typically used for selective inference--a result that goes well beyond our particular framework. We illustrate the behavior of our method in terms of calibration, power and speed and discuss its power/speed trade-off with a simpler data-split strategy. SEISM paves the way to an easier analysis of neural networks used in regulatory genomics, and to more powerful methods for genome wide association studies (GWAS).

bioinformatics↗