Lorenzo Frigerio | Searcharxiv

arXiv2026

Condensate-mediated shape transformations of cellular membranes by capillary forces

Phase-separated biomolecular condensates with liquid-like properties play a key role in the organization and compartmentalization of the intracellular environment. Condensate-mediated capillary forces acting on membranes drive physiologically important reshaping of membrane-bound organelles, such as vacuoles and autophagosomes. Here, we explore condensate-mediated membrane shape transformations. We employ {\textit{in planta}} live-cell imaging, an \textit{in vitro} reconstitution system with tunable interfacial tension, and computer simulations of an elastic membrane model to describe three morphologies of membrane structures localized at condensate interfaces: tubes, sheets, and cups. We find that the forces associated with high interfacial tension drive the formation of stable sheets, while tubes and cups prevail at lower interfacial tension. We calculate the free energies of each membrane shape and identify the energy barriers that govern the transitions between the shapes. With this approach, we find that shape transformations depend on the history of the interfacial membrane and exhibit a tube-to-cup hysteresis. These findings indicate that temporal control of condensate surface properties can mediate the morphogenesis of cup-like structures in cells, such as the formation of "bulbs" within plant vacuoles. Our results further generalize how the interplay of condensates and membranes contributes to intracellular organization.

cond-mat.soft↗

arXiv2019

Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data

Open data plays a fundamental role in the 21th century by stimulating economic growth and by enabling more transparent and inclusive societies. However, it is always difficult to create new high-quality datasets with the required privacy guarantees for many use cases. This paper aims at creating a framework for releasing new open data while protecting the individuality of the users through a strict definition of privacy called differential privacy. Unlike previous work, this paper provides a framework for privacy preserving data publishing that can be easily adapted to different use cases, from the generation of time-series to continuous data, and discrete data; no previous work has focused on the later class. Indeed, many use cases expose discrete data or at least a combination between categorical and numerical values. Thanks to the latest developments in deep learning and generative models, it is now possible to model rich-semantic data maintaining both the original distribution of the features and the correlations between them. The output of this framework is a deep network, namely a generator, able to create new data on demand. We demonstrate the efficiency of our approach on real datasets from the French public administration and classic benchmark datasets.

cs.CR↗