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Jesus Pineda defended his PhD thesis on November 11th, 2025. Congrats!

Jesus Pineda defended his PhD thesis on November 11th, 2025. Congrats!
The defense took place in SB-H7 lecture hall, Institutionen för fysik, Johanneberg Campus, Göteborg, at 9:00.

Title: Inductive Biases for Efficient Deep Learning in Microscopy

Abstract: Deep learning has become an indispensable tool for the analysis of microscopy data, yet its integration into routine research remains uneven. Several factors contribute to this gap, including the limited availability of well-annotated datasets and the high computational demands of modern architectures. Microscopy introduces further challenges, as it spans diverse modalities and scales, from proteins to tissues, producing heterogeneous data that defy standardization. Generating reliable annotations also requires expertise and time, while unequal access to high-performance computing further widens the divide between well-resourced institutions and smaller laboratories.

This dissertation argues that the prevailing paradigm of scaling models with ever-larger datasets and computational resources yields diminishing returns for microscopy. Instead, it explores the role of inductive biases as a foundation for building models that are more data-efficient, computationally accessible, and scientifically meaningful. Inductive biases are structural assumptions embedded in model design that guide learning toward patterns aligned with the underlying problem. The first part of this work examines their central role in the advancement of modern deep learning and the diverse ways they shape model behavior.

This potential is demonstrated through three case studies. First, MAGIK employs graph neural networks to analyze biological dynamics in time-lapse microscopy, uncovering local and global properties with high precision, even when trained on limited data. Next, MIRO leverages recurrent graph neural networks to process single-molecule localization datasets, improving the efficiency and reliability of clustering for variable biological structures and scales while retaining strong generalization with minimal supervision. Finally, GAUDI introduces a representation learning framework for characterizing biological systems, providing a physically meaningful representation space for interpretable and transferable analysis.

The findings presented here demonstrate that the integration of inductive biases provides a cohesive strategy to extend the reach of deep learning in the life sciences, enhancing accessibility and ensuring scientific utility under resource constraints.

Thesis: https://gupea.ub.gu.se/items/672c7946-51d6-4773-ad8c-35a3eed41499

Supervisor: Giovanni Volpe
Co-Supervisor: Carlo Manzo
Examiner: Raimund Feifel
Opponent: Anna Kreshuk
Committee: Juliette Griffié, Daniel sage, Daniel Persson
Alternate board member: Jonas Enger

Enhanced spatial clustering of single-molecule localizations with graph neural networks published in Nature Communications

MIRO employs a recurrent graph neural network to refine SMLM point clouds by compressing clusters around their center, enhancing inter-cluster distinction and background separation for efficient clustering. (Image by J. Pineda.)
Enhanced spatial clustering of single-molecule localizations with graph neural networks
Jesús Pineda, Sergi Masó-Orriols, Montse Masoliver, Joan Bertran, Mattias Goksör, Giovanni Volpe and Carlo Manzo
Nature Communications 16, 9693 (2025)
arXiv: 2412.00173
doi: 10.1038/s41467-025-65557-7

Single-molecule localization microscopy generates point clouds corresponding to fluorophore localizations. Spatial cluster identification and analysis of these point clouds are crucial for extracting insights about molecular organization. However, this task becomes challenging in the presence of localization noise, high point density, or complex biological structures. Here, we introduce MIRO (Multifunctional Integration through Relational Optimization), an algorithm that uses recurrent graph neural networks to transform the point clouds in order to improve clustering efficiency when applying conventional clustering techniques. We show that MIRO supports simultaneous processing of clusters of different shapes and at multiple scales, demonstrating improved performance across varied datasets. Our comprehensive evaluation demonstrates MIRO’s transformative potential for single-molecule localization applications, showcasing its capability to revolutionize cluster analysis and provide accurate, reliable details of molecular architecture. In addition, MIRO’s robust clustering capabilities hold promise for applications in various fields such as neuroscience, for the analysis of neural connectivity patterns, and environmental science, for studying spatial distributions of ecological data.

How Do Proteins Fold? on ArXiv

Conceptual models of protein folding: funnel versus foldon. (Figure from the Authors of the manuscript.)
How Do Proteins Fold?
Carlos Bustamante, Christian Kaiser, Erik Lindahl, Robert Sosa, Giovanni Volpe
arXiv: 2510.27074

How proteins fold remains a central unsolved problem in biology. While the idea of a folding code embedded in the amino acid sequence was introduced more than 6 decades ago, this code remains undefined. While we now have powerful predictive tools to predict the final native structure of proteins, we still lack a predictive framework for how sequences dictate folding pathways. Two main conceptual models dominate as explanations of folding mechanism: the funnel model, in which folding proceeds through many alternative routes on a rugged, hyperdimensional energy landscape; and the foldon model, which proposes a hierarchical sequence of discrete intermediates. Recent advances on two fronts are now enabling folding studies in unprecedented ways. Powerful experimental approaches; in particular, single-molecule force spectroscopy and hydrogen (deuterium exchange assays) allow time-resolved tracking of the folding process at high resolution. At the same time, computational breakthroughs culminating in algorithms such as AlphaFold have revolutionized static structure prediction, opening opportunities to extend machine learning toward dynamics. Together, these developments mark a turning point: for the first time, we are positioned to resolve how proteins fold, why they misfold, and how this knowledge can be harnessed for biology and medicine.

Myxococcus xanthus for active matter studies: a tutorial for its growth and potential applications published in Soft Matter

Myxococcus xanthus colonies develop different strategies to adapt to their environment, leading to the formation of macroscopic patterns from microscopic entities. (Image by the Authors of the manuscript.)
Tutorial for the growth and development of Myxococcus xanthus as a Model System at the Intersection of Biology and Physics
Jesus Manuel Antúnez Domínguez, Laura Pérez García, Natsuko Rivera-Yoshida, Jasmin Di Franco, David Steiner, Alejandro V. Arzola, Mariana Benítez, Charlotte Hamngren Blomqvist, Roberto Cerbino, Caroline Beck Adiels, Giovanni Volpe
Soft Matter 21, 8602-8623 (2025)
arXiv: 2407.18714
doi: 10.1063/5.0235449

Myxococcus xanthus is a unicellular organism known for its capacity to move and communicate, giving rise to complex collective properties, structures and behaviors. These characteristics have contributed to position M. xanthus as a valuable model organism for exploring emergent collective phenomena at the interface of biology and physics, particularly within the growing domain of active matter research. Yet, researchers frequently encounter difficulties in establishing reproducible and reliable culturing protocols. This tutorial provides a detailed and accessible guide to the culture, growth, development, and experimental sample preparation of M. xanthus. In addition, it presents several exemplary experiments that can be conducted using these samples, including motility assays, fruiting body formation, predation, and elasticotaxis—phenomena of direct relevance for active matter studies.

Andrea Schiano di Colella joins the Soft Matter Lab

Andrea Schiano di Colella. (Photo by A. Ciarlo.)
Andrea Schiano di Colella started his PhD at the Physics Department of the University of Gothenburg on the 24th of October 2025.

Andrea has a Master degree in Theoretical Physics from the University of Naples “Federico II”, Italy.

During the course of his PhD, as part of the GREENS MSCA Doctoral Network he will focus on the development of deep learning based protocols for accurate autonomous microscopy.

Video‐rate tunable colour electronic paper with human resolution published in Nature

High-resolution display of “The Kiss” on Retina E-Paper vs. iPhone 15: Photographs comparing the display of “The Kiss” on an iPhone 15 and Retina E-paper. The surface area of the Retina E-paper is ~ 1/4000 times smaller than the iPhone 15. (Image by the Authors of the manuscript.)
Video‐rate tunable colour electronic paper with human resolution
Ade Satria Saloka Santosa, Yu-Wei Chang, Andreas B. Dahlin, Lars Osterlund, Giovanni Volpe, Kunli Xiong
Nature 646, 1089-1095 (2025)
arXiv: 2502.03580
doi: 10.1038/s41586-025-09642-3

As demand for immersive experiences grows, displays are moving closer to the eye with smaller sizes and higher resolutions. However, shrinking pixel emitters reduce intensity, making them harder to perceive. Electronic Papers utilize ambient light for visibility, maintaining optical contrast regardless of pixel size, but cannot achieve high resolution. We show electrically tunable meta-pixels down to ~560 nm in size (>45,000 PPI) consisting of WO3 nanodiscs, allowing one-to-one pixel-photodetector mapping on the retina when the display size matches the pupil diameter, which we call Retina Electronic Paper. Our technology also supports video display (25 Hz), high reflectance (~80%), and optical contrast (~50%), which will help create the ultimate virtual reality display.

Martin Selin defended his PhD thesis on October 8th, 2025. Congrats!

Cover of the PhD thesis. (Image by M. Selin.)
Martin Selin defended his PhD thesis on October 8th, 2025. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 13:00.

Title: Advanced and Autonomous Applications of Optical Tweezers

Abstract: Optical tweezers have become a central tool, using lasers to manipulate and probe objects with exceptional precision enabling single-molecule, single-cell, and single-particle studies. However, this precision comes at the cost of throughput.

By developing a fully autonomous system we can adress this limitation of optical tweezers. The system is capable of perfoming multiple different experiments independently and of operating for over 10 hours continously. Using the same system we also investigate particle adsorption into liquid-liquid interfaces revealing never before seen dynamics.

These developments help optical tweezers by bridging the gap between single-molecule, cell or particle studies and ensemble measurements, enabling the application of deep learning for advanced modeling and unlocking the potential of optical tweezers for large, data-driven studies.

Thesis: https://gupea.ub.gu.se/handle/2077/87446?show=full

Supervisor: Giovanni Volpe
Examiner: Raimund Feifel
Opponent: Borja Ibarra
Committee: Dag Hanstorp, Timo Betz, Kristine Berg-Sørensen
Alternate board member: Paolo Vinai

Prakhar Dutta joins the Soft Matter Lab

Prakhar Dutta. (Photo by A. Ciarlo.)
Prakhar Dutta started his PhD at the Physics Department of the University of Gothenburg on the 1st of October 2025.

Prakhar has a Master’s degree in Biomedical Engineering from RWTH Aachen University in Germany.

During the course of his PhD, as part of the SPM 4.0 MSCA Doctoral Network he will focus on the development of deep learning based packages for processing of Atomic Force Microscopy data and on the development of a photonic force microscope.

Workshop by Y.-W. Chang at NEMES 2025, Gothenburg, 26 September 2025

Massimiliano Passaretti (left) and Yu-Wei Chang (right) at NEME 2025. (Photo courtesy of Clarion Hotel Draken.)
Graph theory and deep learning pipelines
Yu-Wei Chang, Massimiliano Passaretti
NEMES 2025, 24-26 September, 2025
Date: 25 September 2025
Time: 12:45 – 14:00
Place: Clarion Hotel Draken

This workshop begins with a practical introduction to graph theory, then guides participants through BRAPH 2 to build connectomes, compute graph measures, and run group comparisons, followed by a hands-on deep-learning pipeline. It demonstrates a unified GUI/command-line workflow, a unique architecture of BRAPH 2, helping participants move smoothly from the GUI to scripts. This workshop also guides participants to reproduce multiplex and deep-learning results on their computers from the BRAPH 2 bioRxiv preprint.