Mark Watson. (Photo by A. Ciarlo.) We are delighted to welcome Dr Mark Watson to the Soft Matter Lab.
Dr Watson is a postdoctoral research fellow in the School of Mathematics and Physics at The University of Queensland, Australia. He is also affiliated with the ARC Centre of Excellence in Quantum Biotechnology (QUBIC).
His research primarily focuses on developing methods using rotational optical tweezers (ROTs) to study microscopic systems, with a particular emphasis on microrheology. He has demonstrated the use of ROTs for intracellular viscometry and recently pioneered ultrafast rotational viscometry measurements using rotational ballistic tweezers.
His current research aims to further develop these techniques for applications across a broad range of highly dynamic microscopic systems, opening new possibilities for probing their mechanical and rheological properties at very short timescales.
We are delighted to host Dr Watson at the Soft Matter Lab and look forward to stimulating scientific discussions, exchanges and collaborations during his visit.
GAUDI leverages a hierarchical graph-convolutional variational autoencoder architecture, where an encoder progressively compresses the graph into a low-dimensional latent space, and a decoder reconstructs the graph from the latent embedding. (Image by M. Granfors and J. Pineda.)Unsupervised Graph Deep Learning Uncovers Protein Organization in Super-Resolution Microscopy
Mirja Granfors, Jesús Pineda, Blanca Zufiria Gerbolés, Joana B. Pereira, Carlo Manzo, and Giovanni Volpe Date: 7 October 2026 Time: 11:45 – 13:00 and 17:20 – 19:00 Place: Biblioteket, Arbetets museum, Norrköping
Modern optical techniques such as super-resolution microscopy generate rich, spatially resolved data that can be naturally represented as graphs. However, the structural variability of these graphs poses significant challenges for their analysis and classification, particularly when similar underlying processes can produce substantially different graph realizations.
To address this challenge, we introduce Graph Autoencoder Uncovering Descriptive Information (GAUDI), an unsupervised graph deep learning framework designed to capture both local details and global structure. GAUDI employs a hierarchical graph-convolutional variational autoencoder architecture with pooling and upsampling layers connected through skip connections, preserving essential connectivity information throughout the encoding–decoding process.
Despite substantial variability between graph realizations generated from identical or highly similar underlying parameters, GAUDI consistently maps them to nearby regions of a structured, continuous latent space. This enables the identification of invariant process-level features while separating them from stochastic variability.
We demonstrate this capability using super-resolution microscopy, where GAUDI characterizes protein assemblies and organizes structurally variable graphs according to their underlying state. The framework is further demonstrated on small-world network models, collective motion in the Vicsek model, and age-related changes in brain connectivity. These results highlight GAUDI as a broadly applicable approach for extracting meaningful information from complex graph-structured data across microscopy and other fields.
DeepTrack2 Logo. (Image by J. Pineda.)DeepTrack2: Physics-Based Microscopy Simulations for Deep Learning
Mirja Granfors, Alex Lech, Jiacheng Huang, Eduard Duta, Guillem Guigo i Corominas, Harshith Bachimanchi, Benjamin Midtvedt, Jesús Pineda, Carlo Manzo, and Giovanni Volpe Date: 7 October 2026 Time: 11:45 – 13:00 and 17:20 – 19:00 Place: Biblioteket, Arbetets museum, Norrköping
Deep learning has become an increasingly powerful tool for microscopy image analysis, but training robust models often requires large and diverse datasets that are difficult and time-consuming to acquire experimentally. Physics-based simulations provide a way to generate synthetic microscopy data while retaining control over the optical and physical properties of the imaging process.
DeepTrack2 is a flexible and scalable Python library designed to generate physics-based synthetic microscopy datasets for training deep learning models. It supports a wide range of imaging modalities, including brightfield, fluorescence, darkfield, and holography, and allows users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects and imaging conditions, and apply image augmentations.
Its modular architecture also enables simulations to be executed using a PyTorch backend, providing GPU acceleration and allowing backpropagation through the optical system. This makes it possible to optimize the optical setup and the deep learning model jointly. The DeepTrack2 repository further provides tutorials covering the complete workflow, from physics-based simulation to deep-learning-based analysis using its companion library, deeplay.
Together, these capabilities provide a flexible framework for developing and benchmarking deep learning methods for microscopy while reducing the need for large experimentally acquired training datasets.
EquiNET uses a graph neural network to infer entropy production from non-equilibrium molecular trajectories and uses this information to estimate equilibrium free-energy differences. (Image by S. K. Manikandan.)
Nonequilibrium Fluctuations as Probes of Thermodynamics Date: 6 October 2026 Time: 11:00 Place: PJ seminar room, Fysik Origo
The first and second laws of thermodynamics provide the basic principles for describing energy transformations and the emergence of the arrow of time in physical systems. Heat dissipation and entropy production, central quantities in these laws, are often difficult to quantify precisely in experiments because the associated energy exchanges are distributed over a vast number of typically inaccessible degrees of freedom in the environment. This is especially true at microscopic scales, where the relevant energy scales are often of the order of kB T, comparable to thermal fluctuations in the environment, and can lie several orders of magnitude below the sensitivity limits of state-of-the-art room-temperature calorimetry.
Recent advances in nonequilibrium statistical physics and data-driven inference provide new approaches for overcoming these challenges. In this talk, I will first introduce our theoretical results showing how the time dependence of dynamical fluctuations of a system (due to interactions with its environment) can be exploited to quantify dissipation at scales inaccessible to calorimetry. I will then demonstrate how these measurements can be used to address a long-standing challenge related to inferring equilibrium free-energy differences from nonequilibrium measurements. The results will be illustrated using experiments and simulations of biophysical systems, demonstrating both the practical applicability of the method and the challenges that remain.
Left: trapping of a healthy (biconcave) red blood cell. Right: trapping of an echinocyte-like cell with membrane protrusions. In the representation of the trapping (focused) beam, only rays originating from points close enough to the propagation axis are drawn. More peripheral rays are omitted. (Image from the manuscript.)How morphology affects optical trapping of red blood cells in health and disease
Emir Erdem, Gökberk Kabacaoğlu, Giovanni Volpe, Agnese Callegari, and Luca Biancofiore
Biomedical Optics Express 17, 5355-5372 (2026)
doi: 10.1364/BOE.609102
Optical tweezers are widely used to manipulate microscopic particles and biological cells through optical forces and torques, yet how cell shape controls trapping response has not been fully elucidated. Here, we develop a computational framework to investigate single-beam optical trapping of red blood cells with physiologically and pathologically relevant deformed morphologies. Representative cell shapes are generated parametrically and analyzed using static displacement and rotation tests, together with Brownian dynamics simulations. We show that all considered morphologies remain laterally and orientationally confined in a single-beam trap, while axial localization is weaker by pure optical means and is experimentally realized by the balance between the optical force and the buoyancy-corrected cell weight. Cell geometry strongly affects translational and rotational stiffness, preferred orientation, and Brownian confinement. In particular, shape-induced anisotropy produces distinct dynamical signatures, indicating that optical trapping measurements may be sensitive to morphological alterations beyond simple changes in cell size or volume. These results establish a quantitative connection between red-blood-cell morphology, trap stiffness, and dynamics, providing a computational framework for interpreting morphology-dependent optical trapping experiments.
FIREFLY provides interactive rendering, exploration and analysis of large three-dimensional light-sheet microscopy datasets, combining real-time visualization, volumetric data processing and machine learning-based approaches for improved microscopy image analysis. (Image by J. Tember)FIREFLY: Framework for Interactive Rendering, Exploration and Feature Extraction of Light-Sheet Microscopy
John Tember Date: 25 September 2026 Time: 18:00 – 18:15 Place: Largo Ciro Menotti, Pisa, Italy
Modern tissue-clearing and light-sheet microscopy techniques can generate highly detailed three-dimensional images of biological samples, but the increasing size and complexity of these datasets pose significant challenges for efficient visualization, processing and analysis. Interactive computational tools are therefore essential for exploring large volumetric datasets and extracting quantitative information from them.
FIREFLY is being developed as a framework for interactive rendering, exploration and feature extraction of light-sheet microscopy data. The presentation introduced computational approaches for real-time 3D visualization, volumetric data processing and multi-scan reconstruction, together with ongoing work using self-supervised machine learning to improve the resolution of anisotropic microscopy volumes.
The work was presented at Scintille di Scienza: la Ricerca dal Laboratorio alla Piazza – II Edizione, a public outreach event organized by the Department of Pharmacy at the University of Pisa as part of BRIGHT Night 2026, the European Researchers’ Night in Tuscany. The event brought researchers to the city centre of Pisa for short talks, interactive activities and demonstrations aimed at communicating current research to a broad public audience.
The aim of FIREFLY is to develop computational tools that make increasingly large and complex microscopy datasets easier to explore, process and quantitatively analyse.
Bridging scientific research and innovation to strengthen Europe’s competitiveness and societal impact. (Image generated by ChatGPT from A. Ciarlo.)Enhancing Europe’s competitiveness by empowering researchers as innovators
Amanda K. A. Silva Brun, Giorgio Presidente, Mangala Srinivas, Valentina Cauda, Alberto Fernandez-Tejada, Christine L. Mummery, Rami I. Aqeilan, Thomas Thum, Mehmet C. Onbasli, Robert Colebunders, Gianni Ciofani, Davide Iannuzzi, Marios Avraamides, João T. Barata, Romana Schirhagl, Ana Cecília A. Roque, Luana Dessbesell, Chloé Dupuis, Marco Paggi, Kerstin Kinkelin, Gerard J. J. Boink, Antonella Bongiovanni, Kevin Braeckmans, Kerem Pekkan, Lorenzo Moroni, Twan Lammers, Xiao Xiang Zhu, Adriele Prina-Mello, Carlo S. Casari, Max C. Lemme, Thomas Wolbers, Achillefs N. Kapanidis, Erdem Yörk, Jan S. Kirschke, Philipp-Leo Mengel, Giulio Cerullo, Volker Hessel, Víctor A. de la Peña O’Shea, Aitziber L. Cortajarena, Frank Kirchhoff, Harald H. H. W. Schmidt, Imre Berger, Anne Houdusse, Edgar R. Gomes, Frederic Amant, Tambet Teesalu, Joan Ballester, Juan José Vilatela, Raymond M. Schiffelers, Fabrizio d’Adda di Fagagna, Roman Jerala, Cristina Canal, Sophie Demolombe, Gaetano Gargiulo, Giovanni Volpe, Andrea C. Ferrari, Daniel Gros, Clemens Fuest, and Dario Polli
Science 393, 1302-1304 (2026)
doi: 10.1126/science.ady2388
Europe has a strong scientific research base, yet many promising discoveries still face barriers on the path from academia to technologies, products, and companies. In this Policy Forum, a broad group of European researchers, innovators, and economists examines how these barriers could be reduced to strengthen Europe’s capacity for breakthrough innovation and socioeconomic impact. Focusing particularly on Horizon Europe, the authors propose a more continuous pathway from fundamental research to commercialization, including closer integration between European Research Council Proof of Concept grants and European Innovation Council programs, stronger and more flexible support for early-stage high-risk innovation, and simpler funding procedures. They also highlight the importance of recognizing entrepreneurship within academic careers, improving intellectual-property management, and providing researchers with clearer access to mentoring, industrial partnerships, and spin-off creation. Together, these measures could help translate more of Europe’s scientific excellence into new technologies, businesses, and solutions for society, strengthening Europe’s long-term competitiveness.
Conceptual models of protein folding: funnel versus foldon. (Figure from the Authors of the manuscript.).
How do proteins fold? Date: 21 September 2026 Time: 13:15 Place: LINXS at The Loop, Lund
Giovanni Volpe will give the invited talk “How do proteins fold?” at the TR+ Conference 2026: From Static to Dynamics, organized by LINXS and MAX IV in Lund from 21 to 23 September 2026. The conference brings together researchers working on time-resolved methods to investigate the structural and functional dynamics of biomolecules.
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 six decades ago, this code remains undefined. Although 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.
Reference: Bustamante, C., Kaiser, C. M., Lindahl, E., Sosa, R., & Volpe, G. (2026). “How proteins fold.” Nature Reviews Molecular Cell Biology. Advance online publication.
EquiNET uses a graph neural network to infer entropy production from non-equilibrium molecular trajectories and uses this information to estimate equilibrium free-energy differences. (Image by S. K. Manikandan.)Nonequilibrium Fluctuations as Probes of Thermodynamics Date: 8 September 2026 Time: 10:30 Place: Department of Applied Physics, University of Tokyo
The first and second laws of thermodynamics provide the basic principles for describing energy transformations and the emergence of the arrow of time in physical systems. Heat dissipation and entropy production, central quantities in these laws, are often difficult to quantify precisely in experiments because the associated energy exchanges are distributed over a vast number of typically inaccessible degrees of freedom in the environment. This is especially true at microscopic scales, where the relevant energy scales are often of the order of kB T, comparable to thermal fluctuations in the environment, and can lie several orders of magnitude below the sensitivity limits of state-of-the-art room-temperature calorimetry.
Recent advances in nonequilibrium statistical physics and data-driven inference provide new approaches for addressing these challenges. In this talk, I will first introduce recent theoretical results showing how nonequilibrium fluctuations can be exploited to quantify energy dissipation at scales inaccessible to conventional calorimetry. I will then demonstrate the practical applicability of these results by quantifying energy dissipation from experimental data in real physical systems, as well as estimating free-energy differences from nonequilibrium measurements in highly dissipative regimes where conventional fluctuation-theorem-based methods fail. I will conclude by discussing the remaining challenges and open questions.
Halina Rubinsztein-Dunlop. (Photo by A. Ciarlo.)Quantum sensing and mechanobiology with optically trapped nanodiamonds Professor Halina Rubinsztein-Dunlop Date: 30 September 2026 Time: 12:30-13:30 Place:PJ Salen Registration:register here
Mechanobiology can be studied using optical micromanipulation enabling determination of forces, displacements and torques acting in complex out of equilibrium biological systems. Optical tweezers have been used for such studies very successfully. Transfer of optical angular momentum, (OAM), to matter broadens these applications to the rotational domain. The OAM is used for introducing rotation and torque to the system enabling application such as nano- and microviscoelasticity and production of all optically driven nano and micromachines.
Nanodiamonds (NDs) containing nitrogen- vacancy (NV) centres have raised interest as multifunctional probes for their unique possibility of being fluorescent scanning probes, combining spin optical read-out that makes them unique quantum sensors. Combining optical micromanipulation and NV centres in nanodiamonds (NDs) focuses on the advantages and achievements in controlling the NDs positioning that enables precise magnetic sensing and measurements of other highly relevant parameters of the environment that they are in such as minute temperature changes and PH. The measurements combine force, position and torque with fluorescence detection and optically detected magnetic resonance, ODMR. For high precision measurements of this type it is essential to be able to control precisely the position of the nanodiamonds. We are developing methods that combine rotational optical tweezers and quantum sensing with FNDs to achieve this goal.
Short bio:
Prof. Halina Rubinsztein-Dunlop, is Deputy Director of the ARC CoE in Quantum Biotechnology and runs Translational Research Portfolio there. She is Professor of Quantum Physics, at University of Queensland. At UQ, she leads groups in experimental quantum atom optics, laser micromanipulation and biophotonics. Halina is the recipient of many national and international awards, including Officer in the General Division of the Order of Australia. She is a Fellow of Australian Academy of Science, a Fellow of SPIE, Optica and AIP. In 2024 Halina was awarded 2024 SPIE Directors’ Medal. In the same year she was also awarded an Honorary Doctor of Science honoris causa by the University of Glasgow. In January 2025 Halina was named as a recipient of SPIE Gold Medal 2025. Halina is actively pursuing work in the Equity, Diversity and Inclusion area and is involved in popularisation and promotion of science.
