Carlos Bustamante. (Photo by H. P. Thanabalan.)We are delighted to welcome Professor Carlos Bustamante to the Soft Matter Lab as the winner of the prestigious Waernska Professorship.
Professor Bustamante is a world-renowned expert in single-molecule biophysics and a Full Professor of Molecular and Cell Biology, Chemistry and Physics at the University of California, Berkeley, USA.
His pioneering work has significantly advanced our understanding of the physical behaviour of biological molecules. Using techniques such as optical tweezers, atomic force microscopy and fluorescence microscopy, Professor Bustamante has provided key insights into molecular motors, protein and RNA folding, and the mechanisms of gene expression and regulation.
We are honoured to host Professor Bustamante at the Soft Matter Lab and look forward to exciting scientific exchanges and collaborations during his visit.
His visit is currently planned between 28 April and 27 May 2025.
An additional visit, yet to be confirmed, might take place during the fall of 2025.
Linde Viaene presenting at the PhD conference. (Image by S. Kilde Westberg.)Studying heat adaptation in yeast one-molecule at a time: The use of single-molecule microscopy for aggregate identification and tracking.
Linde Viaene Date: 25th of April Time: 13:00 Place: Veras Gräsmatta, Gothenburg
The importance of protein folding and misfolding is indicated by the broad range of clinical manifestations that have protein aggregation at the base, such as neurodegenerative diseases, cancer and type II diabetes. A key factor in (energy) homeostasis is the DNA configuration of chromatin which allows for essential gene expression and adaptation to environmental factors. The Rpd3 deacetylase histone complex (DHAC) plays a crucial role in gene regulation and its disruption impairs stress-induced gene activation, highlighting its importance in cellular adaptation.
Using Saccharomyces cerevisiae as a model system, we aim to investigate the role of chromatin remodelling components in protein aggregation and cellular rejuvenation, which may influence aggregate retention and recovery speed. We will expose yeast cells to stressors such as heat shock, metabolic shifts, and oxidative stress to assess their effects on protein homeostasis and chromatin regulation. Growth assays will evaluate survival rates, while Western blotting will measure Hsp104 expression, a key heat shock protein involved in aggregate clearance. By employing our bespoke single-molecule fluorescence microscope, we will track aggregate formation, clearance, and spatial localization in live cells at molecular precision.
Our preliminary results indicate that some components of the Rpd3L complex, respectively alter the recovery rate after heat stress exposure. Hence, the goal is to explore further candidate genes and to determine their role in the stress-induced response. By elucidating the role of chromatin remodelers in stress adaptation, our findings may inform novel therapeutic strategies for age-related diseases.
Illustration of polymer detachments. At low pH polymers attach weakly to liquid-liquid interfaces. Having the polymer attached also to a colloidal particle allows for an optical tweezers to pull the polymer loose and to detect single detachments. (Image by M. Selin.)Optical Tweezers applications: From particle adsorption to single molecules.
Martin Selin Date: 11 April 2025 Time: 10:30 Place: University of Münster, Germany
Optical tweezers are powerful tools for probing microscale forces in systems ranging from colloidal particles to single molecules. Here, we demonstrate their use in two different fields. First, by trapping individual colloidal particles, we study their adsorption dynamics at liquid–liquid interfaces, highlighting the critical role of surface chemistry and the presence of polymer shells. We also observe reversible polymer attachments and stretching. Second, we apply tweezers to study single-molecule mechanics. By automating these complex biophysical experiments, we enable high-throughput measurements of molecular dynamics. Our results suggest that, like DNA, synthetic polymers can be effectively described by the worm-like chain model.
BRAPH 2 Genesis enables swift creation of custom, reproducible software distributions—tackling the growing complexity of neuroscience by streamlining analysis across diverse data types and workflows. (Image by B. Zufiria-Gerbolés and Y.-W. Chang.)BRAPH 2: a flexible, open-source, reproducible, community-oriented, easy-to-use framework for network analyses in neurosciences
Yu-Wei Chang, Blanca Zufiria-Gerbolés, Pablo Emiliano Gómez-Ruiz, Anna Canal-Garcia, Hang Zhao, Mite Mijalkov, Joana Braga Pereira, Giovanni Volpe
bioRxiv: 10.1101/2025.04.11.648455
As network analyses in neuroscience continue to grow in both complexity and size, flexible methods are urgently needed to provide unbiased, reproducible insights into brain function. BRAPH 2 is a versatile, open-source framework that meets this challenge by offering streamlined workflows for advanced statistical models and deep learning in a community-oriented environment. Through its Genesis compiler, users can build specialized distributions with custom pipelines, ensuring flexibility and scalability across diverse research domains. These powerful capabilities will ensure reproducibility and accelerate discoveries in neuroscience.
Illustration of adsorption process of a polymer coated particle. A single particle is brought to a liquid-liquid interface using an optical tweezers and once the polymer shell makes contact with the interface the particle immediately jumps into the interface. (Image by M. Selin.)Optical tweezers reveal how polymer coated particles jump into liquid-liquid interfaces
Martin Selin Date: 8 April 2025 Time: 17:40 Place: Center for Interdisciplinary Research, Bielefeld University, Germany
Colloidal particles typically require salt to overcome electrostatic barriers and adsorb to liquid-liquid interfaces. Here, we show that coating particles with polymers enables spontaneous adsorption without salt. Our model system consists of silica cores coated with poly(2-(dimethylamino)ethyl methacrylate) (PDMAEMA). Using optical tweezers, we track individual particles showing that the polymer shell makes particles jump into a dodecane–water interface. This behavior extends to other polymers. By tuning pH, we control polymer swelling and adsorption distance. At very low pH, the attachment to the interface is weak enough that the optical tweezers can pull particles out from the interface. During this desorption process we observe single polymers detaching. These findings offer new approaches for designing responsive emulsions.
Cover of the PhD thesis. (Image by L. Natali.)Laura Natali defended her PhD thesis on March 28th, 2025. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 10:00.
Title: Neural Networks for Complex Systems: From Epidemic Modeling to Swarm Robotics
Abstract: Deep learning models, inspired by the structure of the brain, were first developed in the last century. These models are trained to recognize patterns in large amounts of data. Recently, deep learning has made a big impact, both in research and in everyday applications, like healthcare, image recognition, and language translation.
However, despite their advancements, these models still fall short of the abilities found in biological brains, which are adaptable, energy-efficient, and have evolved over millions of years. In contrast, artificial models are specialized and struggle to adapt to new information.
To help address this gap, we have developed a robotic experiment that combines the programmability of artificial neural networks with some of the physical constraints seen in biological systems.
Supervisor: Giovanni Volpe Examiner: Bernhard Mehlig Opponent: Hamid Kellay Committee: Maria Guix Noguera, Juliane Simmchen, Michael Felsberg Alternate board member: Paolo Vinai
From left: Anupam Sengupta (opponent), Harshith Bachimanchi, Giovanni Volpe (supervisor). (Photo by A. Argun.)Harshith Bachimanchi defended his PhD thesis on March 26th, 2025. Congrats!
The defense took place in PJ, Institutionen för fysik, Origovägen 6b, Göteborg, at 13:00.
Title: Deep Learning Enhanced Optical Methods for Single-Plankton Studies
Abstract: Among Earth’s earliest life forms, cyanobacteria reshaped the planet by oxygenating the atmosphere during the Great Oxidation Event 2.4 billion years ago. This process, which led to ozone formation and UV protection, paved the way for more complex photosynthetic organisms—phytoplankton, the eukaryotic descendants of cyanobacteria. Today, phytoplankton drive the global carbon cycle, producing 50–80% of Earth’s oxygen and fueling the marine food web. Microzooplankton consume nearly two-thirds of the organic carbon generated, yet despite their ecological significance, tracking biomass flow at the single-cell level remains a major challenge.
This thesis presents novel methodologies that integrate advanced optical techniques, deep learning, and simulated datasets to analyze microplankton dynamics with unprecedented resolution.
A key contribution is a deep-learning-enhanced holographic microscopy approach that quantifies microplankton biomass at the single-cell level while simultaneously capturing their three-dimensional swimming behavior. This method overcomes computational bottlenecks in traditional holography, enabling high-throughput analysis across diverse species and size ranges. Expanding on this, I demonstrate its application in mixed-species experiments to examine feeding interactions between phytoplankton and microzooplankton, capturing biomass transfer and behavioral shifts during predation.
Beyond direct imaging, this thesis leverages synthetic data to advance microscopy-based research. Neural networks trained on simulated microscopy datasets are used to detect, segment, and classify plankton species while reconstructing motion dynamics. To showcase the versatility of this approach, I present its application in a non-biological setting—detecting bubble-propelled artificial micromotors within complex experimental backgrounds. In addition to object detection, these methods also enable motion characterization of microscopic entities. To demonstrate this, I introduce synthetic microscopy videos that model microscopic organisms undergoing various anomalous diffusion behaviors. This framework is then used to develop a method that extracts motion characteristics without explicit trajectory linking, broadening its applications beyond plankton ecology.
Finally, I investigate how zooplankton—key players in the marine food web—respond to ocean wave-induced light patterns using an LED matrix. The results suggest that zooplankton use steady light sources, such as celestial objects, to ascend more rapidly during favorable low-turbulent conditions, offering new insights into their migratory strategies. Collectively, this thesis bridges marine ecology, microscopy, artificial intelligence, and biophysics to provide new tools for exploring the unseen dynamics that shape our planet.
Memory capacity in aging. A Brain reservoir computing architecture with uniform random signals applied to all nodes. (Image from the article.)Computational memory capacity predicts aging and cognitive decline
Mite Mijalkov, Ludvig Storm, Blanca Zufiria-Gerbolés, Dániel Veréb, Zhilei Xu, Anna Canal-Garcia, Jiawei Sun, Yu-Wei Chang, Hang Zhao, Emiliano Gómez-Ruiz, Massimiliano Passaretti, Sara Garcia-Ptacek, Miia Kivipelto, Per Svenningsson, Henrik Zetterberg, Heidi Jacobs, Kathy Lüdge, Daniel Brunner, Bernhard Mehlig, Giovanni Volpe, Joana B. Pereira
Nature Communications 16, 2748 (2025)
doi: 10.1038/s41467-025-57995-0
Memory is a crucial cognitive function that deteriorates with age. However, this ability is normally assessed using cognitive tests instead of the architecture of brain networks. Here, we use reservoir computing, a recurrent neural network computing paradigm, to assess the linear memory capacities of neural-network reservoirs extracted from brain anatomical connectivity data in a lifespan cohort of 636 individuals. The computational memory capacity emerges as a robust marker of aging, being associated with resting-state functional activity, white matter integrity, locus coeruleus signal intensity, and cognitive performance. We replicate our findings in an independent cohort of 154 young and 72 old individuals. By linking the computational memory capacity of the brain network with cognition, brain function and integrity, our findings open new pathways to employ reservoir computing to investigate aging and age-related disorders.
