Soft Matter Lab

An in vivo mimetic liver-lobule-chip (LLoC) for stem cell maturation, and zonation of hepatocyte-like cells on chip published in Lab on a Chip

The image shows a liver-lobule-chip (LLoC) with 21 artificial lobules mimicking liver microarchitecture. Its PDMS design supports diffusion-based perfusion, shear stress, and nutrient gradients and enables iPSC-derived hepatic maturation and spatially organized, zonated function in 3D. *(Image by C. Beck Adiels)*

An in vivo mimetic liver-lobule-chip (LLoC) for stem cell maturation, and zonation of hepatocyte-like cells on chip
Philip Dalsbecker, Siiri Suominen, Muhammad Asim Faridi, Reza Mahdavi, Julia Johansson, Charlotte Hamngren Blomqvist, Mattias Goksör, Katriina Aalto-Setälä, Leena E. Viiri and Caroline B. Adiels
Lab on a Chip 25, 4328 – 4344 (2025)
doi: 10.1039/D4LC00509K

In vitro cell culture models play a crucial role in preclinical drug discovery. To achieve optimal culturing environments and establish physiologically relevant organ-specific conditions, it is imperative to replicate in vivo scenarios when working with primary or induced pluripotent cell types. However, current approaches to recreating in vivo conditions and generating relevant 3D cell cultures still fall short. In this study, we validate a liver-lobule-chip (LLoC) containing 21 artificial liver lobules, each representing the smallest functional unit of the human liver. The LLoC facilitates diffusion-based perfusion via sinusoid-mimetic structures, providing physiologically relevant shear stress exposure and radial nutrient concentration gradients within each lobule. We demonstrate the feasibility of long term cultures (up to 14 days) of viable and functional HepG2 cells in a 3D discoid tissue structure, serving as initial proof of concept. Thereafter, we successfully differentiate sensitive, human induced pluripotent stem cell (iPSC)-derived cells into hepatocyte-like cells over a period of 20 days on-chip, exhibiting advancements in maturity compared to traditional 2D cultures. Further, hepatocyte-like cells cultured in the LLoC exhibit zonated protein expression profiles, indicating the presence of metabolic gradients characteristic of liver lobules. Our results highlight the suitability of the LLoC for long-term discoid tissue cultures, specifically for iPSCs, and their differentiation in a perfused environment. We envision the LLoC as a starting point for more advanced in vitro models, allowing for the combination of multiple liver cell types to create a comprehensive liver model for disease-onchip studies. Ultimately, when combined with stem cell technology, the LLoC offers a promising and robust on-chip liver model that serves as a viable alternative to primary hepatocyte cultures—ideally suited for preclinical drug screening and personalized medicine applications.

Presentation by M. Granfors at EUROMECH Colloquium 656 in Gothenburg, 22 May 2025

Mirja Granfors presenting at the EUROMECH Colloquium. *(Photo by A. Lech.)*

DeepTrack2: Physics-based Microscopy Simulations for Deep Learning
Mirja Granfors

Date: 22 May 2025
Time: 15:15
Place: Veras Gräsmatta, Gothenburg
Part of the EUROMECH Colloquium 656 Data-Driven Mechanics and Physics of Materials

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, enabling the creation of synthetic samples that accurately replicate real experimental conditions. Its modular architecture empowers users to customize optical systems, incorporate optical aberrations and noise, simulate diverse objects across various imaging scenarios, and apply image augmentations. DeepTrack2 is accompanied by a dedicated GitHub page, providing extensive documentation, examples, and an active community for support and collaboration: https://github.com/DeepTrackAI/DeepTrack2.

Presentation by A. Lech at EUROMECH Colloquium 656 in Gothenburg, 22 May 2025

Alex Lech Granfors presenting at the EUROMECH Colloquium. *(Photo by M. Granfors.)*

Deeplay: Enhancing PyTorch with Customizable and Reusable Neural Networks
Alex Lech

Date: 22 May 2025
Time: 15:00
Place: Veras Gräsmatta, Gothenburg
Part of the EUROMECH Colloquium 656 Data-Driven Mechanics and Physics of Materials

Deeplay is a Python-based deep learning library that extends PyTorch, addressing limitations in modularity and reusability commonly encountered in neural network development. Built with a core philosophy of modularity and adaptability, Deeplay introduces a system for defining, training, and dynamically modifying neural networks. Unlike traditional PyTorch modules, Deeplay allows users to adjust the properties of submodules post-creation, enabling seamless integration of changes without compromising the compatibility of other components. This flexibility promotes reusability, reduces redundant implementations, and simplifies experimentation with neural architectures. Deeplay’s architecture is organized around a hierarchy of abstractions, spanning from high-level models to individual layers. Each abstraction operates independently of the specifics of lower levels, allowing neural network components to be reconfigured or replaced without requiring foresight during initial design. Key features include a registry-based system for component customization, support for dynamic property modifications, and reusable modules that can be integrated across multiple projects. As a fully compatible superset of PyTorch, Deeplay enhances its functionality with advanced modularity and flexibility while maintaining seamless integration with existing PyTorch workflows. It extends the capabilities of PyTorch Lightning by addressing not only training loop optimization, but also the flexible and dynamic design of model architectures. By combining the familiarity and robustness of PyTorch with enhanced design flexibility, Deeplay empowers developers to efficiently prototype, refine, and deploy neural networks tailored to diverse machine learning challenges. Deeplay is accompanied by a dedicated GitHub page, featuring extensive documentation, examples, and an active community for support and collaboration.

Invited Seminar by G. Volpe at Cognitive and Behavior Changes in Parkinson’s Disease and Parkinsonism: Advances and Challenges, Santa Maria di Leuca, Italy, 21 May 2025

The Role of Artificial Intelligence in Advanced Neuroimaging Analysis
Giovanni Volpe
Cognitive and Behavior Changes in Parkinson’s Disease and Parkinsonism: Advances and Challenges
Date: 21 May 2025
Time: 11:50
Place: Tricase, Santa Maria di Leuca, Italy

Delayed Active Swimmer in a Velocity Landscape on ArXiv

Experimental setup. (Top) Thermophoretic microswimmer undergoes active Brownian motion in a spatially-varying laser intensity profile that controls the self-thermophoretic propulsion of the swimmer using a feedback loop. (Bottom) Sample trajectory of the microswimmer over 15 minutes in a chamber. Colors indicate instantaneous velocity. *(Image from the manuscript.)*

Delayed Active Swimmer in a Velocity Landscape
Viktor Holubec, Alexander Fischer, Giovanni Volpe, Frank Cichos
arXiv: 2505.11042

Self-propelled active particles exhibit delayed responses to environmental changes, modulating their propulsion speed through intrinsic sensing and feedback mechanisms. This adaptive behavior fundamentally determines their dynamics and self-organization in active matter systems, with implications for biological microswimmers and engineered microrobots. Here, we investigate active Brownian particles whose propulsion speed is governed by spatially varying activity landscapes, incorporating a temporal delay between environmental sensing and speed adaptation. Through analytical solutions derived for both short-time and long-time delay regimes, we demonstrate that steady-state density and polarization profiles exhibit maxima at characteristic delays. Significantly, we observe that the polarization profile undergoes sign reversal when the swimming distance during the delay time exceeds the characteristic diffusion length, providing a novel mechanism for controlling particle transport without external fields. Our theoretical predictions, validated through experimental observations and numerical simulations, establish time delay as a crucial control parameter for particle transport and organization in active matter systems. These findings provide insights into how biological microorganisms might use response delays to gain navigation advantages and suggest design principles for synthetic microswimmers with programmable responses to heterogeneous environments.

Aitor González Marfil joins the Soft Matter Lab

Aitor González Marfil starts his visiting period at the Physics Department of the University of Gothenburg on 19 May 2025.

Aitor is a PhD student at the University of the Basque Country.

During his visit, that will last until the 19 of August, he will focus on machine learning for image analysis.

Robert Sosa Principe joins the Soft Matter Lab

Robert Sosa Principe started his visit at the Physics Department at the University of Gothenburg on 17 May 2025.

Robert is a PostDoc in the group of Prof. Carlos Bustamante at the University of California, Berkeley.

During his visit, he will focus on experiments of single-molecule biophysics.

Invited talk by Sreekanth K. Manikandan at the online Workshop on Stochastic Thermodynamics (WOST), 14th May 2025

Recent advances in nonequilibrium physics allow extracting thermodynamic quantities, such as entropy production, directly from dynamical information in microscopic movies. *(Image by S. Manikandan.)*

Localizing entropy production in cellular processes
Sreekanth Manikandan
Date: 14 Mar 2025
Time: 17:30 CEST
Place: Online
Part of the Workshop on Stochastic Thermodynamics

Quantifying the spatiotemporal forces, affinities, and dissipative costs of cellular-scale non-equilibrium processes from experimental data and localizing it in space and time remain a significant open challenge. Here, I explore how principles from stochastic thermodynamics, combined with machine learning techniques, offer a promising approach to addressing this issue. I will present preliminary results from experiments on fluctuating cell membranes and simulations of non-equilibrium systems in stationary and time-dependently driven states. These studies reveal potential strategies for localizing entropy production in experimental biophysical contexts while also highlighting key challenges and limitations that must be addressed.

SmartTrap: Automated Precision Experiments with Optical Tweezers on ArXiv

Illustration of three different experiments autonomously performed by the SmartTrap system: DNA pulling experiments (top), red blood cell stretching (bottom left), and particle-particle interaction measurements (bottom right). *(Image by M. Selin.)*

SmartTrap: Automated Precision Experiments with Optical Tweezers
Martin Selin, Antonio Ciarlo, Giuseppe Pesce, Lars Bengtsson, Joan Camunas-Soler, Vinoth Sundar Rajan, Fredrik Westerlund, L. Marcus Wilhelmsson, Isabel Pastor, Felix Ritort, Steven B. Smith, Carlos Bustamante, Giovanni Volpe
arXiv: 2505.05290

There is a trend in research towards more automation using smart systems powered by artificial intelligence. While experiments are often challenging to automate, they can greatly benefit from automation by reducing labor and increasing reproducibility. For example, optical tweezers are widely employed in single-molecule biophysics, cell biomechanics, and soft matter physics, but they still require a human operator, resulting in low throughput and limited repeatability. Here, we present a smart optical tweezers platform, which we name SmartTrap, capable of performing complex experiments completely autonomously. SmartTrap integrates real-time 3D particle tracking using
deep learning, custom electronics for precise feedback control, and a microfluidic setup for particle handling. We demonstrate the ability of SmartTrap to operate continuously, acquiring high-precision data over extended periods of time, through a series of experiments. By bridging the gap between manual experimentation and autonomous operation, SmartTrap establishes a robust and open source framework for the next generation of optical tweezers research, capable of performing large-scale studies in single-molecule biophysics, cell mechanics, and colloidal science with reduced experimental
overhead and operator bias.

Series of lectures by C. Bustamante, Waernska Professorship lectures, 29 April – 7 May 2025

Carlos Bustamante. *(Photo by H. P. Thanabalan.)*

Fundamentals and Applications of Single Molecule Force Spectroscopy – Waernska Professorship lectures
Professor Carlos Bustamante, who is visiting the Soft Matter Lab between 28 April and 27 May and is a winner of the Waernska Professorship, will be giving a series of lectures on Fundamentals and Applications of Single Molecule Force Spectroscopy.

Professor Carlos Bustamante from UC Berkeley is a pioneer in the use of optical tweezers for the biomechanical study of single molecules. He will explain the basics of how and why you can perform single-molecule experiments with them.

Here is the schedule and location of the lectures:
– 29 April 2025 from 13:00 to 17:00 in Gustaf Dalén-salen;
– 05 May 2025 from 13:00 to 17:00 in FL71;
– 06 May 2025 from 13:00 to 17:00 in Lecture Hall FL71;
– 07 May 2025 from 13:00 to 17:00 in Lecture Hall KB.