Invited Talk by G. Volpe at XVII Congress of the Spanish Biophysical Society, Castelldefels, Spain, 30 June 2023

(Image by A. Argun)
Deep Learning for Imaging and Microscopy
Giovanni Volpe
XVII Congress of the Spanish Biophysical Society, Castelldefels, Spain, 30 June 2023
Date: 30 June 2023
Time: 11:00

Invited Talk by G. Volpe at Active Matter at Surfaces and in Complex Environments, Dresden, 20 June 2023

Illustration of anomalous diffusion. (Image by G. Muñoz-Gil)
The anomalous diffusion challenge 2
Giovanni Volpe
Active Matter at Surfaces and in Complex Environments, Dresden, Germany
Date: 20 June 2023
Time: 15:30

Emir Erdem joins the Soft Matter Lab

(Photo by A. Ciarlo.)
Emir Erdem joined the Soft Matter Lab on June 17, 2023.

Emir is an undergraduate student at the Mechanical Engineering Department of Bilkent University in Ankara, Turkey.

During his time at the Soft Matter Lab, he will be working on the modeling of red blood cells with geometrical optics.

He is supported by an Erasmus Traineeship scholarship. He will stay in our lab till September 13, 2023.

Christian Rutgersson defended his Master Thesis on 9 June 2023. Congrats!

Christian Rutgersson defended his Master thesis in Complex Adaptive Systems at Chalmers University of Technology on 9 June 2023 at 17:00. Congrats!

Title: Characterizing Active Matter Particle Systems with Graph Neural Networks

Abstract:
Biological systems often have self-organizing properties. On the microscopic scale, the self-organization may be driven by constituents that generate their own motility by expending energy. Systems made up of such constituents, that self-propel, are termed active matter. Via this definition, it is clear that it is of importance to understand the rules that govern the microscopic constituents of an active matter in order to understand the active matter itself. Even though active matter is a topic of interest today, finding good methods of qualitatively and quantitatively characterizing the constituents of active matter remains an issue. Coincidentally, different types of artificial neural networks (ANN) have, in recent years, been used increasingly in research settings with great success. One such network is called the graph neural network (GNN). As the name suggests, this network is specifically designed to work with graphs as input data. Graphs can act as a useful representation of a system of particles, including active matter systems. Therefore, this project aims to characterize the forces that underpin an active matter system consisting of interacting particles that also have an active component, using a special type of GNN called message passing network (MPN). This was done by simulating the active matter using a Python code written from scratch, and training the MPN with standard machine learning algorithms. In the end, the simulations were found to give rise to characteristic active matter phenomena, and the MPN was able to correctly predict the force dynamics of a particle in the given active matter system.

Supervisors: Miguel Ruiz Garcia, Carlo Manzo, Jesus Pineda Castro, Giovanni Volpe
Examiner: Giovanni Volpe
Opponent: Ludvig Lindahl

Place: Nexus
Time: 9 June, 2023, 12:00

John Klint and Niphredil Klint defended their Master Thesis on 7 June 2023. Congrats!

John Klint and Niphredil Klint defended their Master thesis in Physics at the University of Gothenburg on 7 June 2023 at 17:00. Congrats!

Self-Organized colloidal molecule in a traveling wave pattern. (Image by J. and N. Klint)
Title: Light-Controlled Self-Organization of Active Molecules

Abstract:
Active matter systems can be found on many different length- and time-scales in nature. Tiny molecular machines, colonies of bacteria and swarming insects are all examples of such systems. What they all have in common is that they are composed of agents that convert energy into different types of directed motion. This activity occurs only when the agents are in a non-equilibrium state. Often, the interactions give rise to emergent behaviours otherwise not observed for single individuals. A key aspect of active matter systems is that without an energy source, the agents do not exhibit any directed motion and therefore no emergence. The energy source may consist of, for example, light, heat, chemical reactions or vibrations.

Research into active matter often involves laboratory experiments and these can be both expensive and time-consuming to set up. In this project, we explore a simple yet powerful numerical method designed to be efficient but still capable of capturing essential phenomena of light-activated systems. We consider two distinct types of colloidal particles, one that absorbs light and one that does not absorb light. When light is absorbed by one of the particle species, a temperature gradient is generated. Both types of particles are attracted to higher temperatures, and this phoretic attraction is the only interaction at a distance considered between the particles. Since the only particles that generate a temperature gradient are the ones that absorb light, there is an effective non-reciprocal phoretic interaction, which is directed from centre to centre. To avoid unphysical overlaps in the simulation, we implemented a volume exclusion scheme to account for the finite size and hard-core nature of the particles.

Through simulations, we examined and catalogued emergent properties for clusters of particles and statistically determined their speed and rotational frequency. We also investigated cluster lifetimes and categorised different formations of active colloidal molecules. Furthermore, we implemented a number of different ways to simulate the illumination of the agents, from homogeneous light to square and Gaussian light pulses. We successfully induced several emergent properties, such as cluster disintegration immediately followed by regeneration (in a cellular automata-like fashion), as well as speed and rotational frequency modulation and orientation of clusters in the direction of the wavefront.

The results obtained from our simulations are in agreement with previous experimental research on similar non-reciprocal systems governed by phoretic interactions. Our model and its implementation is capable of capturing a wide range of emergent behaviours. We have confirmed that the model can be used to explore how various light environments influence the behaviour of light-activated agents. The minimalistic approach of our work can be seen as a vantage point for further numerical studies of active matter systems.

Examiner: Giovanni Volpe
Supervisor: Agnese Callegari
Opponent: Mathias Samuelsson

Place: von Bahr
Time: 7 June, 2023, 17:00

Presentation by A. Callegari at AI for Scientific Data Analysis, Gothenburg, 31 May 2023

Focused rays scattered by an ellipsoidal particles. (Image reproduced from: 10.1021/acsphotonics.2c01565.)
Faster and more accurate geometrical-optics optical force calculation using neural networks
Agnese Callegari

Optical forces are often calculated by discretizing the trapping light beam into a set of rays and using geometrical optics to compute the exchange of momentum. However, the number of rays sets a trade-off between calculation speed and accuracy. Here, we show that using neural networks permits one to overcome this limitation, obtaining not only faster but also more accurate simulations. We demonstrate this using an optically trapped spherical particle for which we obtain an analytical solution to use as ground truth. Then, we take advantage of the acceleration provided by neural networks to study the dynamics of an ellipsoidal particle in a double trap, which would be computationally impossible otherwise.

Date: 31 May 2023
Time: 10:45
Place: MC2 Kollektorn
Event: AI for Scientific Data Analysis: Miniconference

Presentation by C. B. Adiels at AI for Scientific Data Analysis, Gothenburg, 31 May 2023

Phase-contrast image before virtual staining. (Image reproduced from https://doi.org/10.1101/2022.07.18.500422.)
Dynamic live/apoptotic cell assay using phase-contrast imaging and deep learning
Caroline B. Adiels

Chemical live/dead assay has a long history of providing information about the viability of cells cultured in vitro. The standard methods rely on imaging chemically-stained cells using fluorescence microscopy and further analysis of the obtained images to retrieve the proportion of living cells in the sample. However, such a technique is not only time-consuming but also invasive. Due to the toxicity of chemical dyes, once a sample is stained, it is discarded, meaning that longitudinal studies are impossible using this approach. Further, information about when cells start programmed cell death (apoptosis) is more relevant for dynamic studies. Here, we present an alternative method where cell images from phase-contrast time-lapse microscopy are virtually-stained using deep learning. In this study, human endothelial cells are stained live or apoptotic and subsequently counted using the self-supervised single-shot deep-learning technique (LodeSTAR). Our approach is less labour-intensive than traditional chemical staining procedures and provides dynamic live/apoptotic cell ratios from a continuous cell population with minimal impact. Further, it can be used to extract data from dense cell samples, where manual counting is unfeasible.

Date: 31 May 2023
Time: 10:30
Place: MC2 Kollektorn
Event: AI for Scientific Data Analysis: Miniconference