Input graph structure including a redundant number of edges. (Image by J. Pineda.)MAGIK: Microscopic motion analysis through graph inductive knowledge Jesús Pineda
Characterizing dynamic processes in living systems provides essential information for advancing our understanding of life processes in health and diseases and for developing new technologies and treatments. In the past two decades, optical microscopy has undergone significant developments, enabling us to study the motion of cells, organelles, and individual molecules with unprecedented detail at various scales in space and time. However, analyzing the dynamic processes that occur in complex and crowded environments remains a challenge. This work introduces MAGIK, a deep-learning framework for the analysis of biological system dynamics from time-lapse microscopy. MAGIK models the movement and interactions of particles through a directed graph where nodes represent detections and edges connect spatiotemporally close nodes. The framework utilizes an attention-based graph neural network (GNN) to process the graph and modulate the strength of associations between its elements, enabling MAGIK to derive insights into the dynamics of the systems. MAGIK provides a key enabling technology to estimate any dynamic aspect of the particles, from reconstructing their trajectories to inferring local and global dynamics. We demonstrate the flexibility and reliability of the framework by applying it to real and simulated data corresponding to a broad range of biological experiments.
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
Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)Bringing microplankton into focus: Deep learning meets holographic microscopy Harshith Bachimanchi
30 June 2023, 12:40 CEST
The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.
(Image by A. Argun)Deep Learning for Imaging and Microscopy
Giovanni Volpe DINAMO 2023, Svolvaer, Lofoten Islands, Norway Date: 15 June 2023 Time: 08:30
Light-driven micromachines. (Image by G. Wang.)Nanophotonic encoding of light-driven micromachines
Gan Wang, Marcel Rey, Mahdi Shanei, Kunli Xiong, Einstom Engay, Mikael Käll, and Giovanni Volpe Date: 13 June 2023 Time: 21:00 (CEST）
On-chip micromotors hold significant application potential in various fields, including cells, microfluidic manipulation, and the micro integration of machines. .The driving mechanism plays a crucial role in the design of micromotors. Currently, various methods such as static electricity, light, magnetism, chemical energy, and mechanical conduction are utilized for this purpose. Optics, in particular, offers distinct advantages including precise control, addressability, non-contact operation, and compatibility with diverse liquid environments. However, existing optically actuated on-chip motors necessitate high energy input, resulting in phototoxicity concerns and hindrances to large-scale manipulation. Furthermore, achieving precise control over speed and direction remains challenging, along with difficulties in establishing coupling among multiple devices.
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
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
Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)Quantitative microplankton tracking by holographic microscopy and deep learning Harshith Bachimanchi
1 June 2023, 15:00 CEST
The marine microbial food web plays a central role in the global carbon cycle. However, our mechanistic understanding of the ocean is biased toward its larger constituents, while rates and biomass fluxes in the microbial food web are mainly inferred from indirect measurements and ensemble averages. Yet, resolution at the level of the individual microplankton is required to advance our understanding of the microbial food web. Here, we demonstrate that, by combining holographic microscopy with deep learning, we can follow microplanktons throughout their lifespan, continuously measuring their three-dimensional position and dry mass. The deep-learning algorithms circumvent the computationally intensive processing of holographic data and allow rapid measurements over extended time periods. This permits us to reliably estimate growth rates, both in terms of dry mass increase and cell divisions, as well as to measure trophic interactions between species such as predation events. The individual resolution provides information about selectivity, individual feeding rates, and handling times for individual microplanktons. The method is particularly useful to detail the rates and routes of organic matter transfer in micro-zooplankton, the most important and least known group of primary consumers in the oceans. Studying individual interactions in idealized small systems provides insights that help us understand microbial food webs and ultimately larger-scale processes. We exemplify this by detailed descriptions of micro-zooplankton feeding events, cell divisions, and long-term monitoring of single cells from division to division.
Input graph structure including a redundant number of edges. (Image by J. Pineda.)Geometric deep learning reveals the spatiotemporal features of microscopic motion Jesús Pineda
Characterizing dynamic processes in living systems provides essential information for advancing our understanding of life processes in health and diseases and for developing new technologies and treatments. In the past two decades, optical microscopy has undergone significant developments, enabling us to study the motion of cells, organelles, and individual molecules with unprecedented detail at various scales in space and time. However, analyzing the dynamic processes that occur in complex and crowded environments remains a challenge. This work introduces MAGIK, a deep-learning framework for the analysis of biological system dynamics from time-lapse microscopy. MAGIK models the movement and interactions of particles through a directed graph where nodes represent detections and edges connect spatiotemporally close nodes. The framework utilizes an attention-based graph neural network (GNN) to process the graph and modulate the strength of associations between its elements, enabling MAGIK to derive insights into the dynamics of the systems. MAGIK provides a key enabling technology to estimate any dynamic aspect of the particles, from reconstructing their trajectories to inferring local and global dynamics. We demonstrate the flexibility and reliability of the framework by applying it to real and simulated data corresponding to a broad range of biological experiments.
