Presentation by Lucas Le Nagard, 15 March 2023

Propulsion of a giant unilamellar vesicle containing E.coli cells. (From: doi:10.1073/pnas.2206096119)
Giant lipid vesicles propelled by encapsulated bacteria
Lucas Le Nagard
15 March 2023
11:00, PJ

I will present the results of a recent study of motile Escherichia coli bacteria encapsulated in lipid vesicles. For slightly deflated vesicles, swimming bacteria deform the vesicles and extrude membrane tubes reminiscent of those seen in eukaryotic cells infected by Listeria monocytogenes. These membrane tubes couple with the flagella of the enclosed bacteria to generate a propulsive force, turning the initially passive vesicles into swimmers. A simple theoretical model used to estimate the magnitude of the propulsive force demonstrates the efficiency of this physical coupling. Interestingly, such vesicle propulsion was not seen in recent studies of swimmers encapsulated in vesicles. While pointing to new design principles for conferring motility to artificial cells, our results illustrate how small differences often matter in active matter physics.

Presentation by Sreekanth K Manikandan, 10 February 2023

Inferring entropy production in microscopic systems
Sreekanth K. Manikandan
Stanford University
10 February 2023, 15:00, Raven and Fox

An inherent feature of small systems in contact with thermal reservoirs, be it a pollen grain in water, or an active microbe flagellum, is fluctuations. Even with advanced microscopic techniques, distinguishing active, non-equilibrium processes defined by a constant dissipation of energy (entropy production) to the environment from passive, equilibrium processes is a very challenging task and a vastly developing field of research. In this talk, I will present a simple and effective way to infer entropy production in microscopic non-equilibrium systems, from short empirical trajectories [1]. I will also demonstrate how this scheme can be used to spatiotemporally resolve the active nature of cell flickering [2]. Our result is built upon the Thermodynamic Uncertainty Relation (TUR) which relates current fluctuations in non-equilibrium states to the entropy production rate.


[1] Inferring entropy production from short experiments [ Phys. Rev. Lett. 124, 120603 (2020) ]

[2] Estimate of entropy generation rate can spatiotemporally resolve the active nature of cell flickering [arXiv:2205.12849]

Bio: Sreekanth completed his PhD at the department of Physics, Stockholm University, in June 2020. His PhD supervisor was Supriya Krishnamurthy. From August 2020 – October 2022, Sreekanth was a Nordita fellow postdoc in the soft condensed matter group at Nordita. Currently, he is a postdoctoral scholar at the Department of Chemistry at Stanford University, funded by the Wallenberg foundation.

Alfred Bergsten joins the Soft Matter Lab

(Photo by A. Argun.)
Alfred Bergsten joined the Soft Matter Lab on 17 January 2023.

Alfred is a master student in Complex Adaptive Systems at Chalmers University of Technology.

During his time at the Soft Matter Lab, he will study the self-assembly of colloids in the presence of travelling waves.

Presentation by Natsuko Rivera-Yoshida, 19 January 2023

M. xanthus cell-cell and cell-particle local interactions during cellular aggregation.
Transitions to multicellularity: the physical environment at the microscale
Natsuko Rivera-Yoshida
19 January 2023
16:30, Nexus

Physical environment contribute to both the robustness and the variation of developmental trajectories and, eventually, to the evolutionary transitions. But how? Myxococcus xanthus is a soil bacterium and is widely used as a biological model. In starvation conditions, cells move individually over the substrate into growing groups of cells which, eventually, organize into three-dimensional structures called fruiting bodies. Commonly, this developmental process is studied using standard experimental protocols that employ homogeneous and flat agar substrates, without considering ecologically relevant variables. However M. Xanthus has shown to drastically alter its development when modifying variables such as the substrate topography or stiffness. This modifications occur with trait and scale specificity, at the level of individual cells, large group of cells, fruiting bodies and also at the population scale. We use experimental and analytical tools to study how multicellular organization is altered at different spatial scales and developmental moments.

Presentation by Andreas Menzel, 19 January 2023

Individual and collective motion of nematic, polar, and chiral actively driven objects
Andreas Menzel
19 January 2023
15:30, Nexus

Actively driven objects comprise a manifold of possible different realizations: from self-propelling bacteria and artificial phoretically driven colloidal particles via vibrated hoppers to walking pedestrians. We analyze basic theoretical models to identify generic features of subclasses of such agents. Within this framework, we first address nematic objects [1]. They predominantly propel along one specific axis of their body, but do not feature an explicit head or tail. That is, they can move either way by spontaneous symmetry breaking. This leads to characteristic kinks along their trajectories. Second, we study chiral objects that show persistent bending of their trajectories and migrate in discrete steps [2]. When, additionally, they tend to migrate towards a fixed remote target, rich nonlinear dynamics emerges. It comprises period doubling and chaotic behavior as a function of the tendency of alignment, which is reflected by the trajectories. Third, we consider the collective motion of continuously moving chiral objects in crystal-like arrangements [3]. We here identify a localization transition with increasing chirality or self-shearing phenomena within the crystal-like structures. Overall, we hope by our work to stimulate experimental realization and observation of the various investigated systems and phenomena.

[1] A. M. Menzel, J. Chem. Phys. 157, 011102 (2022).
[2] A. M. Menzel, resubmitted.
[3] Z.-F. Huang, A. M. Menzel, H. Löwen, Phys. Rev. Lett. 125, 218002 (2020).

Short Bio:
Andreas Menzel studied physics at the University of Bayreuth (Germany), where he also completed his PhD on the continuum theory of soft elastic liquid-crystalline composite materials. After postdoctoral stays at the University of Illinois at Urbana-Champaign with Prof. Nigel Goldenfeld and at the Max Planck Institute for Polymer Research in Mainz in the department headed by Prof. Kurt Kremer, as well as research stays at Kyoto University with Prof. Takao Ohta, he completed his Habilitation at Heinrich Heine University Düsseldorf at the Theory Institute for Soft Matter headed by Prof. Hartmut Löwen. Amongst others, Andreas is interested in developing and applying explicit Green’s functions methods, statistical descriptions, and continuum theories on soft matter, addressing, for example, functionalized elastic composite materials and active matter. In 2020 he moved as a Heisenberg Fellow of the German Research Foundation to Otto von Guericke University Magdeburg (Germany), where he now heads the department on Theory of Soft Matter / Biophysics.

Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion published in Nature Machine Intelligence

Input graph structure including a redundant number of edges. (Image by J. Pineda.)
Geometric deep learning reveals the spatiotemporal fingerprint of microscopic motion
Jesús Pineda, Benjamin Midtvedt, Harshith Bachimanchi, Sergio Noé, Daniel Midtvedt, Giovanni Volpe, Carlo Manzo
Nature Machine Intelligence (2023)
arXiv: 2202.06355
doi: 10.1038/s42256-022-00595-0

The characterization of dynamical processes in living systems provides important clues for their mechanistic interpretation and link to biological functions. Thanks to recent advances in microscopy techniques, it is now possible to routinely record the motion of cells, organelles, and individual molecules at multiple spatiotemporal scales in physiological conditions. However, the automated analysis of dynamics occurring in crowded and complex environments still lags behind the acquisition of microscopic image sequences. Here, we present a framework based on geometric deep learning that achieves the accurate estimation of dynamical properties in various biologically-relevant scenarios. This deep-learning approach relies on a graph neural network enhanced by attention-based components. By processing object features with geometric priors, the network is capable of performing multiple tasks, from linking coordinates into trajectories to inferring local and global dynamic properties. We demonstrate the flexibility and reliability of this approach by applying it to real and simulated data corresponding to a broad range of biological experiments.

Faster and more accurate geometrical-optics optical force calculation using neural networks published in ACS Photonics

Focused rays scattered by an ellipsoidal particles (left). Optical torque along y calculated in the x-y plane using ray scattering with a grid of 1600 rays (up, right) and using a trained neural network (down, right). (Image by the Authors of the manuscript.)
Faster and more accurate geometrical-optics optical force calculation using neural networks
David Bronte Ciriza, Alessandro Magazzù, Agnese Callegari, Gunther Barbosa, Antonio A. R. Neves, Maria A. Iatì, Giovanni Volpe, Onofrio M. Maragò
ACS Photonics, 2022
doi: 10.1021/acsphotonics.2c01565
arXiv: 2209.04032

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.

Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps published in Biomedical Optics Express

Example of final segmentation with the UNet-dm of the specular microscopy image of a severe case of cornea guttata. (Image by the Authors of the manuscript.)
Corneal endothelium assessment in specular microscopy images with Fuchs’ dystrophy via deep regression of signed distance maps
Juan S. Sierra, Jesus Pineda, Daniela Rueda, Alejandro Tello, Angelica M. Prada, Virgilio Galvis, Giovanni Volpe, Maria S. Millan, Lenny A. Romero, Andres G. Marrugo
Biomedical Optics Express 14, 335-351 (2023)
doi: 10.1364/BOE.477495
arXiv: 2210.07102

Specular microscopy assessment of the human corneal endothelium (CE) in Fuchs’ dystrophy is challenging due to the presence of dark image regions called guttae. This paper proposes a UNet-based segmentation approach that requires minimal post-processing and achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs’ dystrophy. We cast the segmentation problem as a regression task of the cell and gutta signed distance maps instead of a pixel-level classification task as typically done with UNets. Compared to the conventional UNet classification approach, the distance-map regression approach converges faster in clinically relevant parameters. It also produces morphometric parameters that agree with the manually-segmented ground-truth data, namely the average cell density difference of -41.9 cells/mm2 (95% confidence interval (CI) [-306.2, 222.5]) and the average difference of mean cell area of 14.8 um2 (95% CI [-41.9, 71.5]). These results suggest a promising alternative for CE assessment.

Invited talk by M. Rey at the University of Granada, 01 December, 2022

Drawing of a coffee mug using only coffee. (Image by M. Rey.)
Marcel Rey got invited to present his recent work on stimuli-responsive emulsions and the coffee ring effect at in the group seminar of the Laboratory of Surface and Interface at the University of Granada.

In the seminar, Marcel Rey talked about his recent advances on understanding the behaviour of stimuli-responsive emulsions and afterwards introduced a simple yet versatile strategy to overcome the coffee ring effect and obtain homogeneous drying of particle dispersions.

Temperature-responsive emulsions combine the long-term stability with controlled on-demand release of the encapsulated liquid. The destabilization has previously been attributed to microgel shrinkage, leading to a lower surface coverage which induces coalescence. We demonstrated that breaking mechanism is fundamentally different than previously thought. Breaking only occurs if the stabilizing soft microgel particles assume a characteristic double-corona microstructure, which serve as weak link enabling stimuli-responsive emulsion behavior. Conversely, emulsions stabilized by regular single-corona microgels remain remarkably insensitive to temperature.

After spilling coffee, a tell-tale circular stain is left by the drying droplet. This universal phenomenon, known as the “coffee ring effect”, is observed independent of the suspended material. We recently developed a simple yet versatile strategy to achieve homogeneous drying of dispersed particles. Modifying the particle surface with surface-active polymers provides enhanced steric stabilization and facilitates adsorption to the liquid/air interface which, after drying, leads to uniform particle deposition. This method is independent of particle size and shape and applicable to a variety of commercial pigment particles promising applications in daily life.