14th Nordic Workshop on Statistical Physics: Biological, Complex and Non-Equilibrium Systems

5 Mar 2025, 09:00 → 7 Mar 2025, 19:00 Europe/Stockholm

Albano Building 3

Ralf Eichhorn (Stockholm University, Nordita), Supriya Krishnamurthy (Stockholm University)

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Venue

Nordita, Stockholm, Sweden

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Scope

This workshop series provides a “forum” where scientists in the Nordic countries working in the area of Statistical Physics can meet regularly. The meeting brings together experts interested in the broad spectrum of timely problems in Statistical Physics, ranging from fundamental aspects in the theory of non-equilibrium processes to modern applications in biophysics. Topics covered include diffusion problems, non-equilibrium transport, work relations and fluctuation theorems, large deviations, resetting, inference methods, soft condensed matter (colloids, liquid crystals etc.), self-assembly, population dynamics, physics of DNA and bio-molecules, single-molecule kinetics, dynamics and structure of networks, neuronal networks, quantum thermodynamics and many more.

The workshop will start on Wednesday morning at around 9.00 with registration and coffee/cake. It will end on Friday (early) afternoon. There will be a conference dinner on Thursday evening.

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Invited speakers (*to be confirmed)

Tobias Ambjörnsson (Lund University)	Ludvig Lizana (Umeå University)
Luiza Angheluta-Bauer (University of Oslo)	Michael A Lomholt (University of Southern Denmark)
Amin Doostmohammadi (Niels Bohr Institute)	Stefano Sarao Manelli (Chalmers University)
Alessia Ferraro (Nordita)	Sreekanth Manikandan (Gothenburg University)
Maryam Ghasemitarei (Aalto University)	Bernhard Mehlig (Gothenburg University)
Henning Kirchberg (Chalmers University)	Namiko Mitarai (Niels Bohr Institute)
Soon Hoe Lim (Nordita)

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Special Guest

Alberto Imparato (Trieste)

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Registration

If you want to participate in the workshop, please fill in the registration form.

Registration deadline: Feb 23, 2025

There is no registration fee.

There is a (very) limited number of travel grants available for PhD students and young Postdoc fellows from the Nordic countries. If you are interested in such a grant, please contact the organizers via email, and provide a rough estimate of your expenses (travel and/or accommodation).

For administrative reasons you have to register before February 16, if you want to apply for such a travel grant.

Depending on interest and time constraints we *might* set up a session dedicated to short talks by junior researchers (PhD students, young Postdocs). During registration you have the possibility to indicate if you are interested in contributing such a short talk (likely around 5-10 min).

 

Please be aware that scammers sometimes approach participants claiming to be able to provide accommodation and asking for credit card details. Do not give this information to them! If you are in any doubt about the legitimacy of an approach, please get in contact with the organizers.

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Sponsored by:

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Wednesday 5 March

1 Registration & Breakfast

2 Opening

3 Bayesian model checking

In the standard approach for model checking a model is falsified if the observed data is too extreme given the fitted model, i.e., the p-value is too small. This answers a binary question about whether the model should be rejected or not. However, at least for complex systems, we already know beforehand that the model is a simplified approximation, and thus actually not correct. An alternative approach is Bayesian model checking, where alternative models are generated from the model to be tested, and then Bayesian model selection is performed between these models and the original, to see if one of the alternatives are better at explaining the data. In this talk I will present a method for performing model checking in this way without the need for Monte Carlo simulations, which means the check can be performed quickly. This also allows for iterating the approach, and one can thereby quantify, using Kullback-Leibler divergence, how much the approach is able to improve on the original model.

Speaker: Michael A Lomholt

4 Following marginal stability manifolds in quasilinear dynamical reductions of multiscale flows in two space dimensions

We derive a two-dimensional (2D) extension of a recently developed formalism for slow–fast quasilinear (QL) systems subject to fast instabilities. The emergent dynamics of these systems is characterized by a slow evolution of (suitably defined) mean fields coupled to marginally stable, fast fluctuation fields. By exploiting this scale separation, an efficient hybrid fast-eigenvalue/slow-initial-value solution algorithm can be developed in which the amplitude of the fast fluctuations is slaved to the slowly evolving mean fields to ensure marginal stability–and temporal scale separation–is maintained. For 2D systems, the fluctuation eigenfunctions are labeled by their Fourier wavenumbers characterizing spatial variability in that extended spatial direction, and the marginal mode(s) must coincide with the fastest-growing mode(s) over all admissible Fourier wavenumbers.
Here, we derive an ordinary differential equation governing the slow evolution of the wavenumber of the fastest-growing fluctuation mode that simultaneously must be slaved to the mean dynamics to ensure the mode has zero growth rate. We illustrate the procedure in the context of a 2D model partial differential equation that shares certain attributes with the equations governing strongly stratified shear flows and other strongly constrained forms of geophysical turbulence in extreme parameter regimes. The slaved evolution follows one or more marginal stability manifolds, which constitute select state-space structures that are not invariant under the full flow dynamics yet capture quasi-coherent structures in physical space in a manner analogous to invariant solutions identified in, e.g., transitionally-turbulent shear flows. Accordingly, we propose that marginal stability manifolds are central organizing structures in a dynamical systems description of certain classes of multiscale flows in which scale separation justifies a QL approximation of the dynamics

Speaker: Alessia Ferraro

12:00 Lunch

5 Optimizing Feedback-Controlled Nano Engines: The Impact of Time- Dependent Information Acquisition

Nanoscale devices that convert energy into useful work are becoming increasingly common. A critical challenge is controlling energy transduction at the nanoscale. In this context, quantum measurement and the associated acquisition of information can be leveraged to guide and enhance work output through feedback control. We explore a quantum information engine (QIE) as a prototype energy-transducing nano device controlled by measurement. This engine utilizes the information exchange between a working medium, modeled as a two-level system, and a meter, modeled as a quantum harmonic oscillator. However, this information transfer is not instantaneous; it depends on the measurement time, which is the time required to establish a correlation between the quantum system and the meter. This measurement time sets a lower bound on the cycle time of the QIE, making information acquisition a crucial resource for the process.
We examine the energetic cost of quantum measurement and the associated information acquisition during finite-time operations. Heat and work flows are analyzed as functions of the system and meter temperatures to demonstrate that the QIE can operate in different modes: as a heat engine, a heat valve, a refrigerator, and a 'true' information engine, which extracts work and cools a colder bath. We show that the QIE's performance in terms of power output is limited for very short measurement times, in the Zeno limit. To increase power output, it is necessary to extend the measurement time; however, this results in a higher cost of measurement. We carefully analyze the relationship between work output and cost in different operating regimes of the QIE to determine optimal conditions for maximizing net total work output and achieving high engine performance.

Speaker: Henning Kirchberg

6 Localizing entropy production in non-equilibrium processes

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.

Speaker: Sreekanth Manikandan

15:30 Coffee Break

7 Modeling Enhancer-Promoter Gene Regulation as a Stochastic Resetting Problem

In bacteria, gene regulation often involves transcription factor proteins that bind to short DNA regions (promoters) near the gene start to control expression. However, in higher organisms, gene activation is typically more complex and frequently depends on enhancer-promoter interactions. Enhancers and promoters are distant DNA elements that come together in 3D space to tune transcription. In this talk, I will show how we model the stochastic dynamics of these interactions using resetting theory. Our model allowed us to calculate experimentally accessible observables such as the mean-first hitting time. In addition, our theory aligns with empirical data from Drosophila and offers new insights into the physical principles underlying long-distance gene regulation.

Speaker: Ludvig Lizana

8 Evidence of robust, universal conformal invariance in living biological matter

Collective cellular movement plays a crucial role in many processes fundamental to health, including development, reproduction, infection, wound healing, and cancer. The emergent dynamics that arise in these systems are typically thought to depend on how cells interact with one another and the mechanisms used to drive motility, both of which exhibit remarkable diversity across different biological systems. I will discuss recent findings, where we report experimental evidence of a universal feature in the patterns of flow that spontaneously emerges in groups of collectively moving cells. Specifically, I demonstrate that the flows generated by collectively moving dog kidney cells, human breast cancer cells, and by two different strains of pathogenic bacteria, all exhibit conformal invariance. Remarkably, not only do the results show that all of these very different systems display robust conformal invariance, but we also uncover that the precise form of the invariance in all four systems is described by the Schramm-Loewner Evolution (SLE), which allows us to reveal the universality class. A continuum model of active matter can recapitulate both the observed conformal invariance and SLE form found in experiments. The presence of universal conformal invariance reveals that the macroscopic features of living biological matter exhibit universal translational, rotational, and scale symmetries that are independent of the microscopic properties of its constituents. The results show that the patterns of flows generated by diverse cellular systems are highly conserved and that biological systems can unexpectedly be used to experimentally test predictions from the theories for conformally invariant structures.

Speaker: Amin Doostmohammadi

Thursday 6 March

9 Tissue Dynamics due to Topological Changes and Fluidisation

Collective cell rearrangements and migration are important mechanical processes in epithelial tissue development and regeneration. In this talk, I will present recent theoretical insights into how dynamical patterns emerge at the tissue scale from localized cell rearrangements and topological defects. Using a minimal polarized cell model, we explore how planar cell polarity (PCP) induces active stresses and spontaneous localised fluidisation. Specifically, a vortex in the PCP ordering generates inward cell migration leading to out-of-surface tissue deformations. Using a multi-phase field model, we show that T1 transitions, as cell neighbour exchanges driven by cell self-propulsion, can induce directional cell migration relative to other cells. T1 transitions are transient sources of vortical flow, controlling the rate of cell mixing through relative dispersion, and promote directional migration.

Speaker: Luiza Angheluta-Bauer

10 Phage-bacteria coexistence: Effect of aggregate formation and spatial structure

Phages and bacteria coexist under various conditions, ranging from liquid cultures to oceans, soil, and the human gut. However, our models are typically limited to well-mixed liquid cultures governed by mass-action kinetics, and the effect of physical structure, such as aggregate formation and spatial structure, is not fully understood. Here, we first discuss modifying the Lotka-Volterra dynamics by including the formation of microcolonies [1]. The model predicts that the colony size distribution is power-low distributed with steeper exponents for the more substantial external influx. In the realistic case where the phage attack rate to individual colonies is proportional to their radius, we obtain self-organization to a steady state where the maximal colony size is smaller for more vigorous external driving. We then introduce the case where the phage T4 attacks the bacteria Escherichia Coli in a spatially structured environment. First, we discuss an experimental analysis of the single spherical colony attacked by phages [2], which indicated that the colony structure marginally protects the bacteria hosts, but the T4 phage is able to penetrate very deep into the bacterial colony. If time allows, we also discuss the effect of lysis inhibition, the phage T4's ability to delay host lysis upon secondary infection, in the spatial spreading of the T4 phage [3].

Speaker: Namiko Mitarai

11 Effect of cysteine oxidation on protein structures and functions: insights from atomistic simulations

Molecular dynamics (MD) simulation is a powerful tool for understanding the structural and functional consequences of oxidative modifications in proteins, particularly cysteine oxidation. By providing atomic-level insights into how oxidative stress alters protein stability, dynamics, and interactions, MD simulations help elucidate the molecular mechanisms underlying disease progression. This approach is especially valuable in studying the effects of cysteine oxidation on proteins involved in cancer cell growth, viral infection, and membrane stability, offering potential implications for targeted therapeutic strategies.
One of the important amino acid residues that can play an essential role in the stability of protein structures is cysteine which can easily make disulfide bridges with other thiol groups and H-bonds with other amino acids. Moreover, cysteine is highly reactive and can be easily oxidized to cysteic acid by reactive oxygen species (ROS) generated inside the body or applied by cold atmospheric plasma. In general, intracellular ROS, such as H2O2, can oxidize 5% of Cys residues of proteins to cysteic or sulfunic acid [1], and this effect is enhanced under oxidative stress [2]. Plasma oxidation of amino acids in proteins, especially oxidation of the thiol groups of Cys residues, can disturb the normal function of some antioxidant enzymes [3]. Indeed, the ROS-induced protein modifications can alter the protein structure and disrupt their function [4]. In this presentation, we will describe the effect of Cys oxidation to cysteic acid on protein functions that are important in cancer cells growth and viral infection, using molecular dynamics simulations. Additionally, the importance of Cys residues in the stability of membrane proteins will be discussed.

[1] B.J. Williams, C.K. Barlow, K.L. Kmiec, W.K. Russell, D.H. Russell, Negative ion fragmentation of cysteic acid containing peptides: cysteic acid as a fixed negative charge, Journal of The American Society for Mass Spectrometry 22(9) (2011) 1622-1630.
[2] E. Takai, T. Kitamura, J. Kuwabara, S. Ikawa, S. Yoshizawa, K. Shiraki, H. Kawasaki, R. Arakawa, K. Kitano, Chemical modification of amino acids by atmospheric-pressure cold plasma in aqueous solution, Journal of Physics D: Applied Physics 47(28) (2014) 285403.
[3] D. Yan, A. Talbot, N. Nourmohammadi, X. Cheng, J. Canady, J. Sherman, M. Keidar, Principles of using cold atmospheric plasma stimulated media for cancer treatment, Scientific reports 5(1) (2015) 1-17.
[4] E. Cabiscol, J. Ros, Oxidative damage to proteins: structural modifications and consequences in cell function, Redox proteomics: from protein modification to cellular dysfunction and disease (2006) 399-471.

Speaker: Maryam Ghasemitarei

12:15 Lunch

12 Information engine fueled by first-passage times

I will consider the thermodynamic properties of an information engine that uses feedback control to extract work from a manipulated stochastic system. I will discuss the fluctuation theorems that involve the information associated with the feedback-controlled stochastic trajectories. Such an information turns out to be based on the first-passage-time distribution. I will then discuss the experimental verification of the above results with a setup consisting of a cantilever submitted to an electrostatic feedback force. I will finally show that the fluctuation theorems extend beyond the specific application to such an experiment.

Aubin Archambault, Caroline Crauste-Thibierge, Alberto Imparato, Christopher Jarzynski, Sergio Ciliberto, Ludovic Bellon, arXiv:2407.17414

Speaker: Alberto Imparato

15:00 Coffee Break

13 Ultrafast Thermodynamics in Non-equilibrium Magnets

We present an ultrafast thermodynamics framework to model heat generation and entropy production in laser-driven ferromagnetic systems. By establishing a connection between the magnetic field strength of the laser pulse and magnetization dynamics we model time-dependent entropy production rates and deduce the associated heat dissipation in epitaxial and polycrystalline FeNi and CoFeB thin films. Our theoretical predictions are validated by comparison to experimental magnetization dynamics data, shedding light on thermodynamic processes on picosecond timescales. Crucially, we incorporate recently observed inertial spin dynamics, to describe their impact on heat generation in pump-probe experiments. As such, this formalism provides novel insights into controlling heat production in magnetic systems, and contributes to advancing the understanding of non-equilibrium thermodynamics in magnetic systems, with implications for future experimental protocols in spintronics and nanotechnology.

Speaker: Finja Tietjen (Chalmers)

14 Combining thermodynamic and kinetic uncertainty relations for quantum transport

Speaker: Didrik Palmqvist (Chalmers)

15 Late stage of phase transformation - balance equation or evolution approach?

Speaker: Victor Kurasov (SU)

16 Force-dependent binding as a mechanism for directional stepping: a model of an artificial molecular motor

Speaker: Mikkel Peter Andersen (Lund University)

16:30 Coffee Break

17 Finite length and boundary effects in the mode selection of a floating elastic sheet

Let me free, I crumple
Give me a support, I wrinkle
Pinned, I am trigonometric
Clamped, I am eclectic
My asymptotic analysis will make you tumble

Speaker: Anthony Bonfils (University of Limerick)

18 Bayesian model selection

Speaker: Søren Vad Iversen (Phylife)

19 Discrete modelling of interactions between phages and swimming bacteria

Speaker: Laura Bergamaschi (Niels Bohr Institute)

20 Self-organized coexistence of phage and a population of host colonies

Speaker: Anjali Yadav (Niels Bohr Institute)

19:00 Dinner

Friday 7 March

21 Droplet evaporation at the cloud edge

Evaporation of cloud droplets accelerates when turbulence mixes dry air into the cloud, affecting droplet-size distributions in atmospheric clouds. The challenge is to model local correlations between droplet numbers, sizes, and supersaturation. These correlations are important because they determine supersaturation fluctuations along droplet paths, which in turn affect how droplets grow or shrink. We derived a statistical model that accounts for these correlations. Its predictions are in quantitative agreement with results of direct numerical simulations, and it explains the key mechanisms at play.

Speaker: Bernhard Mehlig

22 Achieving sub-temporal resolution in the analysis of two-state singlemolecule trajectories

While spatial resolution in flourescence microscopy and related fields during the last two decades reached the nanometer scale, the time resolution has remained essentially unchanged and is set by the camera system's imaging time. Yet adequate time resolution is crucial for accurate information acquisition about, for instance, dynamical processes in cells. In a reaction-difffusion process in a cell a given molecule will undergo an alterating process: unbound (free molecule) to bound (molecule bound into a complex) and back. The two states (bound and unbound) are characterized by different diffusion constants, and the transitions between the two states are characterized by two rates (bound-to-unbound) and (bound-to-unbound). The analysis of experimentally acquired trajctories for such two-state trajectories is often done using a discrete-time hidden Markov model, thus implicitly assuming that the observations generated by the hidden states are near-perfectly resolved, which is seldom the case in practise. The matter is brought to its head for rapid kinetics, where sub-time events that happen during imaging time are commonplace. To deal with type of rapid switching dynamics, we introduce a Bayesian parameter estimation procedure combined with a novel algorithm that efficiently calculates the exact probability of observed trajectories, including the many "unseen" switching events during imaging. Our method is based on an analytic derivation of generalised transition probabilities - transition-accretion probabilities - that probabilistically capture unseen switching behaviour during data acquisition. We do in-silico parameter inference where we compare this sub-time hidden Markov model to the standard variant (applicable to slow kinetics) as well as to an approximative method recently proposed (applicable to rapid kinetics). We find that our method works well irrespective of the temporal resolution of the setup, and that it can be extended to other types of two-state single-molecule experiments such as single-molecule FRET.

Speaker: Tobias Ambjörnsson

11:00 Coffee Break

23 Statistical physics of bias generation and amplification

As machine learning systems become more pervasive in our daily lives, the emergence of biases--often disproportionately affecting vulnerable populations--has raised significant concerns. In response, governments have introduced regulatory measures, such as the recent Artificial Intelligence Act. However, addressing bias through regulation remains challenging, as bias generation within machine learning systems is complex and multifaceted, and still poorly understood.
In this presentation, I will offer a novel, theory grounded approach to investigate this issue. Using tools from statistical physics, I will introduce a simplified model that isolates bias arising from the underlying data structure. By analysing both the asymptotic and dynamic behaviour of classifiers, I will demonstrate how bias can emerge and propagate through the system. While the introduction of bias is often remarkably easy, the statistical physics framework reveals unexpected pathways and mechanisms through which bias manifests. This approach sheds new light on some of the root causes of bias, eventually leading to a more rigorous basis for understanding and mitigating its impact.

Speaker: Stefano Manelli

24 From Schrödinger Bridge to Probabilistic Forecasting of Spatio-Temporal Dynamics

Flow matching has recently emerged as a powerful paradigm for generative modeling and has been extended to probabilistic forecasting of spatio-temporal dynamics. However, the impact of the specific choice of probability path model on forecasting performance remains under-explored. Motivated by dynamical optimal transport and the Schrödinger bridge perspective, we present a novel probability path model, together with the theoretical framework and efficient algorithms, for applying flow matching in latent space to improve forecasting performance. Our empirical results across various dynamical system benchmarks show that our model achieves faster convergence during training and improved predictive performance compared to existing probability path models. Importantly, our approach is efficient during inference, requiring only a few sampling steps. This makes our proposed model practical for real-world applications and opens new avenues for probabilistic forecasting.

Speaker: Soon Hoe Lim

13:00 Light Lunch

25 Free discussion/ Closing