Giulia Celora:
Title: Migration of living droplets: a novel paradigm for chemotaxis of multicellular communities
Abstract: Collective cell migration is ubiquitous amongst multicellular communities and contributes to many phenomena, e.g., morphogenesis and cancer metastasis. Nonetheless, it is still poorly understood how cells coordinate to control the emergent collective motion of cell groups (or swarms). Recent experimental data suggests that physical interactions between cells within the swarms can result in emergent fluid-like properties. In this work, we propose a continuum, coarse-grained, active fluid model to study how physical interactions affect the complex spatiotemporal dynamics of collective migration of cell groups in response to self-generated chemical gradients. Our results reveal a new mode of pattern formation via self-organised shedding of migrating groups. A travelling wave analysis of the model elucidates the dynamics leading to the group shedding and how this arises from the interplay of physical interactions, cell proliferation and chemotaxis. Overall, our work offers a new perspective to the study of chemotaxis of multicellular communities revealing the role of physical interactions in mediating their collective dynamics.
Edgar Roldan:
Title: Thermodynamic probes of life at the microscale
Abstract: Living systems can maintain a high degree of order with low entropy while performing complex tasks that are essential for their survival. Such ordered complexity is, due to the second law of thermodynamics, necessarily accompanied by production of entropy in living systems’ surroundings that is manifested as heat dissipation. Understanding, characterising, and quantitatively measuring the heat dissipated by living systems, and in particular cells, has become over the last decade a major endeavour of several frontier disciplines such as micro-calorimetry, soft matter, biophysics, and non-equilibrium stochastic thermodynamics [1]. In this talk, I will overview key efforts in characterising the entropy production by living cells developed in the field of stochastic thermodynamics in synergy with biophysical advances. Special attention will be dedicated to active mechanosensory spontaneous oscillations in bullfrog’s ear-hair bundles [2-4], and flickering of red blood cell's membrane [5] which are both becoming key case studies of non-equilibrium aspects in biology. To conclude, I will outline potential applications of stochastic-thermodynamics approaches in the biomedical context by focusing on theory-experiment synergies that may enable to characterise the thermodynamics of tumoral cells’ rapid metabolic activity.
[1] E Roldan, Science 383 (6686), 952 (2024)
[2] E Roldan et al., NJP 23, 083013 (2021)
[3] G Tucci, et al, PRL 129, 030603 (2022)
[4] Y Thipmaungprom, et al, arXiv:2502.14485 (2025)
[5] I Di Terlizzi, et al, Science 383 (6686), 971 (2024)
Anna Erzberger
Title: How geometry shapes living matter across scales
Abstract: The spontaneous generation of patterns and structures is fundamental to the functioning of living systems. Such processes often take place on domains that themselves evolve in time, and they can be guided by or coupled to geometrical features. Despite its significance, the influence of geometry on the self-organization of functional structures remains poorly understood. In this talk, I will present two biophysical examples that illustrate how geometry directs spatial organization across scales. I will discuss how boundary geometry controls a novel topological defect transition that guides lumen nucleation in embryonic development and how shape can act as a form of memory in cell-cell signaling. These findings highlight how identifying theoretical principles of geometry-driven self-organisation advances our understanding towards controlling and engineering complex biological systems.
David Brückner
Title: Self-organization and information flow in multi-cellular systems
Abstract: Multi-cellular systems, such as tissues, organs, and whole embryos, are a spectacular example of self-organizing living matter. These systems combine active mechanics and biochemical signalling to establish complex three-dimensional shapes and patterns of different cell types. To ensure proper biological function, such patterns must be established reproducibly, by controlling and even harnessing intrinsic and extrinsic fluctuations. While the relevant molecular processes are increasingly well understood, a central theoretical challenge is to identify the basic physical principles of multi-cellular self-organization. I will present an information-theoretic framework to mathematically define and interpret the reproducibility and robustness of self-organised patterns. This framework provides a normative approach for optimization of cell signalling and mechanics, which predicts optimal operating regimes of self-organizing systems. I will show how this approach guides the development of physical models of multicellular stem cell assemblies, including artificial organ-like structures. Finally, I will discuss how combining information theory with biophysical models and machine learning will provide a new theoretical avenue for understanding the physics of multicellular systems in future research.
Steffen Rulands
Title: A biophysical perspective on emergence in artificial intelligence
Abstract: Are artificial intelligence systems just complicated ways of doing curve fitting, or can they exhibit new behaviour that goes beyond the bounds of the data used to train them? The question of whether artificial intelligence systems can exhibit emergent behaviour is not only of fundamental interest to our understanding of artificial intelligence but is also crucial for estimating potential security risks. In this talk, I will take a biophysical perspective on this question and investigate the possibility of emergent behaviour in artificial intelligence on two different levels: in deep neural networks and in systems of interacting intelligent agents.
Fabian Fröhlich
Title: Regulation of Intracellular Signalling Across Scales
Abstract: Cell signalling orchestrates key cellular behaviours, growth, differentiation, and survival, and its dysregulation underlies many diseases, including cancer. The EGFR (epidermal growth factor receptor)–ERK (extracellular signal-regulated kinase) pathway exemplifies this: it governs critical cell-fate decisions and is perturbed by mutations in over 30% of cancers. Yet the clinical success of targeted inhibitors remains limited, partly due to pronounced heterogeneity in signalling responses. Previous studies have implicated factors operating at multiple organisational scales, ranging from molecular mechanisms such as receptor abundance to systems-level programmes like endocytosis, as contributors to this heterogeneity. However, it remains unclear how broadly these principles apply across diverse cellular contexts.
Here, we present DeepMechanisticModels (DMMs), a computational framework that integrates mechanistic ordinary differential equation (ODE) models with machine learning to identify drivers of signalling heterogeneity from proteomic and transcriptomic data. We applied DMMs to a large dataset comprising time-resolved mass-cytometry measurements of EGFR–ERK pathway dynamics in over 50 cancerous and non-cancerous breast-cell lines stimulated with epidermal growth factor, in the presence or absence of kinase inhibitors. DMMs achieved predictive performance comparable to state-of-the-art purely data-driven models while retaining mechanistic interpretability, and they rediscovered established biomarkers used for patient stratification. Finally, we highlight candidate molecular mechanisms that may enable cells to toggle between transient and sustained ERK activation and discuss the challenges of disentangling molecular- from systems-level drivers of heterogeneity.
