The study of dynamical systems has long focused on the characterization of their asymptotic dynamics such as fixed points, limit cycles and other types of attractors and how these invariant sets change their properties as systems parameters are varied. More recently, however, the importance of transient dynamics, especially of long transients and sequential transitions between them, has been increasingly recognized in various fields including ecology, neuroscience and cell biology. Among several possible origins of long transients, ghost attractors have received particular attention due to interesting dynamical properties in non-autonomous systems, new theoretical developments, and an increasing number of systems that empirically show dynamics consistent with ghost attractors. In this talk, I will present a new formal definition for ghost attractors of fixed points which allows us to not only develop an algorithm for finding ghosts (available as open-source python package: PyGhostID) but also to identify novel types of ghosts with unique properties, leading to surprising phenomena such as bifurcations involving ghosts. I will further present how our framework can be used to gain new insights into the transient dynamics of a wide range of systems, ranging from information processing and decision making in neuronal circuits, tipping of coupled climate elements and potentially also tissue regeneration following a heart attack.

Volume-exclusion interactions play a crucial role in the collective behaviour and self-organisation of anisotropic particle systems, from liquid crystals to biological populations such as dense suspensions of myxobacteria and migrating cells. Even when particles have negligible volume, their mutual exclusion can generate a non-trivial effective volume in configuration space, strongly influencing macroscopic dynamics and the emergence of orientational order. In this talk, we first consider hard Brownian needles in two dimensions and use matched asymptotic expansions to derive a nonlinear kinetic equation for their density in position and orientation. We explore the resulting excluded-volume effects, including the emergence of an isotropic-to-nematic transition at increasing densities. We then extend the framework to finite-width rod-like particles, investigating how particle geometry and aspect ratio modify the effective interactions, and finally discuss active elongated particles, where steric interactions combine with self-propulsion to produce richer collective dynamics.

Cells make decisions across developmental biology, immunology, and synthetic biology. Yet, while the timing of these decisions can be as important as their outcome, mathematical models often focus primarily on stable cell states. In this talk, I will present a mathematical framework based on orbital equivalence to identify molecular mechanisms that can control the speed of cellular decisions while preserving their underlying dynamics. I will first address developmental tempo, where embryos from different species execute remarkably similar genetic programmes at different speeds. I will then turn to gene regulatory networks, showing how critical slowing down and transient dynamics can regulate decision times, and how analysing different network topologies can reveal design principles for controlling dynamical speed in synthetic and natural systems.

We develop mechanistic models at the cell and tissue level based on PhysiCell and PhysiBoSS, combining probabilistic Boolean networks for intracellular signalling with agent-based rules for cell growth, migration and interactions. These simulations have been applied to predict drug synergies, modes of metastasis, and responses to metabolic conditions. However, building patient-specific digital twins remains complex and cumbersome. Mechanistic models offer interpretability and causal reasoning but struggle with parameter identifiability and sparse patient-specific data. 

To address these problems we are working in embedding mechanistic agent-based simulations within an AI-assisted architecture. The system combines our multiscale simulator with a data integrator that learns patient-specific network structures and model parameters through a bidirectional co-design loop, enabling AI to inform mechanistic parameterisation while simulations generate training constraints for AI models. The initial developments are explored in the context ofthe simulation of perturbations in organoids with drugs and drug combinations and cancer progression and treatment response. 

In my view, this approach illustrates a paradigm for AI for Science where machine learning and mechanistic modelling work as integrated components of a unified framework for understanding and predicting complex biological systems.

Nature has survived billions of years of upheaval—yet coral reefs are collapsing, the Amazon is losing resilience, and lakes can flip into degraded states almost overnight. How can systems this battle-tested also be this brittle? In this talk, I'll argue that the answer lies in a central paradox of complex systems: the "Robust-Yet-Fragile" principle. The very features that confer resilience can also accumulate hidden vulnerabilities to novel or extreme shocks.

I'll walk through a series of empirical examples to show this duality in action, then formalize it through the lens of control theory's "conservation of fragility". The implication is unsettling: managing nature for resilience may be exactly what makes it fragile, and averting collapse requires learning to spot vulnerabilities hiding in plain sight.

Research into the origin of life and its possible presence in the universe has never been more intense. To detect it, specialists from all fields (biologists, geneticists, chemists, physicists and astrobiologists) attempt to define and study its properties: chemical composition, metabolism, reproduction, evolution, self-organisation and complexity. However, whilst small rovers scour the Martian surface in search of it, the most obvious aspect of life on Earth has, paradoxically, never been formally defined: its geometry. Indeed, there is currently no comprehensive inventory of the morphological diversity and complexity of organisms that would enable us to define its fundamental properties. Consequently, essential questions as ‘what kinds of forms can living beings produce?’, ‘how can we recognize life on another planet?’ or ‘are there general limits or rules governing the morphological evolution of life?’ currently have no satisfactory answers at the scale of the biosphere. The reason for this is very simple: there is no theoretical framework that allows us to rationally compare and quantify geometries and structures as complex and diverse as those of the living beings inhabiting our planet.

In this presentation, I will outline a new method based on the structural and fractal properties of shapes, designed to quantify and compare the morphology of all living beings on Earth (e.g., bacteria, ants, whales, sequoias, office colleagues), regardless of their level of complexity and size. By applying this method to all known phyla, we will explore how body size influences the form of life on a large scale and discuss the role of chance and necessity, as well as the constraints and general trends in the evolution of life on Earth (and perhaps in the Universe…).

Coordinated behavior is a hallmark of development. If a cell expresses genes for one cell type, it should not also express the genes for another type. Signals that control an animal's growth should be uniform throughout the body, to prevent one part of the body growing faster than another. I will present two theoretical projects, driven by experimental work on the nematode worm C. elegans in our group, that point to depletion of a common pool of resources as a coordinating force. I will first discuss how depletion of gene expression resources can prevent erroneous induction of neuron fate in networks underlying C. elegans neurodevelopment. Next, I will discuss how stochastic pulses of insulin signaling can be synchronized by depletion of a common pool of insulins to ensure a uniform arrest of body growth in C. elegans under external stress.

Cellular populations need to respond collectively to their environmental conditions. A wide range of such collective strategies, evolved over hundreds of millions of years, ensure that multicellular organisms develop correctly to become functional life forms. Yet simpler life forms such as bacteria, which have lived on earth for billions of years, exhibit similar non-trivial responses. In this talk I will discuss some of those strategies.