Groups of migrating cells are usually guided by external cues, such as gradients of chemoattractant (chemotaxis), substrate stiffness (durotaxis), or electrostatic potential (electrotaxis). Here, I will show that cell groups can also be guided by internal cues, i.e., by gradients of their own properties. We found that, when moving from soft to stiff substrate, clusters of neural crest cells exhibit an opposite gradient in their own tissue stiffness, with soft cells at the front and stiff cells at the back. We predict that this internal stiffness gradient is enough to guide collective cell migration — a phenomenon that we call internal durotaxis. Moreover, these cell clusters are taller at the back than at the front. We explain this asymmetric height profile by modeling the cell cluster as an active liquid droplet driven by the motile cells at its base. We speculate that the emergence of internal guidance cues could provide robustness to the migration of cell clusters in noisy environments.

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Microbes perform essential ecosystem functions, from carbon fixation in the ocean to pathogen resistance in the human gut, in communities with thousands of coexisting species. This remarkable diversity contrasts with classical ecological theory, which predicts that the number of coexisting species should be limited by the number of available resources. At the same time, microorganisms evolve rapidly, and evolutionary theory would suggest that the fittest genotypes should dominate; nevertheless, microbial populations maintain extensive genetic variation. Explaining microbial diversity therefore requires a framework that integrates both ecological and evolutionary processes.

Frequency-dependent selection provides such a mechanism. Species and genotypes often modify their own environment—by depleting resources, producing toxic by-products, detoxifying the environment or generating metabolites that benefit competitors—causing their fitness to decline as they become common and promoting coexistence at low frequencies.

I will present theoretical and experimental results showing how metabolic trade-offs enable stable coexistence in simple environments and how growth–resistance trade-offs shape the evolution of antibiotic resistance. Understanding these mechanisms offers new opportunities to predict and steer the evolution of microbial communities.

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Gastruloids are embryonic cell aggregates that elongate themselves from spheres into rods. They thus model formation of the primary body axis of vertebrate embryos. This requires breaking symmetry to organise cell rearrangements known as convergent extension (CE). Gastruloid elongation demonstrates that CE can be entirely self-organising, and that self-organising mechanisms are likely embedded in this and the many other instances of CE in development. Classically, self-organisation in biology involves Turing Reaction-Diffusion (RD) patterning by diffusible morphogens. We have explored the possibility that a completely different, non-RD-based, self-organisation principle for symmetry-breaking by CE could exist, namely polarity-propagating mechanical feedback, which provides nematic structure and for which there is experimental evidence at the single-cell level. Using in silico modelling, we show that this simple rule for mechanical interactions is sufficient to organise convergent extension in 2D whereas, in 3D, we find that a combination of nematic and dipolar organisation, representing Planar Cell Polarity pathway function, gives robust elongation. Thus, although Turing himself chose to consider only chemical morphogenesis, short-range mechanical self-organisation seems likely to be involved in CE in vivo.

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Fracture and buckling are typically understood as signs of mechanical failure in materials. In this talk, I will show how, in living systems, these instabilities are instead employed as productive mechanisms. I will show how mechanical fracture of the cardiac extracellular matrix guides morphogenesis in the developing heart, and how fracture more generally emerges as a unifying principle across morphogenetic processes. I will then turn to malaria, showing how parasite-mediated cytoadhesion to the endothelium induces mechanical buckling of infected red blood cells, leading to interdigitated contacts that strengthen anchoring and promote sequestration. I will show how similar interdigitated architectures arise across biological systems to increase structural stability, including the heart myocardium itself.

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Continuous neural activity is not wasteful background noise—it is the physical basis of being alive, conscious, and ready to act. Some of the observe brain activity’s function can be isolated with controlled experiments and carefully designed tasks, but it is well-known that the bulk of this activity is not straightforwardly related to these tasks. This activity, at the whole-brain scale, is slow and has substantial spatiotemporal structure. On this grounds, how much this activity actually reflects cognition is debated and unclear. In this talk, I will propose an interpretative axis ranging from physical epiphenomenon to mental-content-realising. I will discuss, in light of what we know, where in this axis whole-brain activity may possibly lie, as we measure it with fMRI and magnetoencephalography (MEG), and as we characterise it with existing analytical methods. Finally, I will propose that when using analysis methods that preserve the phase of the signal, MEG patterns are likely to reflect context and variables that constrain the computations behind cognition; although we cannot conclude these to have any representational role. 

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Malaria is still one of the most severe infectious diseases and the underlying mechanisms are far from being understood. The lifecycle of the malaria parasite is complex and strongly shaped by the requirement to frequently switch between different physical environments. We first discuss the case of sporozoites, which are the slender forms injected by female mosquitoes into the skin of the vertebrate hosts, and show that its motion patterns are strongly determined by right-handed chirality. We then discuss the blood stage, when the malaria parasite induces a system of adhesive knobs on the surface of infected red blood cells, in order to increase residency time in the vasculature and to avoid clearance by the spleen. In both cases, we combine theoretical models and experimental data to uncover the underlying mechanisms.

This exceptionnal seminar is organised in partnership with Science By the Beach (EMBL), at the PRBB. 

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Embryonic development is a spectacular display of self-organization of multi-cellular systems, combining transformations of tissue mechanics and patterns of gene expression. These processes are driven by the ability of cells to communicate through mechanical and chemical signaling, allowing coordination of both collective movement and patterning of cellular states. 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, we lack principled frameworks to understand how tissues obtain information to generate reproducible patterns. I will discuss how combining dynamical systems models with information theory provides a mathematical language to analyze biological self-organization across diverse systems. Our approach can be used to define and measure the information content of observed patterns, to functionally assess the importance of various patterning mechanisms, and to predict optimal operating regimes of self-organizing systems. I will demonstrate how our framework reveals mechanisms of self-organization of in vitro stem cell systems in direct connection to experimental data, including intestinal organoids and gastruloids. This framework provides an avenue towards unifying the zoo of chemical and mechanical signaling processes that orchestrate embryonic development.

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During organ development cells undergo significant morphological and positional changes. Yet, by the end of organogenesis, internal organ structure is typically robustly defined, with cells tightly packed. It remains an open question as to how the three-dimensional (3D) internal structure of an organ emerges reliably, particularly when there are multiple cell types interacting and dynamic boundary constraints. Here, I discuss recent work from my lab utilising quantitative live imaging and 3D morphological measures of the developing zebrafish myotome to unravel how early muscle organisation emerges. Contrary to the textbook view of muscle fibres as cylindrical, myocytes undergo an ordered chiral twist, the direction and magnitude of which depends on their position within the myotome. Further, cells skew and rearrange, seemingly to facilitate close packing of neighbouring muscle fibres. Cell movement undergoes a rapid decline in speed once the cells span the myotome segment.  We find that cell packing is altered in mutants that disrupt cell fate or cell fusion, even though the final muscle segments remain largely confluent. Biophysical perturbation reveals that the cells are mechanically plastic, able to adjust to changes in the local cellular environment and boundary constraints. Taking these results together, we propose that the early myotome undergoes a structural transition, from a fluid-like state into a frozen state, resembling glass-like behaviour. Cellular plasticity in response to varying boundary constraints may be a general mechanism for ensuring robust organ morphogenesis in dense 3D tissues.

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Protein language models trained on multiple sequence alignments of homologous proteins successfully capture coevolution between amino acids in structural contact: this is one of the ingredients of the success of AlphaFold. We have used such models, especially MSA Transformer, to generate new protein sequences from given protein families, and to predict which proteins interact among the members of two protein families. 

Despite their successes, a drawback of models based on multiple sequence alignments is that sequence alignment can be imperfect. Thus, we developed ProtMamba, a homology-aware but alignment-free protein language model, which is able to generate new protein sequences from given protein families. 

Beyond the amino-acid scale, coevolution also exists between genes that in a genome. To capture it, we trained ProteomeLM on complete proteomes spanning the tree of life. This model allows quick and precise scans of whole protein interaction networks.

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