This weekly seminar aims at gathering researchers from different fields (physics,chemistry, biology, computer science, ...) and from different institutes in central Paris. The objective is to cover a broad spectrum of topics, including experimental, numerical and/or theoretical approaches. To describe life sciences all scales are needed, from single molecules, to cells, tissues, organs, organisms, and populations. The scope of our seminar encompasses embryonic development, genetic regulation, evolution, neuroscience, biomechanics and cell migration, immunology, microbiology, synthetic biology, machine learning and data science, etc. Organising committee: Antonio Carlos Costa, Jonas Ranft and Raphael Jeanneret
23 June 2023, 1:30pm - Benjamin Lindner (HU-Berlin)
In this talk I will present two studies of collective behavior observed in animal groups. In the first one, we studied the spontaneous and intermittent collective displacements observed in small groups of sheep. We performed experiments and noticed that these animals form files while moving as a group, with a clearly-defined leader at the front (i.e.: a strongly hierarchical structure). We refer to this events as Collective Motion Phases (CMPs). A second observation we made was that the leader of a CMP was not always the same individual, but there was a change of leaders, where each individual of the group had the same probability of playing the leading role (i.e.: a "democratic" structure). We investigated the benefits of such seemingly opposite mechanisms (hierarchical vs. democratic) and found scenarios where individuals of the group might benefit from them, like the optimal guidance while navigating a complex landscape or the optimal visit of multiple targets in space. In the second study I will present experimental data acquired in Teapa, Mexico, where the sulphur mollies (Poecilia sulphuraria) evolved to live in sulphidic springs with high concentration of hydrogen sulphide. In this system, we studied the highly conspicuous, repetitive, and rhythmic collective dive cascades produced by many thousands of these fish that resemble neuronal avalanches observed in brain tissue at criticality. Together with the results of an agent-based model of the system, we explored in how far these fish shoals indeed operate at a critical point between a state of high individual diving activity and low overall diving activity. We explored the adaptive significance (or 'functionality') of acting at this critical point for the fish by using machine learning algorithms and showed that the best-fitting model, which indeed is located at a critical point, allows information about external perturbations – such as predator attacks – to propagate most effectively through the shoal. Our results suggest that criticality may represent a fundamental principle of distributed information processing in biological systems including large animal collectives.
Pseudomonas aeruginosa makes and secretes massive amounts of rhamnolipid surfactants that enable swarming motility over agar surfaces. But how these rhamnolipids interact with agar to assist swarming remains unclear. Here, I use a combination of optical techniques across scales and genetically engineered strains to demonstrate that rhamnolipids can induce agar gel swelling over distances >10,000× the body size of an individual cell. The swelling front is on the micrometric scale and is easily visible using shadowgraphy. Rhamnolipid transport is not restricted to the surface of the gel but occurs through the whole thickness of the plate and, consequently, the spreading dynamics depend on the local thickness. Surprisingly, rhamnolipids can cross the whole gel and induce swelling on the opposite side of a two-face Petri dish. The swelling front delimits an area where the mechanical properties of the surface properties are modified: water wets the surface more easily, which increases the motility of individual bacteria and enables collective motility. A genetically engineered mutant unable to secrete rhamnolipids and therefore unable to swarm, is rescued from afar with rhamnolipids produced by a remote colony. These results exemplify the remarkable capacity of bacteria to change the physical environment around them and its ecological consequences. If there’s enough time, I will also present an innovative Deep-Learning based method to segment and track bacterial cells in dense populations, and I will show first results related to the density-dependant collective motile behaviour.
Biology is a discipline studying life-as-we-know-it: life on earth, based on carbon chemistry. Even though it is the result of millennia of evolution through natural selection, there is no reason to believe that this form of flora and fauna around us is the only possible ‘solution', leading to one of the fundamental obstacles in theoretical biology: how can one establish general theories of life when only one instance of it is available to us? The field of Artificial life — essentially a synthetic approach to biology — is trying to bridge the gap by exploring life-as-it-could-be. The reactions and types of molecules we observe in living systems, however, can constrain and make the exploration difficult. We therefore turn to artificial chemistries that study and explore 'chemical' reactions as they could be imagined. In an artificial chemistry, one starts with reactants, i.e., atoms, that follow certain interaction rules we prescribe. This leads to a combinatorial space of possible structures, i.e., molecules, that goes beyond what we know in the chemistry we live in. We then define how the molecules interact (this can be whatever one wishes), leading to the dynamics in this vast combinatorial space. Even though mostly computational, with fast developments of subfields in (bio)chemisty such as supramolecular chemistry and DNA nanotechnology, experimental realizations of artificial chemistries are not so far fetched anymore. I will discuss our recent work on self-assembly of particles into supracolloidal complexes through programmable folding, and show experimental realizations of colloidal foldamers - our the first step toward realization of complex architectures.
We all have in all likelihood more bacterial cells in us and on us than our own cells. Many bacteria bear appendages called Type IV pili. These long retractable polymers enable bacteria to exert forces on their surroundings and between each other. We will show characterization of the physical forces involved and how they can shape the interaction between bacterial cells and between bacterial cells and substrates whether abiotic or human cells.
Brains can gracefully weed out irrelevant stimuli to guide behavior. This feat is believed to rely on a progressive selection of task-relevant stimuli across the cortical hierarchy, but the specific across-area interactions enabling stimulus selection are still unclear. Here, we propose that population gating, occurring within primary auditory cortex (A1) but controlled by top-down inputs from prelimbic region of medial prefrontal cortex (mPFC), can support across-area stimulus selection. Examining single-unit activity recorded while rats performed an auditory context-dependent task, we found that A1 encoded relevant and irrelevant stimuli along a common dimension of its neural space. Yet, the relevant stimulus encoding was enhanced along an extra dimension. In turn, mPFC encoded only the stimulus relevant to the ongoing context. To identify candidate mechanisms for stimulus selection within A1, we reverse-engineered low-rank RNNs trained on a similar task. Our analyses predicted that two context-modulated neural populations gated their preferred stimulus in opposite contexts, which we confirmed in further analyses of A1. Finally, we show in a two-region RNN how population gating within A1 could be controlled by top-down inputs from PFC, enabling flexible across-area communication despite fixed inter-areal connectivity.
Why biological quality-control systems fail is often mysterious. A fascinating observation is that cells override checkpoints despite the presence of errors. We wished to understand this phenomenon quantitatively and at the system level. We propose a mathematically optimal checkpoint strategy, balancing the trade-off between risk and opportunities for growth. The theory predicts the optimal override time without free parameters based on two inputs, the statistics i) of error correction and ii) of survival. We applied the theory experimentally to the DNA damage checkpoint in budding yeast. Using a novel fluorescent reporter for DNA breaks, we quantified i) the probability distribution of DNA break repair as well as ii) the survival probability after override with one DNA break. Based on these two measurements, the optimal checkpoint theory predicted remarkably accurately the DNA damage checkpoint override times as a function of DSB numbers. Thus, a first-principles calculation uncovered hitherto hidden patterns underlying the noisy checkpoint override process. The universal nature of the balance between risk and speed is in principle relevant to many other systems, including other checkpoints, developmental decisions, or reprogramming of cancer cells.
The incredible biodiversity that characterizes natural ecosystems has attracted ecologists for a long time and, more recently, has started gathering interest also among theoretical physicists. From a statistical physics perspective, measuring the interactions in a diversity-rich ecosystem is extremely demanding and requires advanced random matrix theory and inference techniques. Moreover, a well-established theoretical framework able to explain data-driven outcomes is still missing, compounded by a series of general unresolved questions. In this talk, I will address some of these questions by discussing the Generalized Lotka-Volterra model in the presence of many randomly interacting species and finite demographic fluctuations. Leveraging on disordered systems’ techniques, I will unveil a rich structure in the organization of the equilibria  and relate the slowing down of correlation functions to glassy-like features. Finally, I will discuss possible generalizations to non-logistic growth functions [2-3]. On the one hand, this will lead to astonishing stabilization mechanisms to be framed within the long-standing diversity-stability debate initiated by May. On the other hand, these developments will allow us to describe positive feedback mechanisms, especially weak and strong Allee effects , and therefore to pinpoint new phase transitions as a smoking-gun signature of criticality. References:  A. Altieri, F. Roy, C. Cammarota, G. Biroli, Phys. Rev. Lett. 126, 258301 (2021);  A. Altieri, G. Biroli, SciPost Physics 12, 013 (2022);  I. Hatton, O. Mazzarisi, A. Altieri and M. Smerlak, submitted to Science (2023).
Mesoscopic neuronal population dynamics describes the collective activity of neuronal networks at a coarse-grained spatial scale, where fluctuations due to a finite number of neurons cannot be neglected. A prime example where a mesoscopic description is crucial, is metastable dynamics in cortical and hippocampal circuits such as Up-Down states and sequential activations of place cells called hippocampal replay. As metastability has been attributed to noise and/or slow fatigue mechanisms, I propose a concise mesoscopic model in form of a Langevin equation which accounts for both. Crucially, the model is bottom-up: it is analytically derived from the dynamics of finite-size networks of Linear-Nonlinear Poisson neurons with short-term synaptic depression. As such, noise is explicitly linked to stochastic spiking and network size, and fatigue is explicitly linked to synaptic dynamics. The Langevin equation is computationally inexpensive to simulate and enables a thorough study of metastable dynamics in classical setups (population spikes and Up-Down-states dynamics) by means of phase-plane analysis. It also constitutes the basic component of our mesoscopic model for replay. Compared to the deterministic Romani-Tsodyks model of place-cell dynamics, it exhibits a higher level of variability regarding order, direction and timing of replayed trajectories. This variability is the product of a new dynamical regime where metastability emerges from a complex interplay between finite-size fluctuations and local fatigue. Furthermore, I will discuss how a mesoscopic model can be used to infer the type of noise underlying Up-Down-states dynamics (external additive noise vs. intrinsic multiplicative (or demographic) noise), and how to account for neuronal refractoriness in the mesoscopic theory. See also: https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010809
The fruit fly larva is a maggot which looks like a dull white cylinder. Within a few days, and without any changes in its genome sequence, it metamorphoses. It gets its sophisticated adult fly shape with wings, legs, antennas, and compound eyes. How do cells migrate, deform, and rearrange to shape a tissue? To approach step by step the dynamics of morphogenesis, we will journey from developmental biology to mechanics, from individual cells to continuous tissues, and from experiments to modeling. We will investigate flows within geometries specifically designed to discriminate between models. We will witness the appearance at tissue scale of mechanical properties or collective movements which are not apparent at cell scale. Bibliography : http://francois.graner.name/recherche/articles.php
Anisotropic cells can exhibit long-range orientational order and topological defects, which often influence their dynamics during shape formation. In the first part of this talk, I will present conditions for an oscillatory morphological transition between a spherical and a dendritic phase in Alcanivorax borkumensis biofilm-covered oil microdroplets. Using phase-field approaches, we studied the dynamics of 2d liquid-liquid interfaces via the coupling between an effective interfacial tension that depends non-monotonically on the bacteria density and a bacteria proliferation rate with a homeostatic state. In experiments, interfacial tubulation occurs preferentially at topological defects and bacteria assemblies on tubes have long-range nematic order. In the second part of this talk, I will discuss how explicit order-curvature couplings influence the buckling of liquid-crystal membranes at integer topological defects and identify conditions for the instability of flat membranes. These results show how bacteria biofilms can utilize topological defects to facilitate membrane deformation and increases oil access. This is a joint work with M. Prasad, N. Obana, S.-Z. Lin, K. Sakai, J. Prost, N. Nomura, J.-F. Rupprecht, J. Fattaccioli, and A.S. Utada.
Intermediate filaments (IF) are involved in key cellular functions including polarization, migration, and protection against large deformations. These functions are related to their remarkable ability to extend without breaking, a capacity that should be determined by the molecular organization of subunits within filaments. However, this structure-mechanics relationship remains poorly understood at the molecular level. In the first part of the talk I will present how we showed that vimentin filaments exhibit a ~49 nm axial repeat both in cells and in vitro using super-resolution microscopy (SRM). As unit-length-filaments (ULFs) precursors, which are the building blocks for filament elongation, were measured at ~59 nm, this demonstrates a partial overlap of ULFs during filament assembly. Using an SRM-compatible stretching device, we also provide evidence that the extensibility of vimentin is due to the unfolding of its subunits and not to their sliding, thus establishing a direct link between the structural organization and its mechanical properties (1). In the second part of the talk, I will present how we uncovered the contribution of filament spontaneous fragmentation in the assembly dynamics of type III vimentin IF using a combination of in vitro reconstitution probed by fluorescence imaging and theoretical modeling (2). (1) Nunes Vincente F et al (2022) Molecular organization and mechanics of single vimentin filaments revealed by super-resolution imaging. Sci. Adv. 8: eabm2696. (2) Tran et al (2023) Fragmentation and Entanglement Limit Vimentin Intermediate Filament Assembly. PRX 13 : 011014
Cilia and flagella are essential organelles composed of a cylinder of 9 doublet microtubules called the axoneme. They are assembled by Intraflagellar transport (IFT), the movement of large multi-protein complexes driven by kinesin or dynein molecular motors that transport flagellar precursors at the tip for incorporation during construction. This can be compared to trains travelling on tracks. Although 9 doublets microtubules are theoretically available for train trafficking, only doublets 3-4 and 7-8 are used in the protist Trypanosoma brucei. During this presentation, we will use a combination of imaging and functional data to unveil how such a complex system could function. Working out how these elements are articulated could be the key for the comprehension of the evolution of different types of cilia and flagella (Mallet and Bastin, BioEssays 2022).
We use biomimetic emulsions to understand the physical basis of collective remodeling in biological tissues. In particular, we focus on the interplay between adhesion and extrinsic mechanical forces to control the emergence of tissue architecture during morphogenesis. As our biomimetic emulsions reproduce the passive mechanical and adhesive properties of cells in biological tissues, we study their elasto-plastic to an applied mechanical perturbation. To do so we flow them in 2D microfluidic constrictions and track droplets deformations, as well as plastic rearrangements. Surprisingly, the presence of adhesion does not affect the global topology of rearrangement avalanches in the emulsion, but only slows down the first stages of individual T1 events. As a result, adhesive droplets exhibit larger deformations and are globally aligned with the direction of tissue elongation. This result indicates that adhesion alone can induce cell polarization in elongating tissues, which could in turn trigger a mechanosensitive feedback in the tissue. Conversely, in static packings we uncover a threshold amount of adhesive contacts above which adhesion percolation sets the deformation of all droplets in the packing, independently of their local adhesive properties. In biological tissues, this indicates that tuning the adhesion properties in a limited number of cells should be sufficient to modify globally the mechanical properties of the tissue. In parallel to these in vitro approaches, we use oil droplets as force sensors in vivo, in developing zebrafish embryos. In particular, the injection of biocompatible oil droplets in their olfactory placode allowed us to measure the presence of anteroposterior compressive forces that can contribute to axon elongation in olfactory neurons. We are currently developing biocompatible self-functionalizing droplets in order to obtain the full force map in the placode and in surrounding tissues during development.
Organisms acquire and use sensory information to guide their behaviors. Likewise, scientists acquire and use the information contained in experimental data to better understand systems of interest. In both cases, the amounts of information available are usually limited, so using it efficiently is critical. In this seminar, I will discuss two aspects of efficient information usage. First, I explore information usage by cells, describing how we have discovered that motile Escherichia coli cells (arguably the simplest model of biological behavior) acquire very little information but use it highly efficiently. Second, I examine information usage by scientists, elaborating on how faced with noisy fluorescence data from single E. coli cells, we developed a method to extract relevant signals from raw data with theoretically maximal efficiency. Finally, I examine similarities between these two processes.  Kamino, K., Keegstra, J. M., Long, J., Emonet, T., and Shimizu, T. S. (2020). Adaptive tuning of cell sensory diversity without changes in gene expression. Science Advances, 6(46), eabc1087.  Mattingly*, H. H., Kamino*, K., Machta, B. B., and Emonet, T. (2021). Escherichia coli chemotaxis is information limited. Nature Physics, 17(12), 1426-1431. (*Equal contribution)  Kamino*, K., Kadakia, N., Aoki, K., Shimizu, T. S., and Emonet*, T. (2022). Optimal inference of molecular interaction dynamics in FRET microscopy. In revision in PNAS (*Corresponding authors)
Topology presents global constraints of a system that limit and guide functionality. Like many complex physical systems, topology plays a fundamental role in living systems, in terms of structures stability, transport etc. In this talk, I will discuss some recent published and unpublished work on topological morphogenesis of multicellular structures. For example, animal organs exhibit complex topologies involving cavities and tubular networks, which underlie their form and function. However, how topology emerges during the development remained unknown and unseen. We recently show that tissue topology and shape is governed by two distinct modes of topological transitions [1,2]. One mode involves the fusion of two separate epithelia and the other involves the fusion of two ends of the same epithelium - as a result increasing its’ genus. We study the general behaviour of these topological transitions based on a coarse-grained description of epithelial elasticity. Predictions from the theory relate the relative rates of the two epithelial fusion modes to cellular mechanics, which allowed us to tune the topology and guide morphogenesis. Recently, we are refining these concepts by studying the 2-dimensional analogue of this system theoretically and experimentally. I will end with a global overview of the of the role of topology in morphogenesis across different systems.  Topological morphogenesis of neuroepithelial organoids. Nat. Phys. (2022). https://doi.org/10.1038/s41567-022-01822-6  Topological control of synthetic morphogenesis. Nat. Phys. (2022). https://doi.org/10.1038/s41567-022-01843-1
The specificity of cortical versus subcortical representations for sensory processing is still somewhat mysterious, considering the fact that cortex is dispensable for many sensory behaviors, but necessary in others, as shown both for auditory and tactile modalities. We have explored this question using large scale sampling of the auditory system and have shown that cortical representations are key for decorrelating information that is available in time sequences. Without this computation, association of sequence information to behavior is extremely slow, as we showed by training mice to discriminate spatio-temporal patterns directly injected in cortex. We have also observed that cortical representations are extremely sensitive to anesthesia, including the sparse representations that sort out temporal information in sounds. Hence, the awake auditory cortex implements a generic transformation that replicates temporal information into time-independent neural population dimensions to make it available for learning and classification.
We study the assembly of programmable quasi-2D DNA compartments as “artificial cells” from the individual cellular level to multicellular communication. We will describe recent progress toward autonomous synthesis and assembly of cellular machines, synchrony, pattern formation, fuzzy decision-making, and electric field manipulation of gene expression.
Next generation sequencing (NGS) is revolutionizing all fields of biological research but it fails to extract the full range of information associated with genetic material. Complementary genomic technologies such as Optical genome mapping and nanopore sequencing, that analyze individual, unamplified genomic DNA, are filling the gaps in the capabilities of NGS. Using such technologies we gain access to the structural variation and long range patterns of genetic and epigenetic information. Recent results from our lab demonstrate our ability to detect and map the epigenetic marks 5-methylacytosine and 5-hydroxymethylcytosine in kidney cancer. This new technology allows genetic and epigenetic variation calling on the single cell level without the need to process single cells.
In this talk, I will present recent methodological developments inspired by the Covid-19 pandemic. How big is an outbreak when a first case is detected? When did the outbreak actually start? And how much (un)certainty is there about these estimates? Early epidemiological dynamics are inherently stochastic, and cannot be properly described by deterministic models. I will present stochastic models developed in collaboration with Pete Czuppon, François Blanquart and Sofìa Jijón to address these questions. We will consider two Covid-19 case studies: the emergence of the Alpha variant in the UK in 2020, and the emergence of SARS-CoV-2 in Wuhan in 2019. I will also discuss the specificities and associated challenges of the datasets our work relies on, and the limitations of our approach. Time permitting, I will finish the talk with a discussion of the current state of search for the origin of SARS-CoV-2.
Single base substitutions in DNA, of which some lead to pathogenic conditions, are known to be non-random and influenced by their flanking base sequence. The latter is also known to modulate the long-range electron-hole transfer that occurs through stacked DNA bases. In this context, we studied the connection between the frequency of single base substitutions observed in human genomes and the ionization potential of DNA base motifs estimated using quantum chemistry calculations. We found good correlations between th¬¬ese quantities for germline variants as well as for somatic variants in cancer or normal tissues. The correlations are especially high for synonymous variants which suffer from less selective pressure than missense variants, and for certain types of cancer mutations. With this analysis we support the role of electron-hole transport in the complex biophysical mechanisms underlying mutagenesis and pathogenicity. Another question we addressed is how natural evolution acts on DNA and protein sequences to ensure mutational robustness and evolvability. We performed a structurome-scale computational study, in which we estimated the change in folding free energy upon all possible single-site mutations in a large protein structure dataset¬. Our results highlight the non-universality of mutational robustness and its multiscale dependence on protein features, the structure of the genetic code, and the codon usage. Our analyses and approach are strongly supported by available experimental mutagenesis data.
In the course of evolution, proteins diversify their sequences while keeping their 3D structure and biological functions remarkably conserved. Modern sequence databases provide us with increasingly large samples for this sequence diversity. In my talk I will describe how these samples can be used to infer data-driven protein sequence landscapes, which in turn allow to statistically “forecast” protein evolution. I will discuss three examples: first the analysis of polymorphisms across more than 60,000 fully sequenced E. coli strains , second the prediction of mutability of SARS-Cov-2 proteins , and finally the modelling of experimental protein evolution .  Vigué, L., Croce, G., Petitjean, M., Ruppé, E., Tenaillon, O., and Weigt, M. Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes. Nat Comm 2023  Rodriguez-Rivas, J., Croce, G., Muscat, M., and Weigt, M. Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes. PNAS 2023  Bisardi, M., Rodriguez-Rivas, J., Zamponi, F., and Weigt, M. Modeling sequence-space exploration and emergence of epistatic signals in protein evolution. Mol Biol Evol 2022.
Proteins are the machinery of life facilitating the key processes that drive living organisms. The physical arrangement of amino acids dictates how proteins fold and interact with their environment. Recent advances have increased the number of experimentally resolved or computationally predicted tertiary structures, however we still lack a practical understanding of how 3D structure determines the function of a protein. While machine learning has been at the forefront of protein science, the inferred models are often hard to interpret physically. In this talk I will introduce physically motivated machine learning approaches to learn interpretable models of protein micro-environments, reflecting the underlying biophysics. With these models we infer amino acid preferences given a surrounding atomic neighborhood, and predict the impact of evolutionary substitutions in proteins. Our computational approach establishes an interpretable model for how biological function emerges from protein micro-environments. The flexibility and efficiency of this approach also show promise for building generative models to design novel protein structures with desired function.
The relationship between the number of cells colonizing a new environment and time for resumption of growth is a subject of long-standing interest. In microbiology this is known as the “inoculum effect”. Its mechanistic basis is unclear with possible explanations ranging from the independent actions of individual cells, to collective actions of populations of cells. Here we use a millifluidic droplet device in which the growth dynamics of hundreds of populations founded by controlled numbers of Pseudomonas fluorescens cells, ranging from a single cell, to one thousand cells, were followed in real time. Our data show that lag phase decreases with inoculum size. The average decrease, variance across droplets, and distribution shapes, follow predictions of extreme value theory, where the inoculum lag time is determined by the minimum value sampled from the single-cell distribution. Our experimental results show that exit from lag phase depends on strong interactions among cells, consistent with a ”leader cell” triggering end of lag phase for the entire population.
Adherent cells in vivo often reside on extracellular matrices (ECMs) that possess a topographical organization at different scales. Various types of engineered microstructured substrates have been developed to study the impact of basal topographical cues on cell behavior in vitro. Amongst them, microgrooves mimicking the anisotropic organization of the ECM have been shown to align and elongate different cell types. However, how cells detect and respond to microgrooves remains unclear, particularly for cellular monolayers. We are investigating these questions using vascular endothelial cells (ECs), which form a monolayer lining the inner surfaces of blood vessels, as a model. In the first part of this presentation, I will focus on the collective migration of vascular endothelial cell monolayers on microgrooved substrates. We describe the emergence of a specific pattern of collective motion characterized by large antiparallel cell streams, which can be predicted in the physical framework of active fluids. I will then address the cellular and subcellular mechanisms underlying the contact guidance response of vascular endothelial cells on the microgrooves. We have in particular highlighted distinct regimes and mechanisms of response to topography in single cells vs. monolayers. Finally, I will present some preliminary results on the deformation of cell nuclei on microgrooved substrates. We have characterized the cellular mechanisms leading to nuclear deformations in the grooves and investigated the links between nuclear deformations and mechanics. We believe that microgrooves constitute a new, simple and high-throughput system to study cell and nuclear mechanics in physiological and/or pathological settings.
Our understanding of the 3D organization and dynamics of mammalian chromosomes in the nucleus, in relation with functional genomic processes, has progressed greatly over the past decades. However, the physical principles underlying this organization remain partially understood due to the lack of tools to directly exert and measure forces on interphase chromosomes inside the nucleus and probe their material nature. To address this gap, we have developed a novel approach to mechanically manipulate chromosomes in the nucleus of a living cell using magnetic forces . It consists in targeting iron-containing nanoparticles to a genomic locus of interest and applying a controlled magnetic field. With this approach, we made the first measurements of how an interphase chromosome responds to a point force and recoils after force release. We observed viscoelastic displacements over microns within minutes in response to near-picoNewton forces. The trajectories we measured are consistent in first approximation with a surprisingly simple physical model for polymer dynamics known as ‘Rouse’ (i.e. a phantom-chain elastic polymer in a liquid). Our results highlight the fluidity of chromatin, with a moderate but detectable contribution of the surrounding material, revealing minor roles for crosslinks and topological effects, and challenging the view that interphase chromatin is a gel-like material. We also characterized, for the first time, physical parameters of interphase chromosomes (force-displacement, energy barriers, fluctuation-dissipation relationship...). Lastly, the forces we found sufficient to substantially displace a genomic locus are of comparable magnitudes with the forces exerted by molecular motors (e.g. polymerases), opening perspectives to understand how biological processes operating on chromosomes may largely alter their conformations in return. Our new approach opens avenues for future research to probe how the physical properties of the genome relate to many genome functions, including transcription, chromosome segregation, DNA damage repair and replication.  Keizer et al. (2022). Science, 377:489-495. DOI: 10.1126/science.abi9810
Recently, systems neuroscience has experienced a surge in interest in the neural control of complex behaviours. This shift has occurred in part due to technological advances in automated behavioral annotation enabling precise quantification of movement, and in multi-region recording techniques which have shown that motor, task, and reward information is widespread across brain regions. However, our current understanding of neural circuit computations are based on decades of reduced experimental paradigms that have aimed to limit behavioural variability. For example, the cerebellar cortex has a famously "crystalline" circuitry that has been argued to optimally implement associative learning in the context of conditioning experiments. However it is unclear how these theories extend towards more complex behaviours that implicate the cerebellum. In the first part of this talk, I will present our lab's latest efforts towards this end by quantifying the dimensionality of granule cell representations in freely behaving mice, extending classic theories of the cerebellar cortex towards reinforcement learning, and building hierarchical models of mouse locomotor coordination to disentangle different covariates of motor learning. Second, in order to better understand how neural representations give rise to behaviour, better tools are key to identify behaviourally-relevant structure in large-scale data. Common methods such as PCA identify zero-lag covariance patterns across neurons that evolve over the course of experiment. However, this view may miss structure that is shared across trials or time, including task-relevant neural sequences and representations that evolve over learning. Towards this end, we have developed a new unsupervised dimensionality reduction method, sliceTCA, that decomposes the data tensor into components that identify different classes of shared variability (across neurons, time, or trials). In the second part of the talk I will demonstrate how sliceTCA is able to demix these different sources of shared variability in three example large-scale datasets, including a multi-region dataset from the IBL. Finally, I will provide geometric intuition for how sliceTCA can capture latent representations that are embedded in both low and high-dimensional subspaces, thereby capturing more behaviourally-relevant structure in neural data than classic methods.
Hydra is a fascinating model organism for neuroscience. It is transparent; new genetic lines allow one to image activity in both neurons and muscle cells; it exhibits quite rich behavior; and it continually rebuilds itself. Hydra’s fairly simple physical structure as a two-layered fluid-filled hydrostat and the accessibility of information about neural and muscle activity open the possibility of a complete model of neural control of behavior. Toward that end, we have developed a biophysical and biomechanical model of Hydra's body that allows us to transform measured neural activity into behavior.
Modular structures in the brain are hypothesized to play a central role in intelligence by permitting compositional representations, but the general mechanisms driving discrete structure emergence from more-continuous genetically specified morphogens have remained elusive. Grid cells represent self-location during navigation in 2D spaces with spatially periodic codes of a small number of discrete periodicities. They present a paradigmatic example of the computational advantages of modular representations– permitting exponential representational capacity and strong intrinsic error-correction by representing a continuous euclidean variable (location), with a modular set of position-encoding phases. Underlying this discrete modular coding, which emerges rapidly postnatally, are biophysical gradients that vary smoothly. I will consider how smooth gradients can give rise to globally discrete function through self-organization in the context of this system. We show that two purely local lateral interactions, one with a smoothly graded parameter in the brain and another without a spatial gradient can simultaneously give rise to local pattern formation and global modularity. We show that this mechanism of modularity emergence is highly generic, robust, and almost completely insensitive to parameters because it is a topological process. The model makes predictions for the relationships between modules that furnish the most accurate match to data to date. Abstractly, the mechanism involves dynamics on the sum of two energy landscapes, one of which is a shallow global minimum that smoothly moves with the spatial parametric variation, and the other of which is a static landscape with multiple narrow minima. We believe this is a novel self-organization mechanism by which simple local interactions and smooth gradients may interact to induce macroscopic modular organization.
Widefield microscopy methods applied to optically thick specimens are faced with reduced contrast due to “spatial crosstalk”, in which the signal at each point in the field of view is the result of a superposition from neighboring points that are simultaneously illuminated. In 1955, Marvin Minsky proposed confocal microscopy as a solution to this problem. Today, laser scanning confocal fluorescence microscopy is broadly used due to its high depth resolution and sensitivity, which come at the price of photobleaching, chemical, and photo-toxicity. Here, we present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity, and chemical specificity, on unlabeled specimens, nondestructively. Thus, we augmented a commercial laser scanning confocal instrument with a quantitative phase imaging module, which provides optical pathlength maps of the specimen in the same field of view as the fluorescence channel. Using pairs of phase and fluorescence images, we trained a convolution neural network to translate the former into the latter. The training to infer a new tag is very practical as the input and ground truth data are intrinsically registered, and the data acquisition is automated. Remarkably, the ACM images present significantly stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and 3D liver cancer spheroids. By training on nucleus-specific tags, ACM allows for segmenting individual nuclei within dense spheroids for both cell counting and volume measurements. Furthermore, taking the estimated fluorescence volumes, as annotation for the phase data, we extracted dry mass information for individual nuclei. In sum, ACM can provide quantitative, dynamic data, nondestructively, from both thin and thick samples, while chemical specificity is recovered computationally.
The mechanism by which living organisms seek optimal light conditions, phototaxis, is a fundamental process for motile photosynthetic microbes. It is involved in a broad array of natural processes and applications from bloom formation to the production of high-value chemicals in photobioreactors. Our experiments with model motile micro-algae Chlamydomonas Reinardthii investigate the response of a dilute or semi-dilute suspensions to local illumination via a laser beam. The denser micro-algae concentrate around the laser spot which results in an increase of the local fluid density. In turn, this can generate bio-convection cells, which spatial range is far larger than the spot width. These bio-convective flows appear in a range of dimensionless Rayleigh number (which quantifies the relative importance of convection over diffusion) far below the critical one required for spontaneous (without light excitation) bio-convection. When initial concentration and suspension thickness are small enough (low Rayleigh number), the stationary concentration profile results in an equilibrium between diffusion and phototactic flux. In this situation, the very good agreement between experiments and numerics also enables to measure the diffusion and phototactic motility coefficients of Chlamydomonas Reinardthii . More recently, we have shown that a population of the model alga Chlamydomonas reinhardtii exhibits a highly sensitive nonlinear response to light . At higher Rayleigh number, various dynamical regimes are observed : waves of concentration propagate radially with well-defined velocity, a fingering pattern with a well-defined orthoradial wavelength or directional budding with an unique finger of high-concentration. Our experimental results compare well with numerical simulations of a relatively simple model of bio-convection. This allowed us to identify the mechanisms of the instability that generates the pattern of waves. More precisely, it is gyrotaxis (i.e. the ability of flagellated microbes to self-orient their swimming direction in a shear flow) that induces the focusing of algae in a thin layer, which eventually destabilizes under gravity. These bioconvective flows enable the continuous mixing of the fluid by a simple light beam, which is especially interesting in confined geometries or when algae concentration is low.  J. Dervaux, M. Capellazzi Resta and P. Brunet. Light-controlled flows in active fluids. Nature Physics 13, 306-312 (2017).  A. Ramamonjy, J. Dervaux and P. Brunet. Nonlinear phototaxis and instabilities in suspensions of light-seeking algae. Physical Review Letters (In press)
The 'social' amoeba D. discoideum is facultatively multicellular. Starvation triggers a life cycle where single cells come together to form multicellular fruiting bodies, essential for efficient dispersal and long-term survival. In this process, part of the cells die while promoting the survival of the spores. The evolution of self-sacrificial behaviour is more easily understood when all cells in the body share the same genome. It is therefore puzzling to observe that in natural conditions multicellular aggregates tend to be genetic chimeras, so that genetic conflicts are unavailable. Theory predicts that the spread of genotypes that reap more than their fair share of benefits from the group -- the so-called cheaters -- should prevent cooperative behaviour to be evolutionary stable. We compared the social performance in chimeras composed of isogenic cells harvested at different phases of population growth, and found that social behavior is modulated by phenotipic plasticity as well as genetic background. By tracing the origin of spore biases to the process of aggregation from single cells, we explored the single-cell determinants of differences in social behaviour. Finally, we show that biases due to non-genetic sources of phenotypic variation are comparable to genetic effects, and can dominate over genetic differences, overturning classical definitions of social behaviour. Our observations suggest that inevitable heterogeneity in cell-level physical properties may act - by breaking heritability of social behaviour - as a hindrance to the evolutionary success of cheaters, and this even when social interactions within the multicellular body are neglected.
One of the hallmarks of nuclear organization in eukaryotes is the spatial segregation of transcriptionally active (euchromatin) and inactive (heterochromatin) genomic regions. Recently we found that such compartmentalization is driven by affinity between heterochromatin regions (Falk et al Nature 2019) through microphase separation. Despite the widespread of such compartmentalized organization in nature, its functional roles remain elusive. Here we examine the role of compartmentalization in the maintenance of epigenetic memory, i.e. maintenance of pattern of histone marks for hundreds of generations. We modeled joint dynamics of chromatin and histone marks: loss and spreading of marks, and refolding of chromosomes through the cell cycle. A surprinting analogy between the spreading of histone marks and the spreading of a disease in a pandemic helped to identify factors that provide robust memory. We further found a parallel between epigenetic memory and an associative memory in the neural network. Our analysis shows that operation of chromatin as a memory device requires enzyme limitation and spatial spreading of the marks in the dense and spatial segregated heterochromatin, suggesting a functional role for this hallmark of nuclear organization.
The cell cortex is a contractile actin meshwork, which determines cell shape and is essential for cell mechanics, migration and division. Because the cortical thickness is below optical resolution, it has been generally considered as a thin uniform elastic and contractile layer. Using two mutually attracted magnetic beads, one inside the cell and the other in the extracellular medium, we pinch the cortex of live dendritic cells and provide an accurate and time resolved measure of its thickness. Our observations draw a new picture of the cell cortex as a highly dynamic layer, harboring large fluctuations in its third dimension due to actomyosin contractility. We propose that the cortex dynamics might be responsible for the fast shape changing capacity of highly contractile cells that use amoeboid-like migration. Depending on time I will also describe some recent results on the mechanics of cell cortex.
The Maximum Entropy principle is an inference framework that allows for finding the discrete Boltzmann distribution that reproduces at best the empirical statistics of a chosen dataset. In this talk I will present some applications of this strategy to the spiking activity of real neurons recorded during experiments, either in the retina or in the cortex. At first I will build the framework and explain how we can solve the inference problem by taking advantage of our stat. phys. knowledge of spin systems. Then I will discuss an application of this method to the prefrontal cortex of behaving rats and how it allows for identifying cell assemblies that undergo memory reply. I will then show how maximum entropy models account well for the system collective behaviour when the input stimuli have short-ranged correlations, but fail for inputs with long-ranged ones. This happens, for example, in the cortex during sleep, or in the retina for full-field visual stimuli. In the latter case, to solve this issue we apply a previously developed framework that, in addition to network effects, accounts for external stimuli that drive the system in time. In this case, the inferred model is not anymore a disordered Ising model, but it takes the form of a random field Ising model with short-ranged ferromagnetic couplings, and disordered magnetic fields.
Fluid-filled biological cavities, or lumens, are ubiquitous in tissues and embryos (1), and have been widely studied experimentally. But their collective dynamics and the control of their size has remained largely unexplored from a physical perspective. First, we focus on a particular type of lumens, which are located at the adhesive side of cells and can therefore interact directly through the intercellular space, as recently observed in the very first stages of mouse embryogenesis (2). Based on these experimental observations, we propose a generic model to describe the hydraulic and osmotic exchanges between lumens themselves, and with the surrounding cellular medium (3). Lumens are pressurized by a surface tension, which leads naturally to their coarsening into a single final cavity through hydraulic exchanges. With extensive numerical simulations and a mean-field theory we predict that such coarsening dynamics follows a robust scaling law, that barely depends on concentration heterogeneities between lumens. On the contrary, active osmotic pumping largely influences the collective dynamics by favoring lumen coalescence and by biasing the position of the final cavity. Then, we propose a generic model to describe the control of cavity size by osmotic gradients, starting from the classical pump-leak mechanism for a single cell, and that applies indifferently to basal or apical lumens. We predict that mechanics plays generally no role on the volume of a single cavity, that the presence of impermeants in the cavity is essential to ensure the existence of a stable state, and we study the impact of paracellular transport on lumen growth. Our theoretical work provides a generic theoretical framework for hydraulic and electro-osmotic control of biological cavity formation and maintenance, that shall find further applications in embryo and tissue morphogenesis.
Chaotic systems are traditionally thought to be dynamical systems that are quantifiably difficulty to predict. However, the striking fractal geometry of strange attractors underscores the generative nature of chaos: like probability distributions, repeated measurements of chaotic systems produce arbitrarily-detailed information about the underlying attractor. Chaotic systems thus pose a unique challenge to modern statistical forecasting models, requiring representations that correctly encode their fractal geometry while capturing their underlying mathematical properties. I will describe my recent work on representing and forecasting chaotic systems. Using a collection of hundreds of known chaotic dynamical systems spanning fields such as astrophysics, climatology, and biochemistry, I show that chaoticity and empirical predictability are only weakly coupled. Instead, contemporary machine learning algorithms uncover structural properties of chaotic attractors, such as dominant orbits, hidden variables, and latent symmetries. I will show how tools from chaos can assist in general machine learning problems, such as time series classification, importance sampling, and symbolic regression.
How genuinely new protein-coding genes originate is a central question in biology. Long thought impossible to arise from non-coding sequence, novel genes arising de novo from genomic "junk" DNA or from long non-coding RNA were recently found in eukaryotic genomes. Novel genes are taxon-restricted and may encode structurally novel proteins with new protein domains. To understand how novel genes arise, we built a mathematical model based on gene and genome parameters and dynamic factors such as mutation. We combined phylostratigraphy and proteogenomics to identify novel genes in 25 eukaryotic genomes and evaluated their predicted biophysical properties. Compared to ancient proteins, novel proteins are shorter, more fragile, disordered and promiscuous, yet less prone to aggregate or to form toxic prions. We performed biophysical experiments comparing novel and ancient proteins, showed that novel genes function in vivo in zebrafish brains, and found novel genes are expressed in human brains at multiple ages. Genomic sequence turnover generates many novel genes encoding short proteins, of which some are maintained and encode proteins with distinct structural features and expressed in the brain. Thus, genomic variation continuously generates new protein structures and new functions.
Behavior and decision-making are determined by physical processes taking place in the complex environment of the brain. Experimental techniques have reached the point where it is now possible to map the complete wiring diagram (the physical connectome of synaptic connections between neurons) of the brain of simple model organisms at the level of single synapses, and to manipulate individual neuron activity in freely moving animals and observe the resulting behavior. Together this lets us investigate how the structure of the connectome constrains an organism’s capability to process information and generate behavior. I will discuss how we can combine knowledge of an animal’s connectome with large-scale behavioral experiments to link neural circuits to decision making and specific behavioral sequences. I will mainly focus on how to extract the statistical regularities (i.e., “motifs”) of a connectome of an animal. Relying on a restricted set of statistically regular circuit motifs, optimized for specific functions, may provide an animal with biologically advantageous inductive biases for efficient learning and help encode innate behaviors. Information theory furthermore tells us that the presence of statistically regularities would make the connectome compressible, and circuit motifs would thus provide a means to encode the neural wiring information in the limited storage space of the genome. Identifying motifs in a connectome is a challenging inverse problem since we have access to only a single experimental realization (i.e., a single graph). To circumvent problems with classic null-model-based analysis linked to multiple testing and the ill-posed problem of defining a proper null model against which statistical significance is defined, we have developed methods combining hierarchies of microcanonical random graph null models and graph compression techniques. We applied our methods to uncover circuit motifs in different brain regions of adult and larval Drosophila as well as C. elegans in different developmental stages. Our preliminary results show more compressible yet more complex brain structure in larger brains. By comparing typical circuit structures in different brains regions and animals, we may furthermore formulate hypotheses linking circuit structure to function that can be tested in behavioral experiments. I will finally discuss how we can build generative models of neural connectomes.
Collective behaviour is found in a startling variety of biological systems, from clusters of bacteria and colonies of cells, up to insect swarms, bird flocks, and vertebrate groups. A unifying ingredient is the presence of strong correlations: experiments in bird flocks, fish schools, mammal herds, insect swarms, bacterial clusters and proteins, have found that the correlation length is significantly larger than the microscopic scales. In the case of natural swarms of insects another key hallmark of statistical physics has been verified, namely dynamic scaling: spatial and temporal relaxation are entangled into one simple law, so that the relaxation time scales as a power of the correlation length, thus defining the dynamical critical exponent, z. Within statistical physics, strong correlations and scaling laws are the two stepping stones leading to the Renormalization Group (RG): when we coarse-grain short-scale fluctuations, the parameters of different models flow towards one common fixed point ruling their large-scale behaviour. RG fixed points therefore organize into few universality classes the macroscopic behaviour of strongly correlated systems, thus providing parameter-free predictions of the collective behaviour. Biology is vastly more complex than physics, but the widespread presence of strong correlations and the validity of scaling laws can hardly be considered a coincidence, and they rather call for an exploration of the correlation-scaling-RG path also in collective biological systems. However, to date there is yet no successful test of an RG prediction against experimental data on living systems. In this talk I will apply the renormalization group to the dynamics of natural swarms of insects. Swarms of midges in the field are strongly correlated systems, obeying dynamic scaling with an experimental exponent z~1.2, significantly smaller than the naive value z = 2 of equilibrium overdamped dynamics. I will show that this anomalous exponent can indeed be reproduced by an RG calculation, provided that off-equilibrium activity *and* inertial dynamics, are both taken into account; the theory gives z=1.3, a value closer to the experimental exponent than any previous theoretical determination. This successful result is a significant step towards testing the core idea of the RG even at the biological level, namely that integrating out the short-scale details of a strongly correlated system impacts on its large-scale behaviour by introducing anomalies in the dimensions of the physical quantities. In the light of this, it is fair to hope that the renormalization group, with its most fruitful consequence -- universality -- may have indeed an incisive impact also in biology.
The ability to reshape the plasma membrane is essential for cells’ life. To perform membrane reshaping, cells rely on a precise controlled coupling of the plasma membrane and the actin cytoskeleton, which can apply physical forces to the membrane while being mechanically linked to it. A prominent example is filopodia, finger-like and actin-rich membrane protrusions important for numerous cellular processes including cell morphogenesis and cancer invasion. Yet, it remains poorly understood how cells control when and where to trigger actin assembly on the plasma membrane to initiate filopodia. Notably, membrane curvature sensor protein IRSp53 has been identified as a key player in filopodia formation. However, the precise mechanism of IRSp53-driven filopodia formation remains elusive. To address this fundamental question in cell biology, we performed experiments using in vitro reconstitution systems composed of model membranes and purified proteins, including IRSp53, actin and actin regulatory proteins. In this seminar, I will present our results demonstrating how IRSp53 initiates actin-filled membrane protrusions and facilitates their stabilization.
Stochastic differential equations are often used to model the dynamics of living systems, from Brownian motion at the molecular scale to the dynamics of cells and animals. How does one learn such models from experimental data? This task faces multiple challenges, from information-theoretical limitations to practical considerations. I will present a recent and ongoing effort to develop new methods to reconstruct such stochastic dynamical models from experimental data, with a focus on robustness and data efficiency. This provides a generic means to quantify complex behavior and unfold the underlying mechanisms of an apparently erratic trajectory.
The enormous diversity of cell types in any animal model system is defined by neuron type-specific gene batteries that endow distinct cells with distinct anatomical and functional properties. Based on my laboratory’s work in Caenorhabditis elegans as well as recent gene expression studies in vertebrates and flies, I propose that the diversity of neuronal cell types can be reduced to a simpler descriptor, the combinatorial expression of a specific class of transcription factors, encoded by homeobox genes. Functional studies in multiple animal model systems have corroborated the importance of homeobox genes in specifying neuronal identity and perhaps also neuronal circuit assembly. I propose that the preponderance of homeobox genes in neuronal identity control is a reflection of an evolutionary trajectory in which an ancestral neuron type was specified by an ancestral homeobox genes and that this functional linkage then duplicated and diversified to generate distinct cell types and neuronal assemblies in an evolving nervous system.
We are interested in understanding how single cell and population events combine to ensure the maintenance of neural stem cell (NSC) pools in the adult brain. We focus on the dorsal telencephalon(pallium), which hosts NSCs in all adult vertebrates. In teleost fish such as the zebrafish, the pallial NSC pool consists of a monolayer of tightly juxtaposed radial glial cells. NSCs are mostly quiescent, but can transiently activate (ie. enter the cell cycle and divide) to divide and generate other NSCs and/or neurons. The NSC decision to activate, and the fate choices it makes at division, are two key events that condition NSC maintenance. These events are controlled at both the single-cell and the population levels, and we are taking quantitative and dynamic approaches to understand these processes in time and space. For this, we developed an intra-vital imaging method to directly record, over weeks and with single cell resolution, the behavior of NSCs in their niche (>1,000 cells per pallial hemisphere). With this method, we generated a 4D map of NSC activation and division events. Using spatial statistics and mathematical modeling in an NSC lattice, we showed that NSC activation events are spatiotemporally correlated by local and temporally delayed interactions that occur between brain germinal cells and generate self-propagating dynamics. We also observed that NSC apical size is highly predictive of NSC fate decisions at division, and are analyzing the mechanisms involved and their cell- and non-cell-autonomous impact. Together, this work will highlight how NSCs across the germinal sheet coordinate their state and fate decisions for the harmonious and long-lasting maintenance of the NSC pool.
Resource sharing outside the kinship bonds is rare. Besides humans, it occurs in chimpanzees, wild dogs and hyenas as well as in vampire bats. Resource sharing is an instance of animal cooperation, where an animal gives away part of the resources that it owns for the benefit of a recipient. Taking inspiration from blood-sharing in vampire bats, here we show the emergence of generosity in a Markov game, which couples the resource sharing between two players with the gathering task of that resource. At variance with the classical evolutionary models for cooperation, the optimal strategies of this game can be potentially learned by animals during their life-time. The players act greedily, that is, they try to individually maximize only their personal income. Nonetheless, the analytical solution of the model shows that three non trivial optimal behaviors emerge depending on conditions. Besides the obvious case when players are selfish in their choice of resource division, there are conditions under which both players are generous. Moreover, we also found a range of situations in which one selfish player exploits another generous individual, for the satisfaction of both players. Our results show that resource sharing is favored by three factors: a long time horizon over which the players try to optimize their own game, the similarity among players in their ability of performing the resource-gathering task, as well as by the availability of resources in the environment. These concurrent requirements lead to identifying necessary conditions for the emergence of generosity.
Invasion of cells through basement membrane (BM) extracellular matrix barriers is an important process during organ development and cancer metastasis. Much has been understood concerning the cell biology of invasion, but the role of cell mechanics in the invasive process is little studied. During invasion cells breach BM barriers with actin-rich protrusions. It remains unclear, however, if actin polymerization applies pushing forces to help break through BM, or if actin filaments play a passive role as scaffolding for targeting invasive machinery. Here using the developmental event of anchor cell (AC) invasion in Caenorhabditis elegans, we observe that the AC deforms the BM just prior to invasion, exerting forces in the tens of nN range. BM deformation is driven by actin polymerization nucleated by the Arp2/3 complex and its activators, while formins, crosslinkers and myosin motor activity are dispensable. Delays in invasion upon actin regulator loss are not caused by defects in AC polarity, trafficking or secretion, as appropriate markers are correctly localized in the AC even when actin is reduced and invasion is disrupted. In addition our preliminary results indicate that the AC nucleus is deformed during invasion, and the role played by the nucleus in AC invasion is currently under investigation. Overall cell and nuclear mechanics emerge from this study as important considerations in BM disruption by invading cells.
In large neuronal networks, functions emerge through the collective behavior of many interconnected neurons. Recent technical development of whole brain imaging in Caenorhabditis elegans - a nematode with 302 neurons, allowed us to ask if such emergence reaches down to even the smallest brains. In the first part of this talk, I will discuss how we use the maximum entropy principle to construct pairwise probabilistic models for the collective activity of 50+ neurons in C. elegans. These models successfully predict higher order statistical structure in the data, the topological features of the structural connectome, and show signatures of collective behavior. In the second part, I will present two ways of how perturbing the inferred model of neuronal activity can shed light on the control principles in the brain, which in turn facilitates future perturbation experiments. Firstly, by ablating and clamping neurons, we discover that the worm brain is both robust against damages and efficient in transmitting information. Secondly, by examining the local information geometry of the model, we find that a few, "pivotal" neurons account for most of the system's sensitivity, suggesting a sparse mechanism for control of the collective behavior. Finally, if time allows, I will briefly describe my current work at ENS on inferring statistical models with long memory kernel for collective dynamics in a group of social animals.
Biological tissues are composite materials, made of cells, extracellular matrix and interstitial fluid. As cells continuously proliferate, migrate, and secrete new extracellular matrix, biological tissues also build up an intrinsic stress during their growth. This complexity gives the tissues emerging rheological and biological properties, which cannot be merely traced back to those of the constitutive cells. In this work, we characterized the mechanical and biological reaction of a model tissue in response to an external mechanical perturbation. In particular, we used multicellular aggregates as a proxy of avascular and homogeneous tissues, we compressed them via osmotic shocks and modeled the experimental results with an active poroelastic material. We concluded that both the mechanical and the biological response are mainly determined by the presence of the extracellular matrix and by its mechanical state, as well as by the flows of the interstitial fluid.
A massive formation of stable sea foam is regularly observed on certain shores. These foams, of natural origin, are concomitant with a loss of phytoplankton biomass and biodiversity. Besides, liquid foams are known to act as filters for solid particles, due to the complex network of internal channels through which the liquid flows. We therefore hypothesise that a relevant part of the phytoplankton, advected in the foam during the foam formation, could be trapped in the foam. Among phytoplanktonic organisms, many are flagellated and therefore motile. In this talk, I will present experiments performed in the laboratory on a model system to investigate the sedimentation of microswimmers in a liquid foam: the unicellular bi-flagellate alga Chlamydomonas reinhardtii (CR) was incorporated into a liquid foam stabilized with biocompatible proteins. Over time, the liquid contained in the foam flows downward by gravity drainage, advecting the solid particles suspended in the liquid, which then escape from the foam and reach the underlying liquid. We measured the dynamics of escape of CR cells from the foam, and compared the case of living and of dead cells. While the dead cells are totally advected by the liquid flow, as expected for passive solid particles of similar size, the living cells sediment much more slowly, and a significant amount remains trapped in the foam at long times. I will ultimately discuss the microscopic mechanisms that can lead to this trapping.
In living tissues, cells exhibit various degrees of mobility and coordination of their movements. These motions are powered by the self-propulsion of individual cells, which also interact with their neighbours and their environment. In particular, the physical contacts between cells are known to mediate the transmission of information, which is further processed to alter the dynamics – e.g. speed, direction – of their motion. With a physicist’s view, a cell colony could thus be viewed as a collection of polar active particles with interactions between their positions – forces – and their polarities – akin to spin interactions. Yet, the actual validity of this view and its detailed features still remain elusive. In this talk, I will show how one can make use of micropatterned adhesive tracks to bridge the scales in that matter. Indeed, by following epithelial cells on such tracks we could characterise both the properties of collective motion and the cell-cell pair interactions. Including our observations into a lowest-order particle-based model allowed us to explain how local interactions that apparently favour disorder may not prevent large-scale order in particular situations. I will then discuss the possibilities offered by this set-up to a finer understanding of the cell-cell interactions.
Clathrin-mediated endocytosis (CME) consists of the formation of a vesicle out of a flat membrane in eukaryotes. When membrane tension and/or turgor pressure are high, actin dynamics is required to produce the forces required to invaginate the membrane and pinch it off into a vesicle. However, how the actin meshwork produces forces at the molecular level has remained elusive, because endocytic structures are intrinsically transient, out of equilibrium, and diffraction limited. In this seminar, I will present results from mathematical modeling and experiments in yeast demonstrating that actin polymerization alone cannot produce sufficient force to invaginate the plasma membrane. I will also present new force production mechanisms by the actin meshwork that are not exclusively based on polymerization, and are relevant to other subcellular processes involving actin and membranes.
A common cellular task is to match gene expression dynamically to a range of concentrations of a regulatory molecule. Studying glucose transport in budding yeast, we determine mechanistically how such matching occurs for seven hexose transporters. By combining time-lapse microscopy with mathematical modelling, we find that levels of transporters are history-dependent and are regulated by a push-pull system comprising two types of repressors. I will argue that matching is favoured by a rate-affinity trade-off and that the regulatory system allows yeast to import glucose rapidly enough to starve competitors.
During chemical navigation organisms must detect molecules, process that information, and make decision (e.g. turn or not to turn), which affects the signal they will encounter next. I will report on recent experiments in our lab that examine different aspects of this process. In the first part of the talk, I will discuss experiments that quantified the strategy used by walking fruit flies to navigate complex odor plumes, when the location and timing of odor packets are uncertain. In the second part of the talk, I will use the simpler and better characterized E. coli chemotaxis system to quantify how information puts a bound on maximal navigational performance, and how efficient a bacterium is at using the information it gathers in order to navigate.
Podosomes are macrophage adhesion structures devoted to the proteolysis of the extracellular matrix that are constitutively formed by monocyte/macrophage-derived cells. We have shown that they are crucial for the capability of macrophages to perform macrophage protease-dependent mesenchymal migration in vivo. Therefore, podosomes are emerging as specific targets to limit the deleterious macrophage infiltration in tumors. Podosomes are composed of a core of F-actin surrounded by adhesion complexes. We have shown that podosomes are capable of applying protrusive forces onto the extracellular environment, thanks to the development of a method called Protrusion Force Microscopy, which consists in measuring by Atomic Force Microscopy the nanometer deformations produced by macrophage podosomes on a compliant formvar membrane. We estimated the protrusive force generated at podosomes and showed that it oscillates with a constant period and requires combined acto-myosin contraction and actin polymerization. We have demonstrated that talin, vinculin and paxillin sustain protrusion force generated at the podosome core, and related force generation to the molecular extension of talin within the podosome ring, indicating that the ring sustains mechanical tension. We are now investigating the organization and regulation of actin filaments in podosomes and the precise localization of actin cross-linkers. Next to the demonstration that the ring is a site of tension balancing protrusion at the core, we are now determining how actin filaments in the core are collectively organized to generate podosome protrusive forces. Using in situ cryo-electron tomography, we have recently unveiled how the nanoscale architecture of macrophage podosomes enables basal membrane protrusion. In particular, we could show that the sum of the actin polymerization forces at the membrane is not sufficient to explain podosome protrusive forces, but that it can be rather explained by the elastic energy that is accumulated inside podosome actin filaments.
The spin glass is a paradigmatic example of a difficult optimization problem arising from simple pairwise interactions, and unsurprisingly recurs in many contexts. One such context is the study of evolution, where spin-glass-like models are extensively used to simulate the complex "fitness landscape" experienced by the organisms as they evolve and interact. I will describe a class of eco-evolutionary models focusing on the simplest case of the interaction between organisms and their environment, namely competition for limited resources. In this class of models, the glassy landscape acquires the meaning of specifying the (complex, idiosyncratic) biochemistry. Focusing on the ecosystem response to external perturbations, I will argue that the spin-glass intuition allows us to expect several parameter regimes with distinct behaviors. In particular, the intuitive regime ("what the community is doing depends on the species it contains") is flanked by two regimes where ecosystem response is predictable: one where this predictability emerges in spite of biochemical details, and another where it arises because of them.
Specific cell and tissue form is essential to support many biological functions of living organisms. During development, the creation of different shapes fundamentally requires the integration of genetic, biochemical and physical inputs.
In plants, it is well established that the cytoskeletal microtubule network plays a key role in the morphogenesis of the plant cell wall by guiding the organisation of new cell wall material. The cell cytoskeleton is thus a major determinant of plant cell shape. What is less clear is how cell geometry in turn influences the cytoskeletal organization.
To explore the relative contribution of geometry to the final organization of actin and microtubule cytoskeletons in single plant cells, we developed an experimental approach combining confinement of plant cells into micro-niches of controlled geometry with imaging of the cytoskeleton. A model of self-organizing microtubules in 3D predicts that severing of microtubules is an important parameter controlling the anisotropy of the microtubules network. We experimentally confirmed the model predictions by analysis of the response to shape change in plant cells with altered microtubule severing dynamics. This work is a first step towards assessing quantitatively how cell geometry controls cytoskeletal organization in plants.
Cells in growing tissues are continuously subjected to and exerting active and passive forces. In fact, growth rate variations or changes in the spatial orientation of growth produce stress. To release the produced stress, the balance between growth and cell division is fundamental. Here we investigate the consequences on cell morphology when this balance is not present. A perfect model system is Drosophila abdominal epidermis, a continuous cell layer formed of two cell types: larval epithelial cells (LECs), and adult epidermis precursors (histoblasts). Histoblasts are organized in nests surrounded by LECs. Interestingly, histoblasts grow without dividing throughout the whole larval life. At the same time, LECs grow at a faster rate than histoblasts. Such imbalance causes an amazing morphological change in histoblasts, with cell junctions changing from straight to deeply folded. Such transition is reminiscent of buckling instabilities. We hypothesize that growing LECs compress histoblasts, causing junctional buckling. Live imaging observations of larvae in which we genetically altered cell cycle or growth of either cell type support this idea. Hence, we show that altering the balance between cell growth and divisions leads to unexpected morphological and mechanical regimes.
Cell migration and cell mechanics play a crucial role in a number of key
biological processes, such as embryo development or cancer metastasis.
It is therefore important to characterise the material properties of
cells and tissues and the way they mechanically interact with their
In this talk, I will present recent work we did to address these questions at the single-cell and tissue level. In particular, experimental studies on the mechanical response of in-vitro epithelial monolayers show that the material exhibits a strong time-dependent response over a broad range of timescales. In this situation, it is challenging to capture the response of the system with a few parameters without losing some of the material’s characteristic features. I will show that rheological models based on fractional calculus are effective empirical tools to summarize such complex data and highlight similarities across a broad range of systems.
Left-right partitioning of the heart underlies the double blood circulation : pulmonary circulation in the right heart, systemic circulation in the left heart. Asymmetric heart morphogenesis is initiated in the embryo, when the tubular primordium acquires a rightward helical shape during the process of heart looping. This shape change determines cardiac chamber alignment and thus heart partitioning. Impairment of the left-right patterning of mesoderm precursor cells leads to the severe heterotaxy syndrome, including complex cardiac malformations and failure to establish the double blood circulation. Whereas the molecular cascade breaking the symmetry has been well characterised, how asymmetric signalling is sensed by precursor cells to generate asymmetric organogenesis has remained largely unknown.
Heart looping had been previously analysed as a binary parameter (left/right) of the helix direction, taken as a readout of the symmetry-breaking event. However, this is too reductionist to describe a 3D shape. We have developed a novel framework to quantify and simulate the fine heart loop shape in the mouse, as a readout of asymmetric morphogenesis. This has led us to propose a model of heart looping centred on the buckling of the tube growing between fixed poles. We have re-analysed the role of the major left determinant Nodal in this context. We have traced the contribution of Nodal expressing cells to regions of the heart tube poles. By manipulating Nodal signalling in time and space, we show that it is not involved in the buckling, but that it biases it. Nodal is required transiently in heart precursors, to amplify and coordinate opposed asymmetries at the heart tube poles and thus generate a robust helical shape.
Ongoing work aims at further dissecting the dynamics of left-right patterning, beyond Nodal signaling. Thus, we provide novel insight into the mechanisms of asymmetric heart morphogenesis relevant to complex congenital heart defects.
Microorganismal motility is often characterised by complex responses to environmental physico-chemical stimuli. Although the biological basis of these responses is often not well understood, their exploitation already promises novel avenues to directly control the motion of living active matter at both the individual and collective level. Here we leverage the phototactic ability of the model microalga Chlamydomonas reinhardtii to precisely control the timing and position of localised cell photo-accumulation, leading to the controlled development of isolated bioconvective plumes. This novel form of photo-bio-convection allows a precise, fast and reconfigurable control of the spatio-temporal dynamics of the instability and the ensuing global recirculation, which can be activated and stopped in real time. A simple continuum model accounts for the phototactic response of the suspension and demonstrates how the spatio-temporal dynamics of the illumination field can be used as a simple external switch to produce efficient bio-mixing.
The olfactory system senses chemicals in the environment to guide behavior in animals. Fluctuating mixtures of chemicals, transported in fluid environments, are detected by an array of olfactory sensors and parsed by neural circuits to recognize odor objects, which then inform behavioral decisions. Some key questions for chemical sensing systems include how they can detect relevant molecules that are embedded in a sea of distractors, and how they use sparse intermittent stimuli to navigate. We work with theorists to frame these questions quantitatively and use experiments in mice to address them. I will present some examples from our recent and ongoing work.
Ageing is a complex process, broadly affecting living organisms in
extremely various ways, ranging from the negligible senescence of some
trees and arthropods, through the sudden post-reproduction death of
salmon and desert organisms, to our human ageing with what has long been
described as a time dependent exponential increase of the mortality risk.
Drosophila melanogaster and Mus musculus, the fruit fly and mouse, are two broadly used model organisms for studying ageing mostly because they show an apparent exponential increase of their mortality risk, same as in humans. Using the first model system, about 10 years ago I identified a physiological marker preceding death - fruit flies would turn blue when fed a non-toxic food dye. This simple visual cue allows us to identify individuals at a different stage of their life amongst a cohort of individuals and study aging and progress towards death.
We use this tool to question our knowledge regarding ageing, showed the broad conservation of this end-of-life phenotype in different drosophila subspecies, nematodes, zebrafish and killifish as well as develop a novel mathematical model for ageing allowing the experimental quantification of various "ageing parameters".
The cerebrospinal fluid (CSF) is a complex solution circulating around the brain and spinal cord. Multiple evidence indicate that the activity and the development of the nervous system can be influenced by the content and flow of the CSF. Yet, it is not known how neuronal activity changes as a function of the physico-chemical properties of the CSF.
We identify throughout vertebrate species, ciliated neurons at the interface between the CSF and the nervous system that are in ideal position to sense CSF cues, to relay information to local networks and to regulate CSF content by secretion.
By combining electrophysiology, optogenetics and calcium imaging in vivo in larval zebrafish, we demonstrate that neurons contacting the CSF detect local bending of the spinal cord and in turn feedback GABAergic inhibition to multiple interneurons driving locomotion and posture in the spinal cord and hindbrain. Such inhibitory feedback modulates neuronal target in a state-dependent manner, depending on the fact that the animal is at rest or actively moving at a define speed.
Behavioral analysis of animals deprived of this sensory pathway reveals differential effects on speed for slow and fast regimes, as well as impairments in the control of posture during active locomotion. Our work first sheds light on the cellular and network mechanisms enabling sensorimotor integration of mechanical and chemical cues from the CSF onto motor circuits controlling locomotion and posture in the spinal cord.
We will present converging evidence that this interoceptive sensory pathway is involved in guiding a straight body axis throughout life, as well as innate immunity via the detection and combat of pathogens intruding the CSF.
Epithelial monolayers are soft thin sheets which shape the body and organs of many multi-cellular organisms. Competition between stretching and bending characterizes shape transitions of thin elastic sheets. While stretching dominates the mechanical response in tension, bending dominates in compression after an abrupt buckling transition. As opposed to inert materials, the morphogenesis of epithelial monolayers is largely influenced by endogenous ATP-dependent forces, which generate in-plane tension and active torques due to the polarization of myosin II molecular motors.
Here, we address the dialog between in-plane and out-of-plane forces in vitro, in epithelial monolayers devoid of substrate and suspended between parallel plates.
I will show that curls of high curvature form spontaneously at the free edge of these monolayers, which we use to estimate the active torques and the bending modulus of the tissue. I will also show that these tissues buckle in response to compression in a time-dependent and myosin II-dependent manner.
Body and caudal fin undulations are a widespread locomotion strategy in fish, and their swimming kinematics is usually described by a characteristic frequency and amplitude of the tail-beat oscillation. In some cases, fish use intermittent gaits, where a single frequency is not enough to fully describe their kinematics. Energy efficiency arguments have been invoked in the literature to explain this so-called burst-and-coast regime but well controlled experimental data are scarce. I will discuss our recent results on an experiment with burst-and-coast swimmers and a numerical model based on the observations showing that the observed burst-and-coast regime can be understood as obeying a
minimization of cost of transport.
Ref: Li et al. (2020) Burst-and-coast swimmers optimize gait by adapting unique intrinsic cycle arXiv:2002.09176
Behavior exhibits multiple spatio-temporal scales: from fast control of the body posture by neural activity, to the slower neuromodulation of exploratory strategies all the way to ageing. How can we bridge these scales? We leverage the interplay between microscopic variability and macroscopic order, fundamental to statistics physics, to extract predictive coarse-grained dynamics from data. We reconstruct the state space as a sequence of measurements, partition the resulting space as to maximize entropy, and choose the sequence length to maximize predictive information. We approximate the dynamics of densities in the partitioned state space through transfer operators, providing an accurate statistical model on multiple scales. The operator spectrum provides a principled means of timescale separation and coarse-graining. We illustrate our approach using high-resolution posture measurements of the nematode C. elegans, and show that long-time changes in exploratory strategies (10's of minutes) can be extracted from fine scale posture samples (10's of milliseconds).
Metabolism and evolution are closely connected: if a mutation incurs extra energetic costs for an organism, there is a baseline selective disadvantage that may or may not be compensated for by other adaptive effects. A long-standing, but to date unproven, hypothesis  is that this disadvantage is equal to the fractional cost relative to the total resting metabolic expenditure. I will present our recent work  which validates this hypothesis from physical principles through a general growth model and show that it holds to excellent approximation for experimental parameters drawn from a wide range of species.
We will also overview the significance of this contribution from metabolic expenditures in the course of evolution, by considering the elements of population dynamics. As an example, I will demonstrate that a close inspection on the thermodynamic costs, noise suppression performance and selection shows intriguing aspects about the evolution of microRNA regulated gene networks which play a critical role by controlling developmental processes of complex organisms and related diseases.
 L. E. Orgel and F. H. Crick, Nature 284, 604 (1980)
 E. Ilker and M. Hinczewski, Phys. Rev. Lett. 122, 238101 (2019)
T cells have to make life-or-death immune decisions based on sensitive and specific interactions with self and/or foreign peptides. On a longer time scale, T cells have to coordinate with one another to trigger a properly balanced immune response. Modeling this process is a daunting task because of the multiplicity of molecular and cellular interactions. I will show how phenotypic models can be built to describe those processes in a simple and predictive way. At the single cell level, we propose an « adaptive kinetic proofreading » model, detecting ligand strength irrespective of ligand concentrations. This model predicts experimental features such as ligand antagonism, which, interestingly, can be related to adversarial problems in artificial neural networks. At the cell population level, I will introduce a data driven approach to build phenomenological models of collective response, suggesting the existence of a simple cytokine code.
Persistent neural activities are ubiquitous in neural systems. This capacity of networks to continuously discharge in the absence of on-going stimuli is believed to subserve short-term memorisation and temporal integration of sensory signals. Although persistence may reflect cellular mechanisms, it can also be a network emergent property. Here we investigate this latter mechanism on larval zebrafish, a model vertebrate that is accessible to brain-scale neuronal recording and high-throughput behavioral studies.
We thus combine behavioral assays, functional imaging and network modeling to understand the dynamics and function of a small bilaterally distributed neural circuit (ARTR). ARTR exhibits slow antiphasic alternations between its left and right subpopulation. This oscillation drives the coordinated orientation of the eyes and swim bouts, thus organizing the fish spatial exploration. The left/right transition can be induced through transient illumination of one eye such as to orient the fish towards towards light sources (phototaxis). We also show that the self-oscillatory frequency can be modulated by the water temperature. To elucidate the mechanism leading to the slow self-oscillation, we train a network (Ising) model on the neural recordings. The model allows us to generate synthetic oscillatory activity, whose features correctly captures the observed dynamics. It provides a simple physical interpretation of the persistent process.
We exploit a theoretical relation between two statistics on lineages trees,
based either on forward lineages or on backward histories [1,2]. A fitness landscape
is introduced, which quantifies the correlations between a trait of interest and
the number of divisions. We derive various inequalities constraining the
fluctuations of a trait of interest or its fitness on lineage trees.
We apply this formalism to single-cell experiments with bacteria populations,
carried out either in the mother machine configuration or in free conditions
using time-lapse video-microscopy. We also investigate how the various sources
of stochasticity at the single cell level can affect the population growth rate.
 Linking lineage and population observables in biological branching processes, R. Garcia-Garcia, A. Genthon and D. Lacoste, Phys. Rev. E, 042413 (2019).
 Fluctuation relations and fitness landscapes of growing cell populations, Scientific Reports, 10, 11889 (2020).
Ants exhibit some of the most diverse and complex patterns of collective behavior in nature. However, the systematic study of these patterns and their computational significance has long been hindered by the lack of a lab model system, which would allow precise manipulations of the determinants of these emergent patterns. In this talk, I will present the potential of a specific ant species, the clonal raider ant Ooceraea biroi, to become such a model system. I will briefly describe the unique properties of the species and the opportunities they open for the understanding of collective behavior. I will then present results from two separate projects, studying different aspects of collective behavior. In the first, we study how ant colonies respond collectively to sensory input. We show that their response is characterized by an emergent threshold, which is sensitive to manipulations of colony properties. I will discuss the implications of this emergence for the understanding of how ants use interactions to reach collective decisions. In the second study, we analyze a more ecologically relevant behavior, the group raid, which is a swift offensive response of a colony to the detection of a potential prey by a scout. I will highlight the differences between this behavior and a behavior exhibited by related ant species, the army ants. Based on these analyses, we suggest that the emergent differences between the two behaviors can be explained by evolutionary changes in colony size alone.
The concept of the hematopoietic stem cell developed from the observation, reported in the 1950s, that the transplantation of bone marrow from adult mice rescues irradiated mice by regenerating their blood. Transplantation experiments have been the mainstay of hematopoiesis research until recently, when non-invasive genetic tools for tracing the progeny of hematopoietic stem cells were developed. Based on these tools, we and others found that post-transplantation recovery differs fundamentally from physiological hematopoiesis. Mathematical models of cell population dynamics, coupled with statistical inference, have been playing a key role in deriving quantitative insights on stem cell behavior from experimental data and in designing new experiments. I will discuss recent work on how the murine hematopoietic system develops in the fetus and functions in the adult. We find that the stem cells, while being highly productive during development, are remarkably unproductive in the adult, even during times of high demand for blood and immune cells. Cell population genetics suggest a function for idle hematopoietic stem cells.
The extensive heterogeneity of biological data poses challenges to analysis and interpretation. Construction of a large-scale mechanistic model of Escherichia coli enabled us to integrate and cross-evaluate a massive, heterogeneous dataset based on measurements reported by various labs over decades. We identified inconsistencies with functional consequences across the data, including: that the data describing total output of the ribosomes and RNA polymerases is not sufficient for a cell to reproduce measured doubling times; that measured metabolic parameters are neither fully compatible with each other nor with overall growth; that essential proteins are absent during the cell cycle - and the cell is robust to this absence. Finally, considering these data as a whole leads to successful predictions of new experimental outcomes, in this case protein half-lives.
Equilibrium statistical mechanics tells us how to control the self-assembly of passive materials by tuning the competition between energy and entropy to achieve desired states of organization. Out of equilibrium, no such principles apply and self-organization principles are scarce. In this talk I will review the progress which has been made over the past ten years to control the organization of self-propelled agents using motility control, either externally or through interactions. I will show that generic principles apply and illustrate the theoretical developments presented in the talk using recent experiments on run-and-tumble bacteria.
We study the origin and function of signaling oscillations in embryonic development. Oscillatory activities (period ~2 hours) of the Notch, Wnt and Fgf signaling pathway have been identified in mouse embryos and are linked to periodic mesoderm segmentation and the formation of pre-vertebrae, somites. Most strikingly, Notch signaling oscillations occur highly synchronized, yet phase-shifted, in cell ensembles, leading to spatio-temporal wave patterns sweeping through the embryo. I will discuss how we use general synchronisation principles based on entrainment/Arnold tongues to reveal general properties and function of collective oscillations during the mesoderm patterning process.
Active fluids consist of self-propelled particles (as bacteria or artificial microswimmers) and display properties that differ strongly from their passive counterparts. Unique physical phenomena, as enhanced Brownian diffusivity, viscosity reduction, active transport and mixing or the extraction of work from chaotic motion, result from the activity of the particles, locally injecting energy into the system. The presence of living and cooperative species may also induce collective motion and organization at the mesoscopic or macroscopic level impacting the constitutive relationships in the semi-dilute or dense regimes. Individual bacteria transported in viscous flows, show complex interactions with flows and bounding surfaces resulting from their complex shape as well as their activity. Understanding these transport dynamics is crucial, as they impact soil contamination, transport in biological conducts or catheters, and constitute thus a serious health thread. Here we investigate the trajectories of individual E-coli bacteria in confined geometries under flow, using microfluidic model systems in bulk flows as well as close to surfaces using a novel Langrangian 3D tracking method. Combining experimental observations and modelling we elucidate the origin of upstream swimming, lateral drift or persistent transport along corners. The understanding gained can be used to design channel geometries to guide bacteria towards specific locations or to prevent upstream contamination.
Spatial navigation constitutes an essential behavior that requires internal representations of environments and online memory processing to guide decisions. The precise integration of orientation and directions along trajectories critically determines the ability of animals to explore their surroundings efficiently. First, I will present recent results obtained in the fruit fly, Drosophila melanogaster. These results show how insects use an internal neural compass to store and compute the direction of cues present in their environments. Then, I will present the structure of the involved neural networks and the mechanisms at play during the processing of the information of direction. The results obtained in the fly mainly involve navigation in 2 dimensions, and thus the processing of a unique angular variable. However, a recent study in bats uncovered the existence of cells representing the orientation of bats in 3D. I will show possible mechanisms to extend the neural computation of directions to 3D rotations, a problem that presents much stronger theoretical challenges. I will propose a neural network model that displays activity patterns that continuously maps to the set of all the 3D rotations. Moreover, the general theory can account for psychophysics observations of "mental rotations.”
Coordinating collective behaviors across groups of cells is critical for a wide range of biological processes ranging from development to wound healing. How these basic group phenomena are regulated at the level of single cells, potentially by modulating factors like the frequency of synchronized signaling or the speed of group migration, is still an open question. Identifying what single cells tune in their own signaling programs to produce these phenotypic changes in multicellular population behaviors would yield parameters we can control when reprogramming these systems for our benefit. To address this challenge, we are pursuing complimentary efforts in multiple model systems where single-cell level and collective signaling can be simultaneously visualized with group behaviors. This talk will focus on these efforts in two systems: the social amoeba, Dictyostelium discoideum, and synthetic stromal tissues. We are interrogating signaling behaviors in these systems using a combination of techniques to visualize and control cellular signaling, and developing quantitative models to understand how the signaling behaviors we observe drive multicellular behaviors. Through directly controlling signaling, we can causally link our observations of single-cell signaling dynamics to the population-wide behaviors they control. Together, these efforts will allow us to identify how population-wide multicellular behaviors are regulated by single cells.
One of the most striking features of embryonic development is that differentiation is happening in a spatially ordered fashion: tissues self-organize to form well-defined patterns that pre-figure the body plan. During gastrulation, the cells of the embryo are allocated into three germ layers: ectoderm, mesoderm and endoderm. During the last decades, signaling pathways responsible for the initiation of gastrulation in mammalian embryos have been identified. However, the physical rules governing the tissue spatial patterning and the extensive morphogenetic movements occurring during that process are still elusive. Studying the spatio-temporal dynamics of pattern formation is difficult in live embryos, because of their inherent lack of observability but also because it is not possible in an embryo to control in a quantitative manner what is relevant for the establishment of the multi-cellular pattern, i.e. the cells' physical and chemical environment. I will discuss how culture and differentiation of mouse ESC on micro-patterned substrates allowed us to recapitulate some aspects of Antero/Posterior axis formation occurring at gastrulation and how microfluidic devices can help us dissect the emergence of A/P polarity.
The actin cytoskeleton assembles into very dynamic structures that generate various forces. In this active process, filament disassembly must be tightly regulated, either to maintain active units, or to discard excess filaments and replenish the pool of monomeric (G) actin. At the centre of all actin filaments disassembly machineries is the family of proteins ADF/cofilin. ADF/cofilin binds along filaments into domains, induces severing and regulates depolymerisation from filament ends. We performed in vitro experiments, in microfluidic chambers, with purified proteins, to directly observe and quantify interactions between single actin filaments and ADF/cofilin. We recently developed new methods to apply tension, curvature and torsion to filaments, and thus understand how mechanical constraints regulate ADF/cofilin activity. First, we discovered that filament curvature and torsion boost ADF/cofilin-mediated severing. Moreover, ADF/cofilin, by increasing the helicity of actin filaments, generates a torque on twist-constrained filaments that accelerates severing up to 100-fold. These findings highlight the deep connections between mechanical forces and biochemical reactions, and their importance for cell behaviour regulation.
Single-molecule techniques continue to transform imaging, biophysics and, more recently, optical sensing. I will introduce a new class of label-free micro and nanosensors that are starting to emerge and that allow us to observe dynamic processes at the single molecule level directly with light, with unprecedented spatial- and temporal resolution, and without significantly affecting the natural and functional movements of the molecules. Initial demonstration include single ion sensing, and visualisation of functional movements of enzymes directly with light.
Multi-drug resistant bacterial pathogens constitute a critical public health threat. This threat has spurred a multidisciplinary response to develop antibiotic alternatives, including the use of bacteriophage (phage), i.e., viruses that exclusively infect and lyse bacteria. Phage-induced lysis eliminates bacterial cells. However, the death of individuals cells need not translate into the elimination of a target population. Instead, lysis can lead to the depletion of bacterial hosts which, in turn, leads to decreased effectiveness of phage therapy and the evolution of phage-resistant hosts. As a consequence, phage are unlikely to eliminate a target population on their own and may even be eliminated altogether. However, a central difference between in vitro dynamics and in vivo therapy is the influence of the mammalian immune system. In this talk, I present collaborative efforts to address this gap via a dynamical systems approach to phage therapy in an immune system context. In doing so, I highlight how the combined use of mathematical models, in vitro manipulation, and in vivo experiments may shed light on principles underlying curative treatment of acute infections.
Mechanics are ubiquitous in the environments of bacteria, but we are only starting to appreciate how they may affect their physiology. Here, I will discuss how the human pathogen Pseudomonas aeruginosa uses a mechano-chemical sensory system to induce virulence upon mechanical stimulation during surface contact. I will show that successive type IV pili extension, attachment and retraction represents a mechanical input readout by a chemotaxis-like system (Chp). Using iSCAT, a microscopy method based on interference and scattering enabling label-free visualization of small extracellular structures in live cells, we were able to measure type IV pili dynamics during surface exploration, thereby providing a direct measurement of mechanical input. I will discuss how we are using these measurements to understand how type IV pili interacts with the Chp system to activate cellular response. This expands the range of known inputs that bacteria sense, providing a new class of signals that can be processed by two component systems. It furthermore argues that mechanics may play a role in the physiology of many other species.
One of the most critical aspects of cell functioning is the ability of protein molecules to quickly find and recognize specific target sequences on DNA. Kinetic measurements indicate that in many cases the corresponding association rates are surprisingly large. For some proteins they might be even larger than maximal allowed 3D diffusion rates, and these observations stimulated strong debates about possible mechanisms. Current experimental and theoretical studies suggest that the search process is a complex combination of 3D and 1D motions. Although significant progress in understanding protein search and recognition of targets on DNA has been achieved, detailed mechanisms of these processes are still not well understood. The most surprising observation is that proteins spend most of the search time being non-specifically bound on DNA where they supposedly move very slowly, but still the overall search is very fast. Another intriguing result is known as a speed-selectivity paradox. It suggests that experimentally observed fast findings of targets require smooth protein-DNA binding potentials, while the stability of the specific protein-DNA complex imposes a large energy gap which should significantly slow down the protein molecule. Here we discuss a theoretical picture that might explain fast protein search for targets on DNA. We developed a discrete-state stochastic framework that allowed us to investigate explicitly target search phenomena. Using exact calculations by analyzing first passage distributions, it is shown that strong coupling between 3D and 1D motion might accelerate the search. It is argued that the speed-selectivity paradox does not exist since it is an artifact of the continuum approximation. We also show how our method can be utilized for taking into account the inter-segment processes. This is important to explain large deviations from the diffusion limit. Our theoretical analysis is supported by Monte Carlo computer simulations, and it agrees with all available experimental observations. Physical-chemical aspects of the mechanism are also discussed.
The two hands of most humans almost superimpose. Similarly, flowers of an individual plant have almost identical shapes and sizes. This robustness is in striking contrast with growth and deformation of cells during organ morphogenesis, which feature considerable variations in space and in time, raising the question of how organs and organisms reach well-defined size and shape. Because heterogeneous growth induces mechanical stress in tissues, we are exploring the biophysical basis of morphogenesis. By combining approaches from developmental biology (molecular genetics, live imaging), and mechanics/physics (mechanical measurements, models), we are unravelling unexpected cellular patterns and behaviours. During this talk, I will discuss some of our recent results and the resulting perspectives, aiming at a general audience.
Range expansions coupled with fluid flows are of great importance in understanding the organization and competition of microorganism populations in liquid environments. However, combining growth dynamics of an expanding assembly of cells with hydrodynamics leads to challenging problems, involving the coupling of nonlinear dynamics, stochasticity and transport. We have created an extremely viscous medium that allows us to grow cells on a controlled liquid interface over macroscopic scales. In this talk, I will present laboratory experiments, combined with numerical modelling, focused on the collective dynamics of genetically labelled microorganisms undergoing division and competition in the presence of a flow. I will show that an expanding population of microbes can itself generate a flow, leading to an accelerated propagation and fragmentation of the initial colony. Finally, I will show the mechanism at the origin of this metabolically generated flow and how it affects the growth and morphology of these microbial populations.
Proteins are very heterogeneous objects: they are sensitive to perturbations at some sites distant from their active site while being insensitive to perturbations at closer sites. I’ll review the evidence for the ubiquity of these long range effects and discuss our current understanding of their physical nature and evolutionary origin. This will motivate the introduction of a new theoretical model where long-range effects emerge spontaneously. I’ll explain how this model accounts for the evolution of long-range regulation (allostery) and for the different patterns of coevolution that may be inferred from protein sequences.
The emergent active behaviors of systems comprising large numbers of molecular motors and cytoskeletal filaments remain poorly understood, even though individual molecules have been extensively characterized. Here, we show in vitro with a minimal acto-myosin system that flagellar-like beating emerges naturally and robustly in polar bundles of filaments. Using surface micro-patterns of a nucleation-promoting factor, we controlled the geometry of actin polymerization to produce thin networks of parallel actin filaments. With either myosin Va or heavy-mero myosin II motors added in bulk, growing actin filaments self-organized into bundles that displayed periodic wave-like beating resembling those observed in eukaryotic cilia and flagella. We studied how varying the motor type or changing the size of the actin bundles influenced the properties of the actin-bending waves. In addition, using myosin-Va-GFP to visualize the motors within the actin bundle, we identified a novel feedback mechanism between motor activity and filament bending. Overall, structural control over the self-assembly process provides key information to clarify the physical principles underlying flagellar-like beating.
Nuclear Pore Complex (NPC) is a biomolecular “nanomachine” that controls nucleocytoplasmic transport in eukaryotic cells, and is operation is central for a multitude of health and disease processes in the cell. The key component of the functional architecture of the NPC is the assembly of the polymer-like intrinsically disordered proteins that line its passageway and play a central role in the NPC transport mechanism. Due to the unstructured nature of the proteins in the NPC passageway, it does not possess a molecular “gate” that transitions from an open to a closed state during translocation of individual cargoes. Rather, its passageway is crowded with multiple transport proteins carrying different cargoes in both directions. It remains unclear how the NPC maintains selective and efficient bi-directional transport under such crowded conditions. Remarkably, although the molecular conservation of the NPC components is low, its physical transport mechanism appears to be universal across eukaryotes – from yeast to humans. Due to the paucity of experimental methods capable to directly probe the internal morphology and the dynamics of NPCs, much of our knowledge about its properties derives from in vitro experiments interpreted through theoretical and computational modeling. I will present the current understanding of the Nuclear Pore Complex structure and function arising from the analysis of in vitro and in vivo experimental data in light of minimal complexity models relying on the statistical physics of molecular assemblies on the nanoscale.
The actin cytoskeleton is able to exert both pushing and pulling forces on the cell membrane, mediating processes such as cellular motility, endocytosis and cytokinesis. In order to investigate the exclusive role of actin dynamics on membrane deformations, the actin dynamics is reconstituted on the outer surface of a deformable liposome. Depending on the elasticity of the membrane and the forces generated by the actin polymerization, both tubular extrusions (i.e. towards the actin cortex) and localized spike-like protrusions occur along the surface of the liposome. In this talk I present a theoretical model where uniform actin polymerization can drive localized membrane deformations and show how polymerization kinetics and membrane/cortex mechanics impact their size and stability.
The coordinated flight of bird flocks is a striking example of collective behavior in biology. Using 3D positions and velocities of large natural flocks of starlings, I will show how to build an explicit mapping of flock behaviour onto statistical physics models of magnetism. Learning the parameters of these models allows us to infer the local rules of alignment, and to reveal that flocks are poised close to a critical point, where susceptibility to external perturbations is maximal. Extending the approach to alignment dynamics shows that flocks are in a state of local quasi-equilibrium.
Developing tissues have the capacity to cope with perturbations, including the modification of cell growth rate and the elimination of a large number of cells through wounding. This plasticity is well illustrated by cell competition, a process that drives the elimination of suboptimal cells (so called “losers”) by fitter cells (so called “winners”) through apoptosis without morphological defects. In the past years, the number of genetic and tissular contexts leading to cell competition has been constantly increasing. Yet, the mechanisms allowing recognition and elimination of suboptimal cells is still actively debated. I will present here our attempt to characterize quantitatively the process of cell competition which led to the characterization of two independent modes of elimination: first a contact dependent elimination which can be modulated by the shape of the interfaces between the two populations, secondly a compaction-driven competition where cell elimination is triggered by mechanical stress and differential sensitivity to compaction. I will then present recent results regarding the characterization of the pathway sensing cell compaction and triggering cell elimination in the Drosophila pupal notum (a single layer epithelium). Eventually I will present our ongoing characterization of the process of cell extrusion (the concerted removal of one cell from the epithelial layer) and its regulation by caspases.
In a similar way to bacteria that have to navigate in their environment, soaring birds try to minimize their effort by finding and exploiting ascending currents. However the environment is highly turbulent and unpredictable with thermals constantly forming, disintegrating and being transported within minutes timescales. How the birds navigate these environments remains unknown. It is a notoriously difficult/impossible task to assess what cues are used by the soaring birds and what strategies they developed to explore and exploit such turbulent environments. For this investigation, we chose to emulate how an agent could learn to see what strategies would emerge. I then set up an experimental reinforcement learning framework that I implemented in a two-meter wingspan glider. Here, the soaring agent measured its state (height and a set of cues), took actions (change the left/right direction it is heading to) and recorded what resulted from these actions given the previous state. After an initial learning period, the glider chose the actions according to their state that would maximize the gain in altitude. In short, the glider learned to soar through its past experience. The learned strategy was based on accurate estimates of local wind accelerations and roll-wise torque. In the field, the glider could typically gain hundreds of meters in height in a few minutes when facing environments with thermals. Our results not only highlight the vertical wind accelerations and roll-wise torques as effective mechanosensory cues for soaring birds but also provide navigational strategy that is directly applicable to the development of autonomous soaring vehicles to increase their time aloft with minimal energy cost.
Fertilization is one of the fundamental processes of living systems. Here I will address marine external fertilization and comment on recent work on mammals. I will show experiments that substantiate that sea urchin sperms exhibit chemotaxis as they swim towards the ovum. They are guided by flagellum internal [Ca2+] concentration fluctuations triggered by the binding of chemicals from the oocyte surroundings. Based on experiment, I present a family of logical regulatory networks for the [Ca2+] fluctuation signaling-pathway that reproduce previously observed electrophysiological behaviors and provide predictions, which have been confirmed with new experiments. These studies give insight on the operation of drugs that control sperm navigation. In this systems biology approach, global properties of the [Ca2+] discrete regulatory network dynamics such as: stability, redundancy, degeneracy, chaoticity and criticality can be determined. Our models operate near a critical dynamical regime, where robustness and evolvablity coexist. This regime is preserved under a class of strong perturbations. Based on global dynamics considerations, we have implemented a network node-reduction method. The coincidence of this reduced network with our bottom-up step-by-step, continuous differential equation modeling is reassuring. For the case of mammals our research has centered on the understanding of capacitation and acrosomal reaction. The first is a process by means of which roughly one third of the spermatozoa acquire the “capacity” to fertilize; the second enables the spermatozoa to penetrate the egg´s surrounding zona pellucida. Overall, our studies might contribute to fertility issues such as the development of male contraception treatments, which is an area of intense research.
The development of most metazoans can be divided in an early phase of embryogenesis and a subsequent phase of post-embryonic development. Developmental dynamics during the post-embryonic phase are generally much slower and often controlled by very different molecular mechanisms that, e.g., ensure tissue synchrony and integrate metabolic queues. However, obtaining long-term in-vivo quantitative imaging data post-embryonically with good statistical and cellular resolution has been highly challenging because animals must be allowed to grow, feed, and move in order to properly develop after embryogenesis. In this talk, I will discuss our recent progress in overcoming these challenges in the model organism C. elegans, using microfluidics technology. I will then outline two of our studies, in which quantitative in-vivo imaging data of post-embryonic development allows novel insights into mechanisms of cell-fate acquisition and the regulation of oscillatory gene expression in C. elegans.
Early embryogenesis of most metazoans is characterized by rapid and synchronous cleavage divisions. After fertilization, Drosophila embryos undergo 13 swift rounds of DNA replication and mitosis without cytokinesis, resulting in a multinucleated syncytium containing about 6,000 nuclei. The very first cycles involve substantial flows, both in the bulk and at the cortex of the syncytial embryo. Waves of activity of Cdk1, the main regulator of the cell cycle, are observed in late cycles. I shall discuss the corresponding experimental data and models that capture those dynamics.
Microtubules are dynamic polymers that are used for intracellular transport and chromosome segregation during cell division. Their unique property to grow and shrink at steady state conditions stems from the low energy of dimer interactions, which sets the growing polymer close to its disassembly conditions. Microtubules function in coordination with molecular motors, such as kinesins and dyneins, which use ATP hydrolysis to produce mechanical work and move on microtubules. This raises the possibility that the forces produced by walking motors can break dimer interactions and trigger microtubule disassembly. We tested this hypothesis by studying the interplay between non-stabilized microtubules and moving molecular motors in vitro. Our results show that the mechanical work of molecular motors is able to remove tubulin dimers from the lattice and rapidly destroys microtubules. This effect was not observed when free tubulin dimers were present in the assay. Using fluorescently labelled tubulin dimers we found that motor motion fosters the insertion of free tubulin dimers into the microtubule lattice. This self-repair mechanism allows microtubules to survive the damages induced by molecular motors as they move along their tracks. Thus, our study reveals the existence of a coupling between the motion of molecular motors and the renewal of microtubules lattice.
In order to guide spatial behaviour the brain has the complex task of keeping track of movement through space, which it accomplishes by integrating information about learned environmental features together with information about movements, both linear and angular. The system that performs this integration contains several canonical cell types including place cells, head direction cells and grid cells. How these neurons achieve this environment/movement integration in two-dimensional space is relatively well understood, but movement in more than two dimensions introduces additional problems such as non-commutativity and anholonomy. This talk will review these problems, and discuss emerging evidence that the brain deals with the resulting complexity by a process of dimension reduction - that is, by reducing the problem to the lowest number of dimensions that will suffice to solve a given task. Not only does this allow for efficient processing of three-dimensional space, it might even be possible, at least in humans, that the brain could learn to apprehend four-dimensional space in this way.
The nuclear pore complex is the unique gateway between the nucleus and the cytoplasm of the cells. It ensures both directional and selective transport of nucleic acids and proteins. Its detailed mechanism is still highly debated. We study its dynamic through two complementary approaches. In a bottom-up approach we use hydrophobic polymer grafted nanopores that mimic the crowding of the native pore. We show using a near field optics (ZMW, ) that we can measure the free energy of translocation and reproduce some of the selectivity and directionality features of the nuclear pore. In a top-down approach we extract nuclear membranes from Xenopus Laevis. Our results obtained using ZMW and optical super-resolution (dSTORM) indicates that the nuclear pore has a plastic architecture. Its large scale organization and its internal structure are strongly influenced by transporters molecular crowding, developmental stage and transcriptional activity .  Zero-mode waveguide detection of flow-driven DNA translocation through nanopores. Auger T, Mathé J, Viasnoff V, Charron G, Di Meglio JM, Auvray L, Montel F. Phys Rev Lett. 2014 Jul 11;113(2):028302.  Nuclear pore complex plasticity during developmental process as revealed by super-resolution microscopy. Sci Rep. 2017 Nov 7;7(1):14732. Sellés J, Penrad-Mobayed M, Guillaume C, Fuger A, Auvray L, Faklaris O, Montel F.
Embryo morphogenesis relies on highly coordinated movements of different tissues as well as cell differentiation and patterning. However, remarkably little is known about how tissues coordinate their movements to shape the embryo and whether and how dynamic changes in signalling and tissue rheology affect tissue morphogenesis. In zebrafish embryogenesis, coordinated tissue movements first become apparent during "doming," when the blastoderm begins to spread over the yolk sac, a process involving coordinated epithelial surface cell layer expansion and deep cell intercalations. In this talk, I will first present how using a combination of active-gel theory and experiments (performed by Dr. Hitoshi Morita, Yamanashi University, Japan) shows that active surface cell expansion represents the key process coordinating tissue movements during doming. I will then talk about the analysis of the intrinsic mechanical properties of the blastoderm at the onset of doming and how, by the aid of a simpler toy model and experiments (performed by Dr. Nicoletta Petridou, IST Austria), blastoderm movement relies on a rapid, pronounced and spatially patterned tissue fluidisation which is found to be linked to local activation of non-canonical Wnt signalling mediating cell cohesion.
Fitness landscapes have moved from theoretical constructions to observable data in last decades, while several experimental fitness landscapes were partially or completely resolved. We have developped few metrics to get a better sense on what are these highly dimensionnal objects. We are now trying to infer what class of models can generate the observed experimental fitness landscapes: clearly none of the ones that were proposed so far. We however found some clues on what to search and where to garden in these landscapes.
The overdamped Langevin equation describes the inertialess motion of a particle under deterministic drift and thermal noise. The deterministic drift is the result of the combined action of active forces and the diffusivity gradient (the “spurious” force). For biological applications, it is important to distinguish between the two components, because the former indicates specific interactions, while the latter is due to a heterogeneous environment, in which these interactions take place. The spurious force is always proportional to the diffusivity gradient, but the proportionality coefficient is only known for equilibrium systems. This leads to a range of possible spurious force values in out-of-equilibrium systems and leads to ambiguity in the interpretation of the observed drift. This ambiguity is known as the Itô-Stratonovich dilemma. In this work, we do not try to resolve the dilemma, but analyze the information that can be extracted about the active forces in an a priori unknown out-of-equilibrium system. To this end, we propose a Bayesian method that marginalizes over all possible values of the spurious force and allows robust identification of active forces in both equilibrium and out-of-equilibrium setups. Under certain assumptions, the main result can be obtained in a closed form. The method has a significantly decreased false positive rate of active force detection as compared to conventional approaches. We illustrate the practical value of the method by integrating it into the open-source software project TRamWAy and applying it to both numerical trajectories and experimental single-biomolecule tracks (HIV capsids assembly) recorded on the cell membrane.
Spatially growing populations are ubiquitous across scales, ranging from the human migration out of Africa to the spreading of diseases. In contrast to well-mixed populations where an individual’s chance to survival is only determined by its fitness, in spatially growing populations the physical location of an individual is determinant for its survival: the individuals at the edge of the expanding front benefit from having access to virgin territory and giving their offspring the same advantage. The emerging population dynamics results in an evolutionary dynamics dominated by noise, with extreme consequences such as the accumulation of deleterious mutations at the front of the population. To explore experimentally how spatial constraints affect evolutionary dynamics, we employ bacteriophage T7, an E. coli virus that allows to cover many generations in short periods of times while controlling the underlying resource constraints, i.e., the bacterial host. In an evolutionary experiment lasting only 7 days, we were able to evolve a T7 strain that more than doubled its spreading speed on a bacterial lawn compared to its ancestor. The results from the experiments pointed out specific properties that are under strong selection in viral expansions and uncovered new remarkable properties of phage spreading dynamics that we believe are shared across viruses and pathogens in general.
Understanding how groups of species diversified, and how species phenotypes evolved during evolutionary history, is key to our understanding patterns of biodiversity as we see them around us today. Phylogenetic comparative methods have been developed to study diversification and phenotypic evolution from present-day data. I will present recent developments that allow testing the effect of past environmental changes on rates of speciation, extinction and phenotypic evolution, as well as models that allow testing the role of species interactions -- such as competitive, mutualistic, and antagonistic interactions – on phenotypic evolution. Empirical applications of these new phylogenetic comparative methods to large empirical datasets demonstrate the pervasive effect of past environmental changes on evolutionary rates across diverse clades spanning micro and macroorganims. They also demonstrate a distinct effect of interspecific competition in traits involved in resource use versus social signaling. Past environmental changes and species interactions have been key in driving the heterogeneity of evolution rates repeatedly observed across the tree of life.
The migration of immune cells is guided by specific chemical signals, such as chemokine gradients. Their trajectories can also be diverted by physical cues and obstacles imposed by the cellular environment, such as topography, rigidity, adhesion, or hydraulic resistance. On the example of hydraulic resistance, it was shown that neutrophils preferentially follow paths of least resistance, a phenomenon referred to as barotaxis. We here combined quantitative imaging and physical modeling to show that barotaxis results from a force imbalance at the scale of the cell, which is amplified by the actomyosin intrinsic polarization capacity. Strikingly, we found that macropinocytosis specifically confers to immature dendritic cells a unique capacity to overcome this physical bias by facilitating external fluid transport across the cell, thereby enhancing their space exploration capacity and promoting their tissue-patrolling function both in silicoand in vivo. Conversely, mature dendritic cells, which down-regulate macropinocytosis, were found to be sensitive to hydraulic resistance. Theoretical modeling suggested that barotaxis, which helps them avoid dead-ends, might accelerate their migration to lymph nodes, where they initiate adaptive immune responses. We conclude that the physical properties of the microenvironment of moving cells can introduce biases in their migratory behaviors but that specific active mechanisms such as macropinocytosis have emerged to diminish the influence of these biases, allowing motile cells to reach their final destination and efficiently fulfill their functions.
My colleague Sunil Laxman has observed the spontaneous emergence of subpopulations of cells in different metabolic states in growing populations of budding yeast. I'll talk about two situations - one where the yeast is in a well-mixed chemostat, and the other where it grows on agar plates. The chemostat produces incredibly regular oscillations between quiescence and proliferation which can sustain for days. I'll spend most time in the talk discussing a simple mathematical model that we think captures many aspects of this oscillation. The model helped us deduce that the the key metabolites triggering the switch from quiescence to proliferation are probably Acetyl-CoA and NADPH. If there is time, I'll also discuss the results of experiments and modelling of the spatial colonies where cells in two different metabolic states self-organize into a complex intermingled spatial pattern, with one state dependent on the other for metabolic raw material.
Our understanding of bacterial cell size control is based mainly on stress-free growth conditions in the laboratory. In the real-world however, bacteria are routinely faced with stresses that produce long filamentous cell morphologies. E. coli is observed to filament in response to DNA damage, antibiotic treatment, host immune systems, temperature, starvation, and more; conditions which are relevant to clinical settings and food preservation. This shape plasticity is considered a survival strategy. Size control in this regime remains largely unexplored. Here we report that E. coli cells use a dynamic size ruler to determine division locations combined with an adder-like mechanism to trigger divisions. As filamentous cells increase in size due to growth, or decrease in size due to divisions, its multiple Fts division rings abruptly reorganize to remain one characteristic cell length away from the cell pole, and two such length units away from each other. These rules can be explained by spatio-temporal oscillations of Min proteins. Upon removal of filamentation stress, the cells undergo a sequence of division events, randomly at one of the possible division sites, on average after the time required to grow one characteristic cell size. These results indicate that E. coli cells continuously keep track of absolute length to control size, suggest a wider relevance for the adder principle beyond the control of normally sized cells, and provide a new perspective on the function of the Fts and Min systems.
Older seminars can be found here.