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Paris Biological Physics
Community Day 2021

Friday 22nd October 2021
ENS @ 9am
Salle Jaurés - 29 rue d'Ulm, (entrance 24 rue Lhomond) 75005 Paris

Biophysics, Pizza and Beers!

The Paris Biological Physics Community Day (PBPCD 2021) is a conference organized by young researchers of biological physics in the Paris area. We aim to bring together enthusiastic researchers in biophysics in the Paris area to create an opportunity for meeting and sharing knowledge.

The meeting is intended for researchers working in diverse areas of biophysics.
It is going to be a day of conviviality and scientific enthusiasm, we envision to have a dynamic and informal atmosphere. In the program the talks of the invited speakers are interleaved with short presentations by young investigators.

No fees: lunch, coffee breaks and closing cocktail included!! Just come at the Salle Jaurés, ENS.

For any questions, contact us on social networks: Facebook Twitter

If you want to attend the meeting (and) submit and abstract please register here.

Keynote Speakers

  • Anne-Florence Bitbol EPFL, Lausanne
  • Jérémie Barral Institut de l'Audition, Paris
  • Serena Ding Max Planck Institute of Animal Behaviour, Konstanz
  • Pierre Ronceray Turing Centre for Living Systems, Marseille


8h30 - 9h15 Welcome coffee and viennoiserie
9h30 - 11h
  • Anne-Florence Bitbol

    Impact of population spatial structure on mutant fixation probabilities, from models on graphs to the gut

    Microbial populations often have complex spatial structures, with homogeneous competition holding only at a local scale. Population structure can strongly impact evolution, in particular by affecting the fixation probability of mutants. I will first discuss a general model for describing structured populations on graphs. Then I will show that the specific structure of the gut with gradients of food and bacterial concentrations can increase the fixation probability of neutral mutants, which can have consequences for the diversity of the microbiota.

  • Matthias Le Bec

    Spatio-temporal control of cooperation in yeast communities

    The division of labor is the separation of a system into different parts specialized in one or multiple tasks. This concept is observed in social economics, in multicellular organism or ecosystems, and especially in microbial communities where the complementarity of different species can result in a more productive and robust consortium. Even among clonal colonies, nutrient uptake, inhibitory chemicals excretion or chemical communication considerably affect the individual microenvironment leading to cell-to-cell phenotypic differentiation. While recent works make effort toward designing rational microbial communities, there is however few established methodologies to control, especially spatially, these microbial associations. Here, we aim at studying the role of spatial patterning of cooperation in a microbial population. We chose to control the degree of cooperation of Saccharomyces cerevisiae cells in a monoclonal population. For this we use the invertase enzyme (SUC2) as a mean for the optogenetic control of public goods production. This enzyme, embedded in the cell wall, allows yeast to grow on sucrose by catalyzing its hydrolysis into usable glucose and fructose, that can diffuse and be shared between cells. We built an optogenetic strain producing Suc2 enzyme upon blue illumination and optimized its induction range. We developed a method to perform simultaneous light patterning and time-lapse recording of yeast growing on agar-plate. We can thus control quantitatively in space and time the production of the Suc2 enzyme, hence the spatial pattern of cooperation in the microbial community. We assessed the influence of the pattern on the global growth behavior and compared our results with a diffusion-reaction model. Our first results outline the fact that our system acts as a spatial low-pass filter with a typical cooperation distance between illuminated and non-illuminated cells, evidencing the importance of microbial species patterning for ecosystem dynamics. Our project provides new insights in the spatial organization of microbial communities by developing new tools with enhanced control over biological parameters, toward a better understanding of microecology.

  • Andrea Mazzolini

    Universality of evolutionary dynamics with arbitrary demography

    The assumption of constant population size is central in population genetics. It led to a large body of results, that are robust to modeling choices and that have proven successful to understand evolutionary dynamics. In reality, allele frequencies and population size are both determined by the interaction between a population and the environment. Including explicitly the demographic factors and life-history traits that determine the eco-evolutionary dynamics makes the analysis difficult and the results contingent on model details. Here, we develop a framework that encompasses a great variety of systems with arbitrary population dynamics and competition between species. By using techniques based on scale separation for stochastic processes, we are able to calculate analytically evolutionary properties, such as the fixation probability. Remarkably, these properties assume a universal form with respect to our framework, which depends on only three life-history traits related to the inter-generation timescale, the invasion fitness, and the carrying capacity of the alleles. In other words, different systems, such as Lotka-Volterra or a chemostat model, share the same evolutionary outcomes after mapping the parameters of the models into three effective life-history traits. An important and surprising consequence of our results is that the direction of selection can be inverted, with a population evolving to reach lower values of invasion fitness.

  • Matteo Bisardi

    Data-driven modeling of experimental protein evolution: first (Monte Carlo) steps

    Proteins are the most advanced molecular machines we know of. Life has solved the problem of designing them with an evolutionary approach that relies on mutation and selection. Experimental protein evolution harnesses this idea as an engineering tool as well as a simplified setting for the study of natural evolution. Thanks to the explosion of protein sequence data available, enabled by next generation sequencing, machine learning methods can be exploited to computationally model and potentially optimize those experiments. I discuss here an approach based on data-driven Potts models of protein sequence landscapes inspired by statistical physics. Starting from some wild-type protein sequence, and its corresponding DNA sequence, we sample from the model to simulate the interplay of random mutation and phenotypic selection. We apply this method to study two recently published experiments. We demonstrate the quantitative character of our approach in predicting important evolutionary signatures like fitness distributions, position-specific mutational profiles or emergence of epistasis. Our modeling framework offers also a quantitative explanation for the different outcomes of such experiments. Finally, I show how this approach can be complemented by experiments in a feedback scenario to proposed optimized protocols.

11h00 - 11h30 Coffee Break
11h30 - 13h00
  • Jérémie Barral

    Synaptic scaling to maintain neuronal dynamics and propagate information

    There are many seminal theories on the conditions for propagating information faithfully while maintaining stability in neuronal networks. Testing these theories is difficult as it requires accurate information about network architecture and precise control of the input. To overcome these limitations, we used cultures of cortical neurons and stimulated the network using a novel optogenetic stimulation technique. Theories suggest that the appropriate scaling of synaptic strengths with network size is crucial to preserve stability and generate in-vivo like dynamics. In a first study, we showed that synaptic strength varies nearly as 1/﹟K (K = number of connections), close to the ideal theoretical value. This result is important because this scaling law was assumed by major theoretical models but lacked any experimental demonstration. Using optogenetics, we delivered high spatiotemporal resolution stimuli to neurons within the network to show that the neuronal dynamics predicted by theory hold even under conditions far from the ideal limits. These results obtained in cultures, predicted by theory and observed in the brain suggest that the synaptic scaling rule and resultant dynamics are emergent properties of networks in general. Information contained in neuronal activity can be represented as the neuronal firing rate or as the precise timing of action potentials. To study what information is propagated in networks composed of several connected layers, we combined microfabrication techniques with neuronal culture to build multilayer networks in vitro. We optogenetically activated neurons from the first layer and identified the conditions to transmit the input. Brief stimuli with different temporal precisions resulted in a firing rate modulation. Whereas temporal information was better preserved in low density networks, dense networks could propagate firing rate. These results indicate that both temporal and rate information can be propagated in multilayer networks, depending not only on the input but also on network architecture and cell density.

  • Mehmet Can Ucar

    Theoretical aspects of branching morphogenesis by local interactions and global guidance

    The development of branched, tree-like biological structures such as lung, kidney, or the neurovascular system has been a pivotal question in biology, physics and mathematics. Recently, many studies based on combinatorial, mechanical, or stochastic models explored local, self-organizing rules leading to branched morphologies in specific systems. However, in addition to local interactions, the growth of branched structures is also regulated globally by external chemical or mechanical guidance cues. In this talk, I will present our recent theoretical framework that integrates local and global regulatory mechanisms of branching morphogenesis. Combining analytical theory and numerical simulations, I will show that branch orientations follow a generic scaling law that depends on the strength of global guidance. Local interactions such as self-avoidance of branches, on the other hand, lead to denser, efficiently space-filling networks, with a minimal influence on the overall shape and territory. These quantitative predictions of the model are corroborated by experimental data on sensory neurons in the zebrafish caudal fin.

  • Mathilde Lacroix

    Collective contact guidance triggers polar laning of turbulent epithelial monolayers

    Collective cell flows within tissues are central in biological processes such as morphogenesis, wound healing, or cancer progression. In vivo, cell behaviors are controlled by their surrounding environment such as oriented bundles of extra cellular matrix proteins. These fibrillar structures can be recapitulated in vitro with grooved substrates allowing to study the cell response to such cues by “contact guidance”. Up to now, contact guidance has been mostly studied on single cell migration. However, little is known about cell collective response to such cues. Here, we show that, when plated on subcellular grooves, a confluent monolayer of human bronchial epithelial cells (HBECs) spontaneously organizes into wide alternating polar lanes that migrate in antiparallel directions. These lanes can be few millimeters in length and coarsen with time reaching widths of several hundred micrometers before cells jam. Within a lane, cell displacements are described by a plug flow with a well-defined polarity at the tissue scale. Our results are well captured by a hydrodynamic description of an active polar fluid that undergoes a disordered-to-flocking transition favored by friction anisotropy via the damping of fluctuations in the transverse direction. More microscopically, particle based simulations encapsulating a friction anisotropy identify polarity-velocity coupling and excluded volume interactions as key ingredients of the laning transition.

  • Mayarani Muraleedhara Pai

    Looking for fibers : Frustrated self-assembly of 3D printed colloids

    Self-assembly manifests in a multitude of systems of varying complexities. However, our understanding of the process of self-assembly is limited to that in simple systems with elementary interactions. To exploit self-assembly for mitigation of diseases such as Alzheimer’s, Parkinson’s, Prions and sickle cell anaemia or to understand protein aggregation, it is essential to generate understanding of a generalized physical mechanism governing self-assembly of complex systems where the building blocks do not fit together perfectly, resulting in geometric frustration. Recent theoretical investigations stipulates that when ill-fitting particles are brought together, geometric frustration builds up, limiting the growth of aggregates thereby determining the final structure of the 'product of self-assembly' [1]. Our focus is to experimentally investigate the self-assembly of various colloidal shapes fabricated through a sophisticated 3D nanoprinting technique based on two-photon polymerization in order to derive a general physical understanding of frustrated self-assembly. I will discuss the experimental strategies used to fabricate colloids of various geometries and their self-assembly under depletion mediated attractive interaction. During the talk I will discuss the experimental techniques, challenges and some solutions we have identified. Some interesting effects of depletion interaction on the diffusion of colloids, which we learned from our failed experimental trials will also be discussed.

13h00 - 14h30 Lunch @ Espace curie, 29 rue d'Ulm
14h30 - 16h00
  • Serena Ding

    Finding the interaction rules behind C. elegans aggregation and swarming

    The model organism C. elegans has recently emerged as a system to study collective behaviour at the mesoscopic scale. We have recently identified a set of three behavioural rules that individual worms follow to give rise to starkly contrasting aggregation phenotypes. We further extended the model to show that aggregation and swarming are related behaviours, with the latter driven by food depletion. We have since imaged the aggregation behaviour of 197 wild C. elegans strains sampled world-wide, and found natural variation in the behaviour. We now ask: can the same set of behavioural rules apply to these wild strains to capture all of the observed variations, or are new interactions required.

  • Alice Briole

    Intracellular rheology of red blood cells

    Characterizing the rigidity of red blood cells (RBCs) and their heterogeneity in a blood sample is a key parameter in understanding erythrocyte diseases. We propose a method of intracellular rheology based on molecular rotors, viscosity sensitive fluorescent probes. Experiments were conducted on RBCs whose rigidity was varied with temperature. We show that the DASPI molecular rotor can detect variations in their overall rigidity, with a fluorescence signal that increases with cell stiffness. A simple RBC model was developed to separate the cytosol and membrane contributions, allowing a qualitative comparison of the cytosol viscosity variation with independent viscosity measurements of hemoglobin solutions. These experiments show that the rotor is able to detect stiffness heterogeneity at the cellular level within a sample and open up the possibility of new diagnostic techniques, especially in the case of sickle cell disease in which hemoglobin polymerization is directly related to the stiffening of RBCs and to the development of painful vaso-occlusive crises.

  • James Provan

    The topology of DNA knots and catenanes by Atomic Force Microscopy

    The topology of DNA is crucial across diverse cellular processes which target topological signatures (e.g., supercoiling, knotting, catenation), these processes then enzymatically drive major topological changes into the DNA. In recent years Atomic Force Microscopy (AFM) imaging has matured into a powerful technique for the investigation of DNA structure, as it is capable of the examination of uncoated DNA molecules in hydrated conditions. However, the direct examination of DNA knots and catenanes (links) by AFM was unexplored. During my PhD thesis at the University of Glasgow (UK) we utilised an E. coli DNA recombination system for the generation of topologically complex knotted or catenated DNA circles. While these DNA recombination products had been investigated using biochemical techniques, they had not yet been verified on a single-molecule level using microscopy. We used high resolution AFM to investigate the topological assignment of these DNA molecules in comparison to our models and the previous biochemical evidence. This work validated the use of AFM as a highly practical and scalable method of single-molecule DNA topology investigations.

  • Adar Ram Michael

    An active-gel theory of multicellular migration in tissue

    Cells may migrate collectively in tissues in a fluid-like manner. We propose that the tissue can be regarded as a two-component, active, permeating gel, with the cells acting as an active, polar solvent. Our new theory identifies the internal forces of each component (cells and environment) and the interaction forces between components, which orient the solvent (“permeation alignment") and deform the network (“permeation deformation"). These mechanisms drive novel, finite-wavelength instabilities, unique to active, permeating polar gels. Our theory opens an avenue to study cell-matrix interactions during multicellular migration.

16h00-16h30 Coffee Break
  • Pierre Ronceray

    What can we learn from the stochastic trajectories of biological systems?

    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 my recent and ongoing effort to develop principled methods to reconstruct 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.

  • Mert Terzi

    Collective deformation modes promote fibrous self-assembly in protein-like particles

    Self-assembly is a crucial and ubiquitous process for biological systems, in which the building blocks spontaneously organize into larger complexes. If the building blocks fit each other perfectly, self-assembly leads to space filling aggregates. However, in the case of misfitting particles, the resulting aggregates may have limiting sizes. When the misfitting particles are deformable, elastic energy builds up during the assembly. The energy cost of elastic deformation competes with a surface tension which drives the particles into assembly. In the regime in which these two energies are comparable, particles can assemble into self-limiting structures. The relationship between characteristics of the individual particles and the resulting aggregates is not well understood. Through numerical simulations and elastic coarse-graining we show that this relationship is dominated by collective aggregate deformation modes in a broad class of soft particles. We identify two characteristics of particles predictive of the overall aggregate structure. When individual particles have soft deformation modes, these modes collectively control the size of self-limiting aggregates and lead to large-scale structures. The second characteristic is incompressibility which favors anisotropic, and hence fibrous aggregates.

  • Maria-Jose Franco-Onate

    Kinetics of stator recruitment and release in the bacterial flagellar motor

    The Bacterial Flagellar Motor (BFM) is the molecular machine responsible for the torque generation that produces the rotational movement of flagella in bacteria, allowing them to move through aqueous media. Thanks to its mechano-sensitivity, the motor is capable of adapting to external forces by a system of active association and dissociation of stators, which are ion channels that consume the proton motive force to generate the torque that allows the motor to turn. Despite being one of the most studied nano-machines, the BFM possesses dynamical features that have not yet been fully explained within the framework of statistical physics. We aim to explain the stochastic behaviour of the recruitment and release processes of stators from the BFM by considering effects such as stator cooperativity and the small size of the system. We use theoretical (lattice gas) and numerical (Monte Carlo simulations) tools to investigate these processes and evaluate the importance of these effects.

  • Marianne Grognot

    Vibrio cholerae motility in aquatic and mucus-mimicking environments

    Vibrio cholerae swimming motility increases the bacteria’s pathogenicity by an unknown mechanism, thought to involve aiding the bacteria in crossing the intestinal mucus barrier to reach sites of infection. The cell can be either pushed or pulled by its single polar flagellum, but there is no consensus on the resulting repertoire of motility behaviors. We use high-throughput 3D bacterial tracking to observe _V. cholerae_ swimming in buffer, in viscous solutions of the synthetic polymer PVP, and in mucin solutions that may mimic the host mucosal environment. We perform a statistical characterization of its motility behavior based on automated analysis of tens of thousands of 3D individual trajectories. We reveal that _V. cholerae_ performs a run-reverse-flick motility, consisting of a sequence of forward swimming, reversal, and backward swimming, followed by a turn of approximately 90° (a flick) preceding the next forward swimming segment. Forward runs are, on average, more than three times longer than backward ones, an asymmetry that results in an 85% increase in the effective diffusion coefficient compared to the symmetric scenario observed in other species. Surprisingly, the turning frequency decreases with increasing macroscopic viscosity, indicating mechanosensing and adaptation of either the flagellar motor itself or of its physiological inputs.

  • Onofrio Mazzarisi

    Maximal Diversity and Zipfs Law

    Zipf law describes the empirical size distribution of the components of many natural and artificial complex systems. Diversity, on the other hand, is a central concept in ecology, economics, information theory, and other natural and social sciences and can be quantified by diversity indices which characterize the system under study from different angles. I will discuss the co-occurrence of Zipf’s law with the maximization of the diversity of the component sizes, understanding here the number of different sizes rep-resented. I will present the law ruling the increase of such diversity with the total dimension of the system and its relation with Heaps’ law. As an example, I will compare analytical results with linguistics and urbanistic datasets. Future directions include the study of biological scenarios, such as gene expression in cells.

18h15 Cocktail @ Hall L'homond, 24 rue L'homond


Talks & Coffee Breaks:

ENS Paris,
Room Salle Jean Jaures
29 rue d'Ulm (Entrance, 24 rue Lhomond)


École normale supérieure,
Espace Curie
29 rue d'Ulm (Entrance, 24 rue Lhomond)


École normale supérieure,
Grand Hall
24 rue Lhomond


M. Bensouda Koraichi1, X. Chen1, F. Camaglia1, A.C. Costa1, M. Ruiz1, A. Joliot2, L. Koehler3, N.B.B. Canelo 4, I. Nagle 5, P. Jeammet5.

1ENS Paris, 2Institut Curie, 3Universite Paris Saclay, 4Laboratoire Jean Perrin, 5Universite Paris Diderot


And the support of:
Aleksandra Walczak Laboratoire de Physique Theorique, École normale supérieure
and Christine Chambon.