The TransClog projet aims at investigating the migration and clogging of flexible fibers in structured media. The motion of pulp fibers in the papermaking process, the migration of parasites in mucus, and the dispersal of bacterial biofilm filaments in biological environments, happen in complex media that are structured by obstacles whose size is comparable to the size of the moving particles. At this micrometric scale, the hydrodynamic and elastic couplings with obstacles can lead to the stoppage of the particles. Once blocked, a biofilm filament can grow rapidly and clog a medical stent or a capillary, thus contaminating the medium. Understanding how small elastic structures navigate in a flow embedded with obstacles is essential to study the physics of biological and industrial systems, but also to deal with major sanitary issues, such as the prevention and treatment of infections. We will combine cutting-edge numerical simulations with experiments to achieve two objectives: 1) Predict the clogging probability and migration modes of flexible fibers in porous media, 2) Quantify the effect of geometry, mechanical properties and physico-chemical interactions of artificial and natural gels on their fiber trapping efficiency.

How do tissues and organs grow to reach well-defined shapes? For instance, thin tissues, such as animal epithelia and plant leaves, are typically flat, whereas the default state of a growing thin sheet is curved. How is flat shape achieved? Here we address this question in Arabidopsis leaves, a system amenable to live-imaging of growth, biophysical experiments, and genetic manipulation. We hypothesize that cell-to-cell growth heterogeneity enables cells to sense variations in leaf curvature and maintain flatness. We aim to test this hypothesis using experimental and theoretical biophysics with cell biology. We will (i) characterize the relationship between cell-to-cell heterogeneity, cell mechanics, and leaf flatness, (ii) build a theoretical framework to model a thin active growing sheet in 3D space, and (iii) characterize the combinatorial regulation of flatness through model predictions and experimental tests. Altogether, we expect to shed light on the robustness of morphogenesis.

Flow transport in complex networks is abundant in biology and engineering, from the vasculature of animals, to the hyphal networks of fungi, to the random porous media making up batteries. It has long been thought that biological network morphologies were optimised to minimise the energetic cost associated to viscous flow dissipation in their branches. However, another possibility, raised recently is for these networks to be optimal for mass exchange, or perfusion. We then need not only to have a network that covers space efficiently, but also whose morphology leads to an even flow of chemicals (catalysts, nutrients, oxygen,...) throughout all its tubes, so that all parts of the network receive the same amount of chemical. Living systems continuously adapt their network morphology in response to stimuli; local feedback coupled to the presence of global flows leads to self-organised structures optimal for perfusion. In contrast, fluid velocities in engineered networks of random media differ from tube to tube, and follow an overall exponential distribution. Transport through these porous media is inefficient, being limited to a few fast lanes. The current strategy to optimize flow in porous media is to build, branch by branch, an optimized network morphology. The aim of our project is to combine theory, simulations and experiments to generate adaptive microfluidic networks whose morphology self-organises in response to signals, leading to network morphologies optimal for perfusion. In addition to its fundamental interest, the outcome of this project has a wide range of applications, from the design and cooling of efficient batteries, to the production of enhanced chemical reactors having high transport efficiency and a large reaction surface, contributing to having a cleaner, more affordable energy.

MIMETUBE aims at providing new types of tubular biomaterials to address unmet clinical needs in vascular and thoracic surgery, and to serve as platforms for fundamental investigations of graft integration and tissue regeneration. The consortium gathers materials scientists, physicists, physiologists and surgeons sharing a strong interest for bioengineering. The processing strategy relies on ice-templating of highly concentrated type I collagen materials coupled with topotactic fibrillogenesis – recently reported by the LCMCP. The resulting hierarchical materials display biomimetic assembly of collagen fibres as well as stabilization of the macroscopic features required for surgical application. By providing on-demand tissues with precisely modulated properties, including geometrical and mechanical features, MIMETUBE aims at dramatically increasing the availability of grafts for both treatment of peripheral arterial disease and airway transplantation.

Engineering strategically uses compliant components in the design of structures exposed to flows, as those can change their shape and adapt to their surrounding fluid environment. While a flexible structure is more adaptable and versatile, it is also more difficult to control. We thus have to understand the way it deforms and find levers to control it. In this project, we will explore an unconventional route to tailor the deformation of surfaces in a flow, making use of the unique properties of origami (folded sheet) and kirigami (sheet with a network of cuts). Previous literature showed that the meso-structure of folds or cuts allows for surfaces to morph into sophisticated shapes, and produces programmable non-linear mechanical properties. The objective of this project is to study how those original features impact the way the structure interact with a flow, and how it can be harnessed to produce novel mechanical behaviors. Origami and kirigami provide opportunities to revisit fluid/structure interaction out of its regular framework, paving the way for controlled deployment pathways in flows.