Bacterial glass transition in Pseudomonas aeruginosa by Martin MALIET

Motile bacteria self-organize in numerous collective phases, such as orientationally ordered phase or swarming state. Understanding how individual properties can trigger emergence of long range order is a crucial aspect of biological and physical studies on bacteria. Here we study the properties of the 2D swarming state of an elongated motile bacteria, Pseudomonas aeruginosa, in growing colonies. While forming an already complex system at low density, P. aeruginosa monolayers undergo kinetic arrest, and collectively transition to a glassy state as density increases. We show that this transition does not only affect the scales of the system’s relaxation times, but also the very nature of the dynamics at play.

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Relating protein sequence to function: large-scale experiments and statistical models by Clément NIZAK

Statistical modeling (‘machine learning’) of large sequence datasets predicts the 3D-structure of proteins from their amino-acid sequence and generates functional synthetic proteins (‘protein design’). These recent breakthroughs were obtained by considering proteins as naturally evolving entities, instead of focusing on physical-chemical properties of amino-acids. Currently, this powerful approach is mainly limited by the availability of experimental data as input. I will present an overview of our experiments on producing the required input datasets and on testing ‘protein design’ model predictions, based on large-scale gene synthesis, high-throughput molecular-level functional screens, and massively parallel DNA sequencing.