Geometry-driven self-organisation in active living matter

Objective

Living materials process information through their physical degrees of freedom, enabling autonomous functions beyond the capabilities of electronic systems. For example, a migrating cell uses its own shape dynamics to navigate through complex environments and decide which way to go. Yet, the principles underlying such physical information processing have not been identified due to the complexity inherent to problems involving fluctuating dynamic boundaries, and the need for a multidisciplinary approach to link theory with experiments. Building on recent scientific and methodological advances from my group, I propose to investigate how soft active materials perform computations and self-organise functional behaviours using the deformations of their own shape. Specifically, using surface anchoring effects in anisotropic materials as a paradigm for physical shape sensing, GOAL will identify how information is transferred from the covariant dynamics of active boundaries to surface-controlled bulk states, enabled by our novel approach to simulate active surface fluctuations (WP1-2). Combining theoretical results with experimental data from the technological frontier of biology, we will identify how the coupling between anisotropic materials and their geometry generates functional behaviours, such as autonomous object avoidance or embryo self-organisation (WP3). Lastly, we will leverage the visual accessibility of geometrical features to probe how machine learning approaches infer geometry-driven states and transitions from images, and to test our information theoretical predictions on shape sensing (WP4). GOAL will likely lead to the discovery of universal principles for shape-adaptive matter, because the nonlinear dependencies that arise from geometrical relations express fundamental mathematical properties of space that hold across all scales and contexts.