Biological matter has the fascinating ability to autonomously generate material deformations via intrinsic active forces, where the latter are often present within effectively two-dimensional structures. The dynamics of such “active surfaces” inevitably entails a complex, self-organized interplay between geometry of a surface and its mechanical interactions with the surrounding. The impact of these factors on the self-organization capacity of surfaces made of an active material, and how related effects are exploited in biological systems, is largely unknown.
In this talk, I will first discuss general numerical challenges in analysing self-organising active surfaces and the bifurcation structure of emergent shape spaces. I will then focus on active surfaces with broken up-down symmetry, of which the eukaryotic cell cortex and epithelial tissues are highly abundant biological examples. In such surfaces, a natural interplay arises between active stresses and surface curvature. We demonstrate that this interplay leads to a comprehensive library of spontaneous shape transformations that resemble stereotypical morphogenetic processes. These include cell-division-like invaginations and the autonomous formation of tubular surfaces of arbitrary length, both of which robustly overcome well-known shape instabilities that would arise in analogue passive systems.
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Alexander Mietke is a theoretical physicist working on active and living matter. He frequently collaborates with experimentalists who study processes at the cell, tissue and organism scale to identify minimal physical principles that guide these processes. This often inspires new theoretical work on topics in non-equilibrium soft matter physics, more broadly in the self-organization of mechanical and chemical patterns in active matter, the emergent shape dynamics of membranes and active surfaces, liquid crystals in complex geometries, chirality in active systems, as well as in developing coarse-graining and inference approaches that are directly applicable to experimental data.