Learning via mechanosensitivity and activity in cytoskeletal networks

2025/04/21 by Deb S. Banerjee, Martin J. Falk, Banerjee, Deb S. +9 · 1 voice
Physics and Astronomy · #Biological Physics (physics.bio-ph) #FOS: Physical sciences #Soft Condensed Matter (cond-mat.soft) #cond-mat.soft #physics.bio-ph

paper · pdf · doi:10.48550/arxiv.2504.15107

Abstract

In this work we show how a network inspired by a coarse-grained description of actomyosin cytoskeleton can learn - in a contrastive learning framework - from environmental perturbations if it is endowed with mechanosensitive proteins and motors. Our work is a proof of principle for how force-sensitive proteins and molecular motors can form the basis of a general strategy to learn in biological systems. Our work identifies a minimal biologically plausible learning mechanism and also explores its implications for commonly occuring phenomenolgy such as adaptation and homeostatis.

Citations

• Temporal Contrastive Learning through implicit non-equilibrium memory
• Minimal vertex model explains how the amnioserosa avoids fluidization during Drosophila dorsal closure
• Rigidity of Epithelial Tissues as a Double Optimization Problem
• Supervised learning in physical networks: From machine learning to learning machines
• Directed aging, memory and Nature's greed
• Vertex models: from cell mechanics to tissue morphogenesis
• Designing allostery-inspired response in mechanical networks
• Modeling semiflexible polymer networks
• Mechanics of Epithelial Tissue Homeostasis and Morphogenesis
• Molecular motors stiffen non-affine semiflexible polymer networks
• The Influence of Cell Mechanics, Cell-Cell Interactions, and Proliferation on Epithelial Packing

Discussions

• check out new work by Deb in collaboration with Martin, @squishycell.bsky.social , Aleks Walczak and Thierry Mora : arxiv.org/abs/2504.15107 o on how simple mechanosensitive agents can enable learning [bsky, 4 points, 0 comments]

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