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Learning via Mechanosensitivity and Activity in Cytoskeletal Networks

Deb S. Banerjee1,2, Martin J. Falk1,3, Margaret L. Gardel1,3,4,5, Aleksandra M. Walczak6,1,3, Thierry Mora6,1,3, and Suriyanarayanan Vaikuntanathan1,2,*

  • *Contact author: svaikunt@uchicago.edu
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PRX Life 4, 013001 – Published 5 January, 2026

DOI: https://doi.org/10.1103/s7tg-6q4q

Abstract

In this work we show how a network inspired by a coarse-grained description of the 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 without neurons. Our work identifies a minimal biologically plausible learning mechanism and also explores its implications for commonly occurring phenomenology, such as adaptation and homeostasis.

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This article appears in the following collection:

PRX Life Collection on adaptation and learning

PRX Life presents a special collection on ‘Biological systems that learn’, curated by the organizers of the National Institute for Theory and Mathematics in Biology (NITMB) workshop together with early-career researchers. The papers in this collection illustrate how living systems acquire functions, showing it is useful to describe them as tunable matter with many individually adjustable degrees of freedom characterizing microscopic properties. Drawing inspiration from AI and non-biological systems that can acquire desired functions, the featured research highlights the timeliness of the topic and its potential to drive unifying frameworks for emergent collective function in living tunable matter.

Every article in this collection is subject to a rigorous peer review process, upholding the high standards applied to all papers in the journal. The PRX Life editorial team manages peer review and makes all editorial decisions.

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PRX Life 4, 013001– Published 5 January, 2026
  • Received 2 June 2025
  • Accepted 17 October 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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