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Mechanosensitive Remodeling Sustains Rigidity Homeostasis and Elastic Memory in Actin Cortex Models

Haina Wang1, Marco A. Galvani Cunha1, John C. Crocker2, and Andrea J. Liu1,3,*

  • *Contact author: ajliu@physics.upenn.edu
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PRX Life 4, 043002 – Published 15 October, 2026

DOI: https://doi.org/10.1103/nxhn-2yd1

Abstract

The actin cortex is a dynamic biopolymer network that, despite constant architectural changes through the assembly and disassembly of filaments and crosslinkers, sustains a rigid homeostatic state, i.e., a steady state of positive elastic moduli, and exhibits an elastic memory that far outlasts the structural turnover timescale. We still lack simple models that maintain collective rigidity and elastic memory at long times compared to the turnover time. To address this, we develop two complementary elastic network models in which rigidity homeostasis and long-lived elastic memory with complete turnover emerge as a result of mechanosensitive dynamics of filaments (edges) and crosslinkers (nodes), respectively. Both models require the following minimal ingredients: (1) preferential disassembly of edges or nodes under small tension or force, (2) a small but nonzero rate of random disassembly, and (3) energy injection upon assembly. Our models are robust to variations in random disassembly rates, can recover from drastic structural disruption, and exhibit diffusion of nodes and edges in steady state, displaying representational drift similar to that found in neuronal activities and physical learning circuits. We propose that the cortex is an example of “tunable matter,” i.e., its mechanosensitive remodeling dynamics tune properties of its edges and nodes so that the cortex as a whole can maintain robust but flexible rigidity in fluctuating mechanical environments.

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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, 043002– Published 15 October, 2026
  • Received 5 October 2025
  • Accepted 8 September 2026
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