Learning epithelial elasticity via local tension remodeling
Year: 2026
Type: article
Abstract: Biological materials such as epithelial tissues continuously remodel their internal structure in response to mechanical forces, but whether this adaptation amounts to a genuine form of physical learning has remained unclear. Here we show that an active vertex model of epithelial sheets, equipped with a single local, mechanosensitive tensionremodeling rule, exhibits the three canonical hallmarks of learning matter. First, at the scale of a single junction, repeated contractile pulses produce habituation: a diminishing, history-dependent response that encodes a memory of prior stimulation. Second, at the tissue scale, a localized stimulus writes a long-range mechanical memory that biases the response to a later, distant stimulus. We derive a continuum theory showing that this memory is transmitted as a system-wide elastic bias and predict its dependence on tissue size and remodeling rate. Third, under cyclic bulk driving, the tissue can be trained to a target mechanical state, autonomously tuning its Poisson ratio to auxetic (negative) values through directed remodeling rather than designed geometry. We further derive an effective viscoelastic memory kernel that distinguishes this adaptive tissue from a passive vertex model. Together, these results identify a candidate mechanism for physical learning in epithelia, in which the ratio of contraction to extension remodeling rates acts as the central control parameter for programmable, history-dependent tissue-scale rheology.
Source: PNAS Nexus
Authors: Sadjad Arzash, Shiladitya Banerjee
Institution: Georgia Institute of Technology
Language: English
Subfield: Cell Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $4,202
OpenAlex ID: W7223451967
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