VertAX: a differentiable vertex model for learning epithelial tissue mechanics

• URL: http://arxiv.org/abs/2604.06896v1
• Date: Wed, 08 Apr 2026 09:56:05 GMT
• Title: VertAX: a differentiable vertex model for learning epithelial tissue mechanics
• Abstract summary: We introduce VertAX, a differentiable JAX-based framework for vertex-modeling of confluent epithelia.<n>Users can define arbitrary energy and cost functions in pure Python, enabling seamless integration with machine-learning pipelines.<n>We benchmark three differentiation strategies-automatic differentiation, implicit differentiation, and equilibrium propagation-showing that the latter can approximate gradients using repeated forward, adjoint-free simulations alone.
• Score: 32.75549521267761
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Epithelial tissues dynamically reshape through local mechanical interactions among cells, a process well captured by vertex models. Yet their many tunable parameters make inference and optimization challenging, motivating computational frameworks that flexibly model and learn tissue mechanics. We introduce VertAX, a differentiable JAX-based framework for vertex-modeling of confluent epithelia. VertAX provides automatic differentiation, GPU acceleration, and end-to-end bilevel optimization for forward simulation, parameter inference, and inverse mechanical design. Users can define arbitrary energy and cost functions in pure Python, enabling seamless integration with machine-learning pipelines. We demonstrate VertAX on three representative tasks: (i) forward modeling of tissue morphogenesis, (ii) mechanical parameter inference, and (iii) inverse design of tissue-scale behaviors. We benchmark three differentiation strategies-automatic differentiation, implicit differentiation, and equilibrium propagation-showing that the latter can approximate gradients using repeated forward, adjoint-free simulations alone, offering a simple route for extending inverse biophysical problems to non-differentiable simulators with limited additional engineering effort.

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