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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2604.06896 |
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| _version_ | 1866911575906975744 |
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| author | Pasqui, Alessandro Ocana, Jim Martin Catacora Sinha, Anshuman Perez, Matthieu Delbary, Fabrice Gosti, Giorgio Miotto, Mattia Caudo, Domenico Ernoult, Maxence Turlier, Hervé |
| author_facet | Pasqui, Alessandro Ocana, Jim Martin Catacora Sinha, Anshuman Perez, Matthieu Delbary, Fabrice Gosti, Giorgio Miotto, Mattia Caudo, Domenico Ernoult, Maxence Turlier, Hervé |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_06896 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | VertAX: a differentiable vertex model for learning epithelial tissue mechanics Pasqui, Alessandro Ocana, Jim Martin Catacora Sinha, Anshuman Perez, Matthieu Delbary, Fabrice Gosti, Giorgio Miotto, Mattia Caudo, Domenico Ernoult, Maxence Turlier, Hervé Machine Learning Software Engineering Biological Physics 49, 92, 68 J.3; J.2; I.6.5; G.1.6 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. |
| title | VertAX: a differentiable vertex model for learning epithelial tissue mechanics |
| topic | Machine Learning Software Engineering Biological Physics 49, 92, 68 J.3; J.2; I.6.5; G.1.6 |
| url | https://arxiv.org/abs/2604.06896 |