用可微分框架学习上皮组织力学，支持参数反演与逆向设计。
VertAX: a differentiable vertex model for learning epithelial tissue mechanics
- 基于JAX的可微分顶点模型，支持自动微分与GPU加速。
- 通过三类任务验证：形态发生模拟、力学参数反演、组织尺度行为逆向设计。
- 提出无需伴随模拟的隐式梯度方法，适配非可微仿真器。
上皮组织通过细胞间局部机械相互作用动态重塑，顶点模型能有效捕捉这一过程。然而其大量可调参数使推断与优化困难，亟需灵活建模并学习组织力学的计算框架。我们提出VertAX，一个基于JAX的可微分顶点建模框架，用于致密上皮组织模拟。VertAX提供自动微分、GPU加速及端到端双层优化能力，支持前向模拟、参数反演与逆向力学设计。用户可在纯Python中定义任意能量与代价函数，无缝集成机器学习流程。我们在三项代表性任务中验证：(i) 组织形态发生前向建模，(ii) 力学参数反演，(iii) 组织尺度行为逆向设计。对比三种微分策略——自动微分、隐式微分、平衡传播——表明后者仅通过重复前向模拟即可近似梯度，为扩展非可微仿真器的逆生物物理问题提供了低工程成本路径。
原文摘要 · Abstract (English)
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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