Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This paper introduces VertAX, a differentiable vertex model framework built in JAX for simulating and learning epithelial tissue mechanics. It enables automatic differentiation, GPU acceleration, and bilevel optimization for tasks like forward simulation, parameter inference, and inverse mechanical design by allowing users to define energy and cost functions in Python. The authors benchmark three differentiation strategies, finding that equilibrium propagation can approximate gradients using repeated forward simulations, simplifying inverse problems for non-differentiable simulators.
Equilibrium propagation lets you train non-differentiable biophysical simulators without adjoints, opening the door to inverse design even when gradients are unavailable.
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.