Cell-induced densification and tether formation in fibrous extracellular matrices with biomimetic physics-informed neural networks

TL;DR

This paper introduces Bio-PINNs, a neural network approach that effectively models cell-induced microstructures and tether formations in fibrous extracellular matrices, overcoming numerical challenges in phase transition simulations.

Contribution

Bio-PINNs implement a curriculum learning strategy and a deformation-uncertainty proxy to improve modeling of microstructures and interfaces in cell-matrix interactions.

Findings

Bio-PINNs reliably recover densified phases near cell boundaries.

They accurately capture tether morphology in fibrous matrices.

Outperform existing adaptive baseline methods in benchmarks.

Abstract

Nonconvex multi-well energies in cell-induced phase transitions give rise to fine-scale microstructures, low-regularity transition layers and sharp interfaces, all of which pose numerical challenges for physics-informed learning. Here we introduce biomimetic physics-informed neural networks (Bio-PINNs), which implement a near-to-far curriculum by progressively revealing the computational domain away from the cell boundary and combining this schedule with a deformation-uncertainty proxy that concentrates collocation points near evolving transition layers and tether-forming regions. Across single-cell and multicellular benchmarks, Bio-PINNs recover the densified phase more reliably near cell boundaries and in intercellular gaps, while capturing tether morphology more faithfully than representative ungated and residual-driven adaptive baselines.