Learning noisy tissue dynamics across time scales

TL;DR

This paper presents a biomimetic machine learning framework that models noisy tissue dynamics across multiple time scales using graph neural networks and stochastic differential equations, enabling accurate predictions and generation of biological tissue behaviors.

Contribution

The authors introduce a novel neural network architecture that encodes tissue as a dual signaling graph, reducing data requirements and capturing complex stochastic cellular dynamics.

Findings

Successfully predicts cell motion and division cycle evolution in epithelial tissues.

Accurately generates developmental dynamics like fly wing development.

Models stochastic ERK signaling waves in cell communication.

Abstract

Tissue dynamics play a crucial role in biological processes ranging from inflammation to morphogenesis. However, these noisy multicellular dynamics are notoriously hard to predict. Here, we introduce a biomimetic machine learning framework capable of inferring noisy multicellular dynamics directly from experimental movies. This generative model combines graph neural networks, normalizing flows and WaveNet algorithms to represent tissues as neural stochastic differential equations where cells are edges of an evolving graph. Cell interactions are encoded in a dual signaling graph capable of handling signaling cascades. The dual graph architecture of our neural networks reflects the architecture of the underlying biological tissues, substantially reducing the amount of data needed for training, compared to convolutional or fully-connected neural networks. Taking epithelial tissue experiments as a case study, we show that our model not only captures stochastic cell motion but also predicts the evolution of cell states in their division cycle. Finally, we demonstrate that our method can accurately generate the experimental dynamics of developmental systems, such as the fly wing, and cell signaling processes mediated by stochastic ERK waves, paving the way for its use as a digital twin in bioengineering and clinical contexts.