Reconstructing gene network structure and dynamics from single cell data.

Abstract

Biological functions are governed by gene regulatory networks (GRNs). Accurately inferring GRNs from high-dimensional and noisy single-cell data remains a major challenge in systems biology. Conventional approaches often struggle with robustness and interpretability, particularly when applied to complex biological processes such as cell fate decisions and complex diseases. In this study, we propose GGANO, a hybrid framework that integrates Gaussian Graphical Models for conditional independence learning with Neural Ordinary Differential Equations for dynamic modeling and inference. Benchmark analyzes show that GGANO achieves superior accuracy and stability compared to existing methods, particularly under high-noise conditions. Furthermore, GGANO enables the inference of stochastic dynamics from single-cell data. Applying GGANO to the EMT datasets, we uncover intermediate cellular states and key regulatory genes driving EMT progression. The source code is available at GitHub: https://github.com/ChenFeng87/GGANO.