Applications of temporal graph learning for predicting the dynamics of biological systems

2026-05-27

AI Summary

This work addresses the limitation of existing biological foundation models, which predominantly rely on static gene expression data and thus fail to capture the dynamic evolution of gene regulatory networks (GRNs) during cellular development. The authors propose a novel approach that infers pseudotemporal trajectories from single-cell transcriptomic data, discretizes them into developmental snapshots, and reconstructs GRNs at each snapshot. A temporal graph neural network is then introduced to explicitly model the dynamic rewiring of regulatory interactions over time, enabling accurate prediction of gene expression, regulatory links, and key hub genes. To the best of our knowledge, this is the first application of temporal graph learning to single-cell biology. Evaluated on mouse erythroid gastrulation and pancreatic endocrine development datasets, the method significantly outperforms state-of-the-art foundation models such as scGPT and scFoundation across all three tasks, revealing non-trivial dynamic regulatory mechanisms.

Abstract

Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene-expression matrices. However, these approaches predominantly operate in static settings and do not explicitly model the temporal evolution of developmental programs in the cell. Modeling such dynamics is important for understanding how cellular states progressively emerge, differentiate, and reorganize during development or disease progression. In this work-in-progress paper, we investigate an alternative temporal graph-based perspective in which cellular states are represented through pseudotime-resolved gene regulatory networks and modeled as evolving graph structures over persistent gene identities. Starting from single-cell transcriptomic data, we infer pseudotime trajectories, discretize cells into developmental snapshots, reconstruct one gene regulatory network per snapshot, and apply temporal graph neural networks to forecast biological states. We evaluate this framework on two publicly available mouse developmental datasets, erythroid gastrulation and pancreatic endocrinogenesis, considering three complementary tasks: gene-expression forecasting, link prediction, and out-degree centrality prediction. Our results show that graph-based models outperform well-known foundation-model such as scGPT and scFoundation, suggesting that explicitly modeling evolving regulatory structure provides useful information beyond static pretrained representations. For link prediction and centrality forecasting, temporal graph learning captures non-trivial regulatory dynamics and enables the identification of temporally important gene hubs. Overall, our findings support temporal graph learning as a promising direction for modeling dynamic biological systems and as a complementary paradigm to current foundation model approaches in single-cell biology.

Problem

• temporal graph learning
• gene regulatory networks
• single-cell transcriptomics
• pseudotime
• biological dynamics

Innovation

• temporal graph learning
• gene regulatory networks
• pseudotime
• single-cell transcriptomics
• dynamic biological systems