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

• URL: http://arxiv.org/abs/2605.28659v1
• Date: Wed, 27 May 2026 15:57:40 GMT
• Title: Applications of temporal graph learning for predicting the dynamics of biological systems
• Abstract summary: We investigate an alternative temporal graph-based perspective in which cellular states are represented through pseudotime-resolved gene regulatory networks.<n>For link prediction and centrality forecasting, temporal graph learning captures non-trivial regulatory dynamics and enables the identification of temporally important gene hubs.
• Score: 2.1485350418225244
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Unified Geometric Framework for Developmental Analysis of Spatial Transcriptomic Data [38.40052829128497]
  High-resolution single-cell and transcriptomic technologies provide snapshots of heterogeneous cellular states.<n> temporal and spatial dynamics must be inferred from independently sampled, unaligned cell populations.<n>We introduce a geometric framework for analyzing thetemporal evolution of gene expression networks through embeddings in Gromov--Wasserstein (GW) space.
  arXiv  Detail & Related papers  (2026-08-15T16:19:58Z)
• Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction [40.68914450179759]
  Single-cell perturbation prediction aims to infer how cells respond to unseen interventions.<n>We propose a latent dynamical causal generative model for single-cell perturbation data.<n>Under suitable conditions, the latent causal variables are recoverable up to standard equivalence classes.
  arXiv  Detail & Related papers  (2026-05-25T08:32:23Z)
• Learning noisy tissue dynamics across time scales [0.0]
  We introduce a biomimetic machine learning framework capable of inferring noisy multicellular dynamics directly from experimental movies.<n>This generative model combines graph neural networks, normalizing flows and WaveNet algorithms to represent tissues as neural differential equations.<n>We show that our model not only captures cell motion but also predicts the evolution of cell states in their division cycle.
  arXiv  Detail & Related papers  (2025-10-21T21:33:54Z)
• Kuramoto Orientation Diffusion Models [67.0711709825854]
  Orientation-rich images, such as fingerprints and textures, often exhibit coherent angular patterns.<n>Motivated by the role of phase synchronization in biological systems, we propose a score-based generative model.<n>We implement competitive results on general image benchmarks and significantly improves generation quality on orientation-dense datasets like fingerprints and textures.
  arXiv  Detail & Related papers  (2025-09-18T18:18:49Z)
• Unifying Physics- and Data-Driven Modeling via Novel Causal Spatiotemporal Graph Neural Network for Interpretable Epidemic Forecasting [4.791960848500047]
  We propose a novel Causpatioal Neural Graph Network (CSTG) framework to model the temporal dynamics of infectious diseases.<n>We use an adaptive static connectivity graph to represent the stable components of human mobility and utilize a temporal dynamics model fluctuations within these patterns.<n>We validate our model using real-world datasets at the provincial level in China and the state level in Germany.
  arXiv  Detail & Related papers  (2025-04-07T14:46:11Z)
• GENERator: A Long-Context Generative Genomic Foundation Model [66.46537421135996]
  We present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs (bp) and 1.2B parameters.<n>Trained on an expansive dataset comprising 386B bp of DNA, the GENERator demonstrates state-of-the-art performance across both established and newly proposed benchmarks.<n>It also shows significant promise in sequence optimization, particularly through the prompt-responsive generation of enhancer sequences with specific activity profiles.
  arXiv  Detail & Related papers  (2025-02-11T05:39:49Z)
• Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion [61.751257172868186]
  Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities by exploiting graph and temporal dynamics. Existing DyGNNs fail to handle distribution shifts, which naturally exist in dynamic graphs.
  arXiv  Detail & Related papers  (2023-11-24T02:42:42Z)
• Tertiary Lymphoid Structures Generation through Graph-based Diffusion [54.37503714313661]
  In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. We show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content.
  arXiv  Detail & Related papers  (2023-10-10T14:37:17Z)
• Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting [50.901984244738806]
  We show how to model the evolutionary and multi-scale interactions of time series. In particular, we first provide a hierarchical graph structure cooperated with the dilated convolution to capture the scale-specific correlations. A unified neural network is provided to integrate the components above to get the final prediction.
  arXiv  Detail & Related papers  (2022-06-28T08:11:12Z)
• Self-Supervised Graph Representation Learning for Neuronal Morphologies [75.38832711445421]
  We present GraphDINO, a data-driven approach to learn low-dimensional representations of 3D neuronal morphologies from unlabeled datasets. We show, in two different species and across multiple brain areas, that this method yields morphological cell type clusterings on par with manual feature-based classification by experts. Our method could potentially enable data-driven discovery of novel morphological features and cell types in large-scale datasets.
  arXiv  Detail & Related papers  (2021-12-23T12:17:47Z)
• Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling [0.0]
  We propose a dynamic adaptivetemporal graph convolution (DASTGCN) model to overcome the shortcomings of pre-defined static correlation-based graph structures. The proposed approach allows end-to-end inference of dynamic connections between brain regions via layer-wise graph structure learning module. We evaluate our pipeline on the UKBiobank for age and gender classification tasks from resting-state functional scans.
  arXiv  Detail & Related papers  (2021-09-26T07:19:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.