Graph Pseudotime Analysis and Neural Stochastic Differential Equations for Analyzing Retinal Degeneration Dynamics and Beyond

2025-02-10

AI Summary

Retinal degenerative diseases exhibit complex, stage-dependent molecular progression, yet the structural and temporal dynamics underlying pathway-level transitions—and their irreversible tipping points—remain poorly understood. Method: We propose Graph-level Pseudotime Analysis (GPA), a novel computational framework that constructs biologically informed pathway interaction graphs and integrates graph neural networks with pseudotime inference to model population-scale disease trajectories. Crucially, we introduce neural stochastic differential equations (Neural SDEs) to model pathway dynamics, enabling quantitative assessment of stability and precise identification of “point-of-no-return” thresholds. Contribution/Results: GPA enables dynamic inter-pathway interaction analysis and multi-dimensional phenotypic mapping. Applied to the JR5558 mouse model, it successfully reconstructs pathway-level evolutionary trajectories, identifies key driver pathways, and pinpoints critical transition nodes—yielding mechanistic insights into retinal degeneration and establishing a foundation for stage-specific therapeutic intervention.

Technology Category

• Machine Learning: Probabilistic Circuits and Graphical Models
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Understanding disease progression at the molecular pathway level usually requires capturing both structural dependencies between pathways and the temporal dynamics of disease evolution. In this work, we solve the former challenge by developing a biologically informed graph-forming method to efficiently construct pathway graphs for subjects from our newly curated JR5558 mouse transcriptomics dataset. We then develop Graph-level Pseudotime Analysis (GPA) to infer graph-level trajectories that reveal how disease progresses at the population level, rather than in individual subjects. Based on the trajectories estimated by GPA, we identify the most sensitive pathways that drive disease stage transitions. In addition, we measure changes in pathway features using neural stochastic differential equations (SDEs), which enables us to formally define and compute pathway stability and disease bifurcation points (points of no return), two fundamental problems in disease progression research. We further extend our theory to the case when pathways can interact with each other, enabling a more comprehensive and multi-faceted characterization of disease phenotypes. The comprehensive experimental results demonstrate the effectiveness of our framework in reconstructing the dynamics of the pathway, identifying critical transitions, and providing novel insights into the mechanistic understanding of disease evolution.

Problem

• Develops graph-forming method for pathway analysis
• Introduces Graph-level Pseudotime Analysis for disease trajectories
• Uses neural SDEs to define pathway stability and bifurcation

Innovation

• Graph Pseudotime Analysis for trajectories
• Neural SDEs for pathway stability
• Biologically informed graph-forming method