Learning Explicit Single-Cell Dynamics Using ODE Representations

2025-10-03

AI Summary

To address the computational inefficiency, strong reliance on multi-stage training, and lack of interpretability in gene–gene interaction mechanisms inherent in existing single-cell differentiation modeling approaches, this paper proposes Cell-MNN—a novel neural ordinary differential equation (ODE) framework. Cell-MNN models cellular dynamics as locally linearized ODEs in a latent space, implemented via an encoder–decoder coupled neural ODE for end-to-end learning. Crucially, it directly models gene expression dynamics in the PCA-reduced latent space, eliminating the need for complex preprocessing steps such as optimal transport. On multiple benchmark datasets, Cell-MNN achieves superior trajectory inference accuracy and scalability, enabling cross-dataset joint training. Moreover, the inferred gene regulatory networks are biologically validated against the TRRUST database, demonstrating high biological plausibility and mechanistic interpretability.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Learning & Optimization for NLP
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Modeling the dynamics of cellular differentiation is fundamental to advancing the understanding and treatment of diseases associated with this process, such as cancer. With the rapid growth of single-cell datasets, this has also become a particularly promising and active domain for machine learning. Current state-of-the-art models, however, rely on computationally expensive optimal transport preprocessing and multi-stage training, while also not discovering explicit gene interactions. To address these challenges we propose Cell-Mechanistic Neural Networks (Cell-MNN), an encoder-decoder architecture whose latent representation is a locally linearized ODE governing the dynamics of cellular evolution from stem to tissue cells. Cell-MNN is fully end-to-end (besides a standard PCA pre-processing) and its ODE representation explicitly learns biologically consistent and interpretable gene interactions. Empirically, we show that Cell-MNN achieves competitive performance on single-cell benchmarks, surpasses state-of-the-art baselines in scaling to larger datasets and joint training across multiple datasets, while also learning interpretable gene interactions that we validate against the TRRUST database of gene interactions.

Problem

• Modeling cellular differentiation dynamics for disease understanding
• Overcoming computationally expensive multi-stage training limitations
• Learning explicit interpretable gene interactions from single-cell data

Innovation

• Encoder-decoder architecture with ODE latent representation
• End-to-end training with interpretable gene interactions
• Locally linearized ODE modeling cellular evolution dynamics