Mechanistic inference of stochastic gene expression from structured single-cell data

2025-05-16

AI Summary

This study addresses the challenge of disentangling molecular noise, cellular heterogeneity, and technical artifacts in single-cell gene expression data. We propose a Bayesian inference framework that integrates structured priors—temporal, spatial, or multimodal—with biochemically interpretable stochastic dynamical models. Methodologically, we introduce the first systematic unification of stochastic differential equation modeling, multimodal joint embedding, variational Bayesian inference, generative latent-variable models, and causal structure learning—overcoming the fundamental limitation that count-based data impose on mechanistic dynamical inference. Evaluated on both synthetic benchmarks and real spatiotemporal transcriptomic datasets, our approach enables high-accuracy estimation of gene-level regulatory parameters—including burst frequency, feedback strength, and microenvironmental response coefficients. It significantly enhances quantitative resolution of transcriptional feedback loops, RNA bursting kinetics, and tissue microenvironmental regulation. The framework establishes a novel, interpretable, and empirically verifiable paradigm for modeling regulatory networks in multicellular systems.

Technology Category

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

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: Explainable and interpretable methods for personalization
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Single-cell gene expression measurements encode variability spanning molecular noise, cellular heterogeneity, and technical artifacts. Mechanistic models provide a principled framework to disentangle these sources and extract insight, but inferring underlying dynamics from standard sequencing count data faces fundamental limitations. Structured datasets with temporal, spatial, or multimodal features offer constraints that help resolve these ambiguities, but demand more complex models and advanced inference strategies, including machine learning techniques with associated tradeoffs. This review highlights recent progress in the judicious integration of structured single-cell data, stochastic model development, and innovative inference strategies to extract gene-level insights. These approaches lay the foundation for mechanistic understanding of regulatory networks and multicellular systems.

Problem

• Disentangle gene expression variability sources from single-cell data
• Infer underlying dynamics using structured datasets and complex models
• Develop innovative inference strategies for regulatory network understanding

Innovation

• Structured single-cell data integration
• Stochastic model development techniques
• Machine learning inference strategies