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

TL;DR

This paper reviews how structured single-cell data can be used with advanced models and inference methods to better understand stochastic gene expression dynamics, overcoming fundamental limitations of snapshot data.

Contribution

It highlights recent progress in integrating structured data with stochastic models and machine learning to improve mechanistic inference of gene expression.

Findings

Structured datasets help resolve identifiability issues.

Advanced inference strategies enable gene-level insights.

Foundations for scaling to networks and tissues are established.

Abstract

Single-cell gene expression measurements encode variability spanning molecular noise, cell-to-cell heterogeneity, and technical artifacts. Mechanistic stochastic models provide powerful approaches to disentangle these sources, yet inferring underlying dynamics from standard snapshot sequencing data faces fundamental identifiability limitations. This review focuses on how structured datasets with temporal, spatial, or multimodal features offer constraints to resolve these ambiguities, but demand more sophisticated models and inference strategies, including machine-learning techniques with inherent tradeoffs. We highlight recent progress in the judicious integration of structured single-cell data, stochastic model development, and innovative inference strategies to extract predictive, gene-level insights. These advances lay the foundation for scaling mechanistic inference upward to regulatory networks and multicellular tissues.