标题： 从结构化的单细胞数据推断基因的随机表达的机制 显示英文标题

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

摘要： 单细胞基因表达测量编码了从分子噪声、细胞异质性到技术性错误的各种变异性。 机制模型提供了一个原则性的框架来分离这些来源并提取见解，但从标准的测序计数数据推断潜在的动力学面临着根本性的限制。 具有时间、空间或多模态特征的结构化数据集提供了有助于解决这些歧义的约束条件，但需要更复杂的模型和先进的推理策略，包括具有相应权衡的机器学习技术。 这篇综述强调了在明智地整合结构化单细胞数据、随机模型开发以及创新推理策略方面取得的最新进展，以提取基因层面的见解。 这些方法为理解调控网络和多细胞系统的机制奠定了基础。 显示英文摘要

摘要： 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.