Zhenquan Zhang

Deep learning linking mechanistic models to single-cell transcriptomics data reveals transcriptional bursting in response to DNA damage

This study introduces DeepTX, a deep learning framework that links mechanistic models to scRNA-seq data to infer transcriptional burst kinetics in response to DNA damage.

Theoretical Principles of Enhancer-Promoter Communication in Transcriptional Bursting

This paper develops a theoretical framework to model enhancer-promoter communication and its impact on transcriptional bursting, revealing power-law relationships and novel distribution patterns.

Inferring transcriptional bursting kinetics from single-cell snapshot data using a generalized telegraph model

This paper introduces a generalized telegraph model and Bayesian inference method to infer transcriptional burst kinetics from single-cell data, showing improved accuracy over classical models.

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics

The paper infers genome-wide transcriptional burst kinetics and feedback regulations using scRNA-seq data from mouse fibroblasts, revealing how feedback influences burst frequency and size.

Silent transcription intervals and translational bursting lead to diverse phenotypic switching

The paper develops a generalized telegraph model to capture bimodal gene expression dynamics caused by silent transcription intervals and translational bursting, showing these factors can generate two nonzero bimodalities not captured by classical models.