Zihao Wang

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.

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.

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.

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.

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.

The paper analyzes a complex promoter model with multiple ON and OFF states to show how transcriptional bursting kinetics are shaped by promoter cycle mechanisms.

This study develops a multiscale model to show that chromatin dynamics and gene transcription synergize to enhance the robustness and stability of epigenetic cell memory across cell cycles.