Tianshou Zhou

This study introduces a stochastic model to analyze nascent RNA kinetics with complex promoters and develops a method to infer promoter memory from stationary RNA distributions, validated on synthetic and HIV-1 data.

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.

DTFLOW infers multi-branch pseudo-temporal trajectories from single-cell data using dimensionality reduction and graph-based methods, showing superior accuracy and robustness.

The paper analyzes gene expression models to identify control strategies that minimize timing variability and mean first passage time of intracellular events.

The paper models fractional killing in cancer cells as a stochastic process, showing that dynamic variability in apoptotic thresholds can enhance cellular killing by reducing the mean first-passage time for p53 to reach the threshold level.

The paper develops two algorithms to compute stationary distributions in non-Markovian stochastic models of cellular processes, demonstrating their application to gene expression and toggle switch models.

The paper derives stationary equations for non-Markovian biochemical systems, transforming them into Markovian problems and enabling noise estimation.

Topographer identifies cell fate mechanisms by analyzing single-cell transcriptomic data, revealing dynamic gene expression patterns and cell-state transitions during myoblast differentiation.