Tianshou Zhou

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.

Kinetic characteristics of transcriptional bursting in a complex gene model with cyclic promoter structure

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.

Synergism between Chromatin Dynamics and Gene Transcription Enhances Robustness and Stability of Epigenetic Cell Memory

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: Inference and Visualization of Single-cell Pseudo-temporal Trajectories Using Diffusion Propagation

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

Control strategies for the timing of intracellular events

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

Dynamic variability in apoptotic threshold as a strategy for combating fractional killing

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.

Computation of stationary distributions in stochastic models of cellular processes with molecular memory

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.

Stationary Equations for Non-Markovian Biochemical Systems

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

Topographer Reveals Dynamic Mechanisms of Cell Fate Decisions from Single-Cell Transcriptomic Data

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