Philipp Thomas

This paper introduces SFABC, a simulation-free method for Bayesian inference of stochastic reaction networks, showing it matches simulation-based accuracy at lower computational cost.

The paper identifies integrin α11 as a quiescence signature protein that promotes cell-cycle re-entry by destabilizing the CDK inhibitor p27 through YAP-dependent regulation.

The paper shows how the cyanobacterial clock buffers noise through its core phosphorylation loop, using single-cell analysis and a stochastic model.

This study shows that p21Cip1/Waf1 promotes survival and relapse in TP53 wild-type NSCLC by enabling quiescence and protecting cells from chemotherapy.

This study uses a stochastic model to analyze time-resolved single-cell RNA-sequencing data and identifies global transcription regulation mechanisms through dynamical correlations.

The paper develops a stochastic agent-based model to study how gene expression noise and population dynamics interact to enable cellular decision-making, using Escherichia coli data to demonstrate its application.

The paper develops a method to model capture efficiency in scRNA-seq data to improve inference of transcriptome-wide burst kinetics.

The paper identifies three interdivision time correlation patterns in cell cycle timing using Bayesian inference on single-cell datasets, revealing hidden rhythmicity and disease-related differences.

The paper develops an agent-based framework to model gene expression noise in growing cell populations and shows it aligns with the chemical master equation under stochastic concentration homeostasis.

The paper introduces a method to predict single-cell metabolite distributions using Gaussian mixture models, bypassing stochastic simulations by leveraging enzyme kinetics and expression data.

bayNorm provides a Bayesian method for normalizing, imputing, and recovering gene expression in scRNA-seq data, improving accuracy and reducing batch effects.

This study uses a stochastic model to show that metabolic heterogeneity arises from enzyme expression and catalysis fluctuations, leading to metabolite distributions that differ from deterministic models.

The paper develops an agent-based framework to model gene expression noise in growing populations, showing how population dynamics and cell cycle fluctuations influence intrinsic and extrinsic noise.

This study shows how the circadian clock and environmental cycles influence cell size control and division timing in cyanobacteria.

The paper develops a stochastic model of bacterial cell growth linking gene expression, metabolism, and replication to analyze growth fluctuations and their consequences across nutrient conditions and antibiotic responses.

The paper presents a framework to predict cell size variations in populations and shows that population-level distributions differ from single-cell observations.

The paper develops a stochastic framework to model cell size histories in growing populations and shows that population growth rates exceed individual cell growth rates.