Ramon Grima

The paper develops a theoretical framework to analyze mRNA and protein fluctuations in cell lineages and validates it using E. coli data, showing how power spectrum features reveal gene expression mechanisms.

The paper develops a mathematical model to analyze cell size distribution in lineage data and validates it against experimental E. coli data.

The paper develops a stochastic model of transcriptional processes to derive analytical expressions for nascent and mature RNA fluctuations and their dependence on transcriptional parameters.

This paper uses neural networks to approximate non-Markov stochastic gene expression models and infer their parameters from noisy data.

The paper derives a closed-form solution for stochastic enzyme kinetics and shows transient bimodality in substrate distribution under specific conditions.

The paper presents a stochastic model of gene expression incorporating polymerase recruitment and pause release, showing it explains experimental data better than the telegraph model and predicts gene dosage compensation.

The paper quantifies how cell cycle variability affects mRNA abundance measurements and shows it can introduce errors in transcription rate inference.

The paper analyzes the time-dependent behavior of an auto-regulating gene under fast promoter switching, identifying six dynamical phases based on parameter space and stochastic dynamics.

The paper shows that standard stochastic models with Hill-type propensities are inaccurate for gene regulatory networks with fast promoter switching, especially when protein-DNA binding rates are high.