François Bertaux

This paper investigates how cell size and geometry in fission yeast vary under different growth conditions and finds that cell volume and growth rate follow a linear scaling under nitrogen and carbon titration.

The paper introduces ReacSight, a system for enhancing bioreactor arrays with automated measurements and reactive control, demonstrating its use in yeast cultures for gene expression control and competition assays.

The paper demonstrates that stochastic modeling of single-cell processes can predict population-level behavior of synthetic circuits without parameter adjustment.

This study investigates how growth rate and nutrient-specific factors influence gene expression in fission yeast, revealing coordinated and condition-specific regulation of metabolic enzymes.

This paper introduces a light-tunable differentiation system in yeast to create and control stable microbial consortia with custom functionalities.

MicroMator enables reactive microscopy experiments by integrating real-time data with machine learning for single-cell control and recombination.

The paper investigates how RNA Polymerase II initiation rates scale with cell size in fission yeast, showing that RNAPII is a limiting factor for transcriptional scaling.

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

The paper uses scRNA-seq and imaging to study gene expression heterogeneity in fission yeast under different environmental conditions, revealing how gene expression varies during growth and stress responses.

The paper models stochastic gene expression in growing cells to show how division rates affect noise in mRNA and protein levels, and how cell size regulation impacts circuit robustness.