Daniel A. Charlebois

This study quantifies antifungal drug tolerance in Candidozyma auris by combining physics-based diffusion models with experimental data, showing it is strain-, drug-, and dose-dependent.

The paper models how antifungal tolerance and resistance in Candidozyma auris depend on infection load, drug treatment, and host immunity.

The paper trains AI models to identify fungal pathogens from microscopy images and uses explainable AI to reveal the features influencing predictions.

This study develops a 3D-printed magnetic field exposure device to investigate the effects of static magnetic fields on yeast and fungal growth, finding that magnetic fields slow 2D yeast mat expansion but not liquid growth.

This paper uses machine learning to identify Candida yeast species from microscopy images, finding that InceptionV3 performs best on processed images.

The paper investigates how heating and cooling affect gene network function in yeast, finding nonmonotone expression trends and temperature-induced bimodality in synthetic circuits.

This study uses mathematical models and experiments to show that a mutation in a regulator gene can lead to a permanent decrease in population fitness due to interactions with gene regulatory network dynamics.