Antimicrobial/antifungal resistance, biophysics, Candida/pathogenic yeast, machine learning, magnetobiology, mathematical/quantitative/computational/systems/synthetic biology, microbial evolution experiments, nongenetic variability, stochastic simulations, synthetic gene networks.
Utilizing biophysical modeling, genetically engineered budding yeast (Saccharomyces cerevisiae) habouring synthetic gene networks, and pathogenic fungi (Candida species) to investigate nongenetic drug resistance and the evolution of antimicrobial tolerance/resistance.
S. Rasouli Koohi, S.A. Shankarnarayan, C. M. Galon, and D.A. Charlebois. Biomedicines, 11: 898 (2023).
J. Guthrie and D.A. Charlebois. Physical Biology, 19: 066002 (2022).
K.S. Farquhar, S. Rasouli Koohi, and D.A. Charlebois. Bioessays, 43: 2100043 (2021).
Training machine learning models to quickly and accurately identify human fungal pathogens and to detect drug resistance.
S.A. Shankarnarayan and D.A. Charlebois. Medical Mycology, 62: myad134 (2024).
S.A. Shankarnarayan, J. Guthrie, and D.A. Charlebois. The Global Antimicrobial Resistance Epidemic – Innovative Approaches and Cutting-Edge Solutions, Guillermo Téllez (Ed.), ISBN: 978-1-80356-042-7 (2022).
Developing spatiotemporal algorithms, building bioelectromagnetic devices, and performing experiments on yeast to study the effects of electromagnetic fields on cellular growth, division, and gene expression, as well as fungal mat formation.
R. Hall and D.A. Charlebois. In Silico Biol., 14: 53-69 (2021).