B. B. Aldridge

This study uses time-lapse imaging and modeling to show that Mycobacterium tuberculosis grows linearly with high cell-to-cell variability, differing from exponential growth in model organisms.

This study systematically measures drug interactions in Mycobacterium abscessus to identify synergistic combinations and highlight strain-specific responses.

This study shows that antibiotic interactions vary with growth medium, with lung mimetic medium better predicting in vivo outcomes for Klebsiella pneumoniae.

The paper identifies resR as a target of positive selection in Mtb clinical isolates, linking its mutations to antibiotic resilience and treatment failure.

The paper uses pairwise drug response data to train machine learning models predicting effective tuberculosis drug combinations, revealing design principles for rational combination therapy.

This study develops a C25-modified rifamycin derivative, 5j, which resists enzymatic inactivation by ArrMab and shows potent antimicrobial activity against M. abscessus in vitro and in macrophages.

This study uses ABPP to identify cell wall synthesis inhibitors targeting Mycobacterium tuberculosis, showing that active compounds inhibit serine hydrolases involved in cell wall and lipid metabolism.

The paper systematically measures drug combinations to predict tuberculosis treatment outcomes using in vitro models and machine learning.