Ali Bootwala

Mycobacterium tuberculosis (Mtb) is a leading cause of death from infectious disease globally. As a hallmark model of biological heterogeneity and an obligate pathogen evolutionally adapted to the human host, the study of Mtb and therefore the rational design of novel drug treatment regimens presents a unique challenge. Preclinical evaluation of drug regimen efficacy is obscured from clinical translation due to variability in metabolic and immune landscape at the organismal level, variation in granuloma microenvironment leading to formation of distinct tissue niches that modulate the bacterial growth environments, and adaptation of bacteria subpopulations to the discrete niches they are found in. My work will aim to utilize efficiently generated, feature-rich, in vitro datasets to build predictive models of in vivo drug regimen efficacy while identifying aspects of disease immunobiology that are critical for successful clinical translation of preclinical experiments.