We combine computational and experimental network biology approaches to understand the molecular interactions that determine infection and treatment fate in tuberculosis.
Minimizing Side-Effects
Adverse drug interactions of side-effects are a major cause of hospital admissions and death. This is exacerbated because interactions are often detected after drugs have reached market. To enable safer co-prescriptions, we aim to understand the mechanisms of these side-effects and to predict adverse drug interactions.
Optimizing in vivo Efficacy
Multidrug therapy reduces drug resistance in infections like TB. However, design is complicated by the astronomical number of possible drug combinations. We can predict drug synergy and antagonism as they act to kill the bacteria that causes TB. We aim to use this tool to study how the host changes multidrug efficacy.
Discovering Novel Drug Targets
To combat rising rates of drug resistance, identifying drug targets with diverse mechanisms of action is urgently needed. To identify candidate virulence-targeting mechanisms, we aim to dissect to identify bacterial factors that control TB disease progression in in vivo-relevant infection settings.
Designing Host-Directed Interventions
Host factors are especially attractive targets for drug discovery as their indirect activity may be harder for pathogens to evade. We aim to dissect to identify host factors that induce TB control in in vivo-relevant infection settings.