With variants scattered all over the genome, it is difficult to associate those having weaker effects with a phenotype. We employ methods of constraint-based modeling to make hypotheses about the far-reaching effects that antibiotics have on the organism’s biochemical networks.
Genome-scale metabolic models have been instrumental in studying metabolic capabilities of organisms and identifying essential pathways and reactions that can serve as potential drug targets. By reconstructing the metabolic network of M. tuberculosis, we can simulate systems level consequences of genomic mutations.
Protein-protein interaction information can elucidate pathways and functions between two or more proteins. We are using this data to probe a set of clinically collected Mtb isolates to determine potential indicators of multiple drug resistance, as well as novel mechanisms of resistance.