Computational prediction of bacterial susceptibility to phages and genomic signatures of phage-bacteria coevolution.
Integrative machine learning to predict AMR and characterize pathogen genomics across clinical and environmental contexts.
Characterizing protein families and (patho)genomic features using molecular evolution and phylogenetics, and the MolEvolvR web application.
Discovering unique pathogenic sRNA in infected hosts
Studying stress response systems using molecular evolution and phylogenetics.
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Computational approaches to study host responses to infection and identify host-directed therapeutic interventions.
Summary We also studied host responses to M. tuberculosis infection using computational approaches:
characterizing the transcriptional response in infected macrophages under various small molecule perturbations using RNA-Seq analysis; understanding the dysregulation of lipid metabolism in M. tuberculosis-infected macrophages using dynamic Bayesian model and statistical analyses of heterogeneous single-cell populations.
Mathematical modeling of pathways involved in cell cycle regulation and differentiation