Predicting fitness in

Introduction: Methods: We assembled a large Mtb RNA expression compendium and applied this to infer a comprehensive Mtb transcriptional regulatory network and compute condition-specific transcription factor activity (TFA) profiles. Using transcriptomic and functional genomics data, we trained an interpretable machine learning model that predicts Mtb fitness from TFA profiles. Results: We demonstrated that a TFA-based model can predict Mtb growth arrest and growth resumption under hypoxia and reaeration using gene expression data alone. This model also directly elucidates the transcriptional programs driving these growth phenotypes. Discussion: These integrative network modeling and machine learning analyses enable the prediction of mycobacterial fitness across different environmental and genetic contexts with mechanistic detail. We envision these models can inform the future design of prognostic assays and therapeutic interventions that can cripple Mtb growth and survival to cure tuberculosis disease.