Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions

Supplemental material

  • Supplemental file 1 -

    Table S1. GranSim parameters. Table S2. Cross-tabulations of mutants in GSMN-TB versus sMtb. Fig. S1. Schematic showing the computation of bacterial growth and response to environmental nutrients. Fig. S2. Representative outcomes of GranSim 150 days postinfection when TNF-α, IL-10, or IFN-γ is removed from the onset of infection. Fig. S3. Despite a trend toward slower bacterial growth, the majority of bacteria remain in the faster-growth phenotype. Fig. S4. Bacterial growth phenotype changes with time and reflects bacterial load. Fig. S5. Defining attenuated knockout mutants. Table S3. Mutants attenuated for growth as identified by hypothesis testing using the output from GranSim-FBM. Fig. S6. The bypass metabolic network when enolase (eno) is knocked out. Fig. S7. Bypass network for ctaB. Fig. S8. Bypass network for gap. Fig. S9. Bypass network for nuoA. Fig. S10. Bypass network for tpi. Fig. S11. Bypass network for pgk. Fig. S12. Bypass network for glcB. Fig. S13. Displayed is a merge of the seven bypass networks featured above.

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