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Inside granulomas, the pathogen\n                    <jats:named-content content-type=\"genus-species\">Mycobacterium tuberculosis<\/jats:named-content>\n                    may enter a metabolically inactive state that is less susceptible to antibiotics. Understanding\n                    <jats:named-content content-type=\"genus-species\">M. tuberculosis<\/jats:named-content>\n                    metabolism within granulomas could contribute to reducing the lengthy treatment required for tuberculosis and provide additional targets for new drugs. Two key adaptations of\n                    <jats:named-content content-type=\"genus-species\">M. tuberculosis<\/jats:named-content>\n                    are a nonreplicating phenotype and accumulation of lipid inclusions in response to hypoxic conditions. To explore how these adaptations influence granuloma-scale outcomes\n                    <jats:italic>in vivo<\/jats:italic>\n                    , we present a multiscale\n                    <jats:italic>in silico<\/jats:italic>\n                    model of granuloma formation in tuberculosis. The model comprises host immunity,\n                    <jats:named-content content-type=\"genus-species\">M. tuberculosis<\/jats:named-content>\n                    metabolism,\n                    <jats:named-content content-type=\"genus-species\">M. tuberculosis<\/jats:named-content>\n                    growth adaptation to hypoxia, and nutrient diffusion. We calibrated our model to\n                    <jats:italic>in vivo<\/jats:italic>\n                    data from nonhuman primates and rabbits and apply the model to predict\n                    <jats:named-content content-type=\"genus-species\">M. tuberculosis<\/jats:named-content>\n                    population dynamics and heterogeneity within granulomas. We found that bacterial populations are highly dynamic throughout infection in response to changing oxygen levels and host immunity pressures. Our results indicate that a nonreplicating phenotype, but not lipid inclusion formation, is important for long-term\n                    <jats:named-content content-type=\"genus-species\">M. tuberculosis<\/jats:named-content>\n                    survival in granulomas. We used virtual\n                    <jats:named-content content-type=\"genus-species\">M. tuberculosis<\/jats:named-content>\n                    knockouts to predict the impact of both metabolic enzyme inhibitors and metabolic pathways exploited to overcome inhibition. Results indicate that knockouts whose growth rates are below \u223c66% of the wild-type growth rate in a culture medium featuring lipid as the only carbon source are unable to sustain infections in granulomas. By mapping metabolite- and gene-scale perturbations to granuloma-scale outcomes and predicting mechanisms of sterilization, our method provides a powerful tool for hypothesis testing and guiding experimental searches for novel antituberculosis interventions.\n                  <\/jats:p>","DOI":"10.1128\/iai.01438-15","type":"journal-article","created":{"date-parts":[[2016,3,14]],"date-time":"2016-03-14T22:23:46Z","timestamp":1457994226000},"page":"1650-1669","update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":49,"title":["Multiscale Model of Mycobacterium tuberculosis Infection Maps Metabolite and Gene Perturbations to Granuloma Sterilization Predictions"],"prefix":"10.1128","volume":"84","author":[{"given":"Elsje","family":"Pienaar","sequence":"first","affiliation":[{"name":"Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, USA"},{"name":"Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William M.","family":"Matern","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jennifer J.","family":"Linderman","sequence":"additional","affiliation":[{"name":"Department of Chemical Engineering, University of Michigan, Ann Arbor, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joel S.","family":"Bader","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and High-Throughput Biology Center, Johns Hopkins University, Baltimore, Maryland, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Denise E.","family":"Kirschner","sequence":"additional","affiliation":[{"name":"Department of Microbiology and Immunology, University of Michigan Medical School, Ann Arbor, Michigan, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"235","reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.disamonth.2013.10.006"},{"key":"e_1_3_3_3_2","volume-title":"Global tuberculosis report","author":"WHO","year":"2014","unstructured":"WHO. 2014. 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