{"database":"biostudies-other","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["4"],"submitter":["Nicolas Le Novère"],"journal":["BMC systems biology"],"pagination":["160"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/MODEL1507180018"],"repository":["biostudies-other"],"additional_accession":["21092312"],"pubmed_authors":["Nicolas Le Novère"]},"is_claimable":false,"name":"Fang2010 - Genome-scale metabolic network of Mycobacterium tuberculosis (iNJ661m)","description":"<notes xmlns=\"http://www.sbml.org/sbml/level3/version1/core\">      <body xmlns=\"http://www.w3.org/1999/xhtml\">        <div class=\"dc:title\">Fang2010 - Genome-scale metabolic network ofMycobacterium tuberculosis (iNJ661m)</div><div class=\"dc:bibliographicCitation\">  <p>This model is described in the article:</p>  <div class=\"bibo:title\">    <a href=\"http://identifiers.org/pubmed/21092312\" title=\"Access to this publication\">Development and analysis of    an in vivo-compatible metabolic network of Mycobacterium    tuberculosis.</a>  </div>  <div class=\"bibo:authorList\">Fang X, Wallqvist A, Reifman  J.</div>  <div class=\"bibo:Journal\">BMC Syst Biol 2010; 4: 160</div>  <p>Abstract:</p>  <div class=\"bibo:abstract\">    <p>BACKGROUND: During infection, Mycobacterium tuberculosis    confronts a generally hostile and nutrient-poor in vivo host    environment. Existing models and analyses of M. tuberculosis    metabolic networks are able to reproduce experimentally    measured cellular growth rates and identify genes required for    growth in a range of different in vitro media. However, these    models, under in vitro conditions, do not provide an adequate    description of the metabolic processes required by the pathogen    to infect and persist in a host. RESULTS: To better account for    the metabolic activity of M. tuberculosis in the host    environment, we developed a set of procedures to systematically    modify an existing in vitro metabolic network by enhancing the    agreement between calculated and in vivo-measured gene    essentiality data. After our modifications, the new in vivo    network contained 663 genes, 838 metabolites, and 1,049    reactions and had a significantly increased sensitivity (0.81)    in predicted gene essentiality than the in vitro network    (0.31). We verified the modifications generated from the purely    computational analysis through a review of the literature and    found, for example, that, as the analysis suggested, lipids are    used as the main source for carbon metabolism and oxygen must    be available for the pathogen under in vivo conditions.    Moreover, we used the developed in vivo network to predict the    effects of double-gene deletions on M. tuberculosis growth in    the host environment, explore metabolic adaptations to life in    an acidic environment, highlight the importance of different    enzymes in the tricarboxylic acid-cycle under different    limiting nutrient conditions, investigate the effects of    inhibiting multiple reactions, and look at the importance of    both aerobic and anaerobic cellular respiration during    infection. CONCLUSIONS: The network modifications we    implemented suggest a distinctive set of metabolic conditions    and requirements faced by M. tuberculosis during host infection    compared with in vitro growth. Likewise, the double-gene    deletion calculations highlight the importance of specific    metabolic pathways used by the pathogen in the host    environment. The newly constructed network provides a    quantitative model to study the metabolism and associated drug    targets of M. tuberculosis under in vivo conditions.</p>  </div></div><div class=\"dc:publisher\">  <p>This model is hosted on   <a href=\"http://www.ebi.ac.uk/biomodels/\">BioModels Database</a>  and identified by:   <a href=\"http://identifiers.org/biomodels.db/MODEL1507180018\">MODEL1507180018</a>.</p>  <p>To cite BioModels Database, please use:   <a href=\"http://identifiers.org/pubmed/20587024\" title=\"Latest BioModels Database publication\">BioModels Database:  An enhanced, curated and annotated resource for published  quantitative kinetic models</a>.</p></div><div class=\"dc:license\">  <p>To the extent possible under law, all copyright and related or  neighbouring rights to this encoded model have been dedicated to  the public domain worldwide. Please refer to   <a href=\"http://creativecommons.org/publicdomain/zero/1.0/\" title=\"Access to: CC0 1.0 Universal (CC0 1.0), Public Domain Dedication\">CC0  Public Domain Dedication</a> for more information.</p></div></body>    </notes>","dates":{"release":"2015-07-18T00:00:00Z","modification":"2025-07-15T09:10:52.349Z","creation":"2025-03-30T21:57:44.083Z"},"accession":"MODEL1507180018","cross_references":{"pubmed":["21092312"],"mamo":["MAMO_0000009"],"unknown":["null"]}}