ArticleBMC systems biology2010
Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis.
Article in BMC systems biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 39 citations in OpenAlex.
- A constraint-based framework for exploring the impact of multireaction dependencies on metabolic functions.NPJ systems biology and applications · 2025Article
- The effective deficiency of biochemical networks.Scientific reports · 2023Article
- The hidden simplicity of metabolic networks is revealed by multireaction dependencies.Science advances · 2022Article
- A structural property for reduction of biochemical networks.Scientific reports · 2021Article
- A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks.PLoS computational biology · 2020Article
- Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions.BMC systems biology · 2018Article
- Antimycobacterial drug discovery using Mycobacteria-infected amoebae identifies anti-infectives and new molecular targets.Scientific reports · 2018Article
- Integration of Biomass Formulations of Genome-Scale Metabolic Models with Experimental Data Reveals Universally Essential Cofactors in Prokaryotes.Metabolic engineering · 2017Article
- Integration of Metabolic Modeling with Gene Co-expression Reveals Transcriptionally Programmed Reactions Explaining Robustness in Mycobacterium tuberculosis.Scientific reports · 2016Article
- PSAMM: A Portable System for the Analysis of Metabolic Models.PLoS computational biology · 2016Article
- Integrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis.PLoS computational biology · 2015Article
- Biofilm Formation Mechanisms of Pseudomonas aeruginosa Predicted via Genome-Scale Kinetic Models of Bacterial Metabolism.PLoS computational biology · 2015Article
- Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis.BMC systems biology · 2015Article
- Unraveling Mycobacterium tuberculosis genomic diversity and evolution in Lisbon, Portugal, a highly drug resistant setting.BMC genomics · 2014Article
- Systems level mapping of metabolic complexity in Mycobacterium tuberculosis to identify high-value drug targets.Journal of translational medicine · 2014Article
- Pharmacologic considerations in use and development of antituberculosis drugs.Cold Spring Harbor perspectives in medicine · 2014Review
- A multi-level multi-scale approach to study essential genes in Mycobacterium tuberculosis.BMC systems biology · 2013Article
- Network motifs provide signatures that characterize metabolism.Molecular bioSystems · 2013Article
- Studying the relationship between robustness against mutations in metabolic networks and lifestyle of organisms.TheScientificWorldJournal · 2013Article
- Systems-based approaches to probing metabolic variation within the Mycobacterium tuberculosis complex.PloS one · 2013Article
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDuring 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.
resultsTo 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.
conclusionsThe 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.
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