ArticleBMC systems biology2015
Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis.
Article in BMC systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 23 citations in OpenAlex.
- Isotopic tracing inVirulence · 2026Article
- Metabolomics and Artificial Intelligence (AI) assisted metabolomics in the diagnosis of non-tuberculous mycobacterial infections: progress and challenges.New microbes and new infections · 2026Review
- Topological criterion for robust perfect adaptation of reaction fluxes in biological networks.iScience · 2025Article
- Mass Spectrometry-Based Proteomic and Metabolomic Profiling of Serum Samples for Discovery and Validation of Tuberculosis Diagnostic Biomarker Signature.International journal of molecular sciences · 2022Article
- Using Omics to Study Leprosy, Tuberculosis, and Other Mycobacterial Diseases.Frontiers in cellular and infection microbiology · 2022Review
- A systematic evaluation of Mycobacterium tuberculosis Genome-Scale Metabolic Networks.PLoS computational biology · 2020Article
- Review
- Current status and applications of genome-scale metabolic models.Genome biology · 2019Review
- Rewiring of Metabolic Network inFrontiers in microbiology · 2019Article
- Modeling Host-Pathogen Interaction to Elucidate the Metabolic Drug Response of IntracellularFrontiers in cellular and infection microbiology · 2019Article
- Metabolic principles of persistence and pathogenicity in Mycobacterium tuberculosis.Nature reviews. Microbiology · 2018Review
- Updated and standardized genome-scale reconstruction of Mycobacterium tuberculosis H37Rv, iEK1011, simulates flux states indicative of physiological conditions.BMC systems biology · 2018Article
- Growth medium-dependent antimicrobial activity of early stage MEP pathway inhibitors.PloS one · 2018Article
- Metabolic adaptation of two in silico mutants of Mycobacterium tuberculosis during infection.BMC systems biology · 2017Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMycobacterium tuberculosis (MTB) is the causal agent of the disease tuberculosis (TB). Metabolic adaptations are thought to be critical to the survival of MTB during pathogenesis. Computational tools that can be used to study MTB metabolism in silico and prioritize resource-intensive experimental work could significantly accelerate research.
resultsWe have developed E-Flux-MFC, an enhancement of our original E-Flux method that enables the prediction of changes in the production of external and internal metabolites corresponding to gene expression measurements. We have used this method to simulate the changes in the metabolic state of Mycobacterium tuberculosis (MTB). We have validated the accuracy of E-Flux-MFC for predicting changes in lipids and metabolites during a hypoxia time course using previously published metabolomics and transcriptomics data. We have further validated the accuracy of the method for predicting changes in MTB lipids following the deletion and induction of two well-studied transcription factors (TFs). We have applied the method to predict the metabolic impact of the induction of each of the approximately 180 MTB TFs using a previously generated and publically available expression data set.
conclusionsE-flux-MFC can be used to study global changes in MTB metabolites from gene expression data associated with environmental and genetic perturbations. The application of this method to a data set of MTB TF perturbations provides a resource for studying the large number of TFs whose functions remain unknown. Most TFs impact metabolites indirectly through the propagation of gene expression changes through the regulatory network rather than through their direct regulons. E-Flux-MFC is also applicable to any organism for which accurate metabolic models are available.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.