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Updated: Apr 3, 2026
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Sample Preparation of Mycobacterium tuberculosis Extracts for Nuclear Magnetic Resonance Metabolomic Studies
Published on: September 3, 2012
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Metabolic modeling predicts metabolite changes in Mycobacterium tuberculosis.
Christopher D Garay1, Jonathan M Dreyfuss2,3, James E Galagan4,5,6
1Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA. cgaray@bu.edu.
BMC Systems Biology
|September 18, 2015
Summary
A new computational tool, E-Flux-MFC, predicts metabolic changes in Mycobacterium tuberculosis (MTB) using gene expression data. This tool aids in understanding tuberculosis pathogenesis and accelerates research by prioritizing experiments.
Area of Science:
- Microbiology
- Computational Biology
- Systems Biology
Background:
- Mycobacterium tuberculosis (MTB) causes tuberculosis (TB).
- Metabolic adaptations are crucial for MTB survival during infection.
- In silico tools can accelerate MTB research by predicting metabolic behavior.
Purpose of the Study:
- To develop and validate E-Flux-MFC, a computational method for predicting metabolic changes in MTB.
- To simulate and analyze metabolic state alterations in MTB in response to various perturbations.
Main Methods:
- E-Flux-MFC enhances the original E-Flux method.
- It predicts metabolite production changes based on gene expression data.
- Validation involved hypoxia time course data and transcription factor perturbation experiments.
Main Results:
- E-Flux-MFC accurately predicts changes in MTB lipids and metabolites.
- The method's accuracy was confirmed using published metabolomics and transcriptomics data.
- Simulations predicted the metabolic impact of inducing approximately 180 MTB transcription factors.
Conclusions:
- E-Flux-MFC enables the study of global metabolic shifts in MTB from gene expression data.
- It serves as a resource for investigating numerous unknown transcription factor functions.
- Most transcription factors influence metabolites indirectly via gene expression networks.
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