ArticleBMC systems biology2009
A systems biology framework for modeling metabolic enzyme inhibition of Mycobacterium tuberculosis.
Article in BMC systems biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 36 citations in OpenAlex.
- Multiscale Modeling in the Clinic: Drug Design and Development.Annals of biomedical engineering · 2016Review
- Article
- A systems chemical biology study of malate synthase and isocitrate lyase inhibition in Mycobacterium tuberculosis during active and NRP growth.Computational biology and chemistry · 2013Article
- Mechanistic systems modeling to guide drug discovery and development.Drug discovery today · 2013Review
- Parameter trajectory analysis to identify treatment effects of pharmacological interventions.PLoS computational biology · 2013Article
- Rapid countermeasure discovery against Francisella tularensis based on a metabolic network reconstruction.PloS one · 2013Article
- Systems biology of fungal infection.Frontiers in microbiology · 2012Article
- Modeling phenotypic metabolic adaptations of Mycobacterium tuberculosis H37Rv under hypoxia.PLoS computational biology · 2012Article
- Parameter adaptations during phenotype transitions in progressive diseases.BMC systems biology · 2011Article
- System-level strategies for studying the metabolism of Mycobacterium tuberculosis.Molecular bioSystems · 2010Article
- Development and analysis of an in vivo-compatible metabolic network of Mycobacterium tuberculosis.BMC systems biology · 2010Article
- Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis.BMC systems biology · 2010Article
- Tuberculosis: global approaches to a global disease.Current opinion in biotechnology · 2010Review
- Simulating plant metabolic pathways with enzyme-kinetic models.Plant physiology · 2010Article
- Systems biology approaches to understanding mycobacterial survival mechanisms.Drug discovery today. Disease mechanisms · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBecause metabolism is fundamental in sustaining microbial life, drugs that target pathogen-specific metabolic enzymes and pathways can be very effective. In particular, the metabolic challenges faced by intracellular pathogens, such as Mycobacterium tuberculosis, residing in the infected host provide novel opportunities for therapeutic intervention.
resultsWe developed a mathematical framework to simulate the effects on the growth of a pathogen when enzymes in its metabolic pathways are inhibited. Combining detailed models of enzyme kinetics, a complete metabolic network description as modeled by flux balance analysis, and a dynamic cell population growth model, we quantitatively modeled and predicted the dose-response of the 3-nitropropionate inhibitor on the growth of M. tuberculosis in a medium whose carbon source was restricted to fatty acids, and that of the 5'-O-(N-salicylsulfamoyl) adenosine inhibitor in a medium with low-iron concentration.
conclusionThe predicted results quantitatively reproduced the experimentally measured dose-response curves, ranging over three orders of magnitude in inhibitor concentration. Thus, by allowing for detailed specifications of the underlying enzymatic kinetics, metabolic reactions/constraints, and growth media, our model captured the essential chemical and biological factors that determine the effects of drug inhibition on in vitro growth of M. tuberculosis cells.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.