ArticlePLoS computational biology2015
Integrated Modeling of Gene Regulatory and Metabolic Networks in Mycobacterium tuberculosis.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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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.
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Who cites it
39 citing papers in PubMed.
- A Multi-Scale Computational Framework for Anti-Persister Tuberculosis Drug Discovery.Chemical biology & drug design · 2026Review
- Divergent transcriptional regulation of redox-homeostasis and permeability modulate rifampicin tolerance and sensitivity in Mycobacterium tuberculosis.Nature communications · 2025Article
- Chemical genetic interactions elucidate pathways controlling tuberculosis antibiotic efficacy during infection.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Metabolic Objectives and Trade-Offs: Inference and Applications.Metabolites · 2025Review
- RBI: a novel algorithm for regulatory-metabolic network model in designing the optimal mutant strain.PeerJ. Computer science · 2025Article
- Deciphering and designing microbial communities by genome-scale metabolic modelling.Computational and structural biotechnology journal · 2024Review
- Catalase activity deficiency sensitizes multidrug-resistant Mycobacterium tuberculosis to the ATP synthase inhibitor bedaquiline.Nature communications · 2024Article
- Exploring the metabolic profile of A. baumannii for antimicrobial development using genome-scale modeling.PLoS pathogens · 2024Article
- Moxifloxacin-Mediated Killing of Mycobacterium tuberculosis Involves Respiratory Downshift, Reductive Stress, and Accumulation of Reactive Oxygen Species.Antimicrobial agents and chemotherapy · 2022Article
- Genome-scale metabolic network models: from first-generation to next-generation.Applied microbiology and biotechnology · 2022Review
- Construction of Multiscale Genome-Scale Metabolic Models: Frameworks and Challenges.Biomolecules · 2022Review
- Gradients in gene essentiality reshape antibacterial research.FEMS microbiology reviews · 2022Review
- Pre-Clinical Tools for Predicting Drug Efficacy in Treatment of Tuberculosis.Microorganisms · 2022Review
- Quantitative prediction of conditional vulnerabilities in regulatory and metabolic networks using PRIME.NPJ systems biology and applications · 2021Article
- Next-Generation Genome-Scale Metabolic Modeling through Integration of Regulatory Mechanisms.Metabolites · 2021Review
- Article
- PathExt: a general framework for path-based mining of omics-integrated biological networks.Bioinformatics (Oxford, England) · 2021Article
- Transcriptional regulator-induced phenotype screen reveals drug potentiators in Mycobacterium tuberculosis.Nature microbiology · 2021Article
- History and Future Perspectives on the Discipline of Quantitative Systems Pharmacology Modeling and Its Applications.Frontiers in physiology · 2021Review
- Genetic Diversity inAnnual review of genetics · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Mycobacterium tuberculosis (MTB) is the causative bacterium of tuberculosis, a disease responsible for over a million deaths worldwide annually with a growing number of strains resistant to antibiotics. The development of better therapeutics would greatly benefit from improved understanding of the mechanisms associated with MTB responses to different genetic and environmental perturbations. Therefore, we expanded a genome-scale regulatory-metabolic model for MTB using the Probabilistic Regulation of Metabolism (PROM) framework. Our model, MTBPROM2.0, represents a substantial knowledge base update and extension of simulation capability. We incorporated a recent ChIP-seq based binding network of 2555 interactions linking to 104 transcription factors (TFs) (representing a 3.5-fold expansion of TF coverage). We integrated this expanded regulatory network with a refined genome-scale metabolic model that can correctly predict growth viability over 69 source metabolite conditions and predict metabolic gene essentiality more accurately than the original model. We used MTBPROM2.0 to simulate the metabolic consequences of knocking out and overexpressing each of the 104 TFs in the model. MTBPROM2.0 improves performance of knockout growth defect predictions compared to the original PROM MTB model, and it can successfully predict growth defects associated with TF overexpression. Moreover, condition-specific models of MTBPROM2.0 successfully predicted synergistic growth consequences of overexpressing the TF whiB4 in the presence of two standard anti-TB drugs. MTBPROM2.0 can screen in silico condition-specific transcription factor perturbations to generate putative targets of interest that can help prioritize future experiments for therapeutic development efforts.
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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.