ReviewNPJ systems biology and applications2021
Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus.
Review in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- High-throughput generation and comparison of genome-scale metabolic models reveal strain-specific metabolic diversity in 439mSystems · 2026Article
- From Single Organisms to Communities: Modeling Methanotrophs and Their Satellites.Microorganisms · 2025Review
- Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.Cancer research · 2025Review
- Heterofunctional Cationic Polyester Dendrimers as Antibacterial Agents: The Role of Internal and External Charges.Biomacromolecules · 2025Article
- Moving from genome-scale to community-scale metabolic models for the human gut microbiome.Nature microbiology · 2025Review
- Logic programming-based Minimal Cut Sets reveal consortium-level therapeutic targets for chronic wound infections.NPJ systems biology and applications · 2024Article
- Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality.NPJ systems biology and applications · 2024Article
- Article
- Napthyridine-derived compounds as promising inhibitors forFrontiers in microbiology · 2023Article
- Genome-scale metabolic models consistently predictFrontiers in bioinformatics · 2023Article
- Comparison of Different Label-Free Raman Spectroscopy Approaches for the Discrimination of Clinical MRSA and MSSA Isolates.Microbiology spectrum · 2022Article
- Nutritional Interactions between Bacterial Species Colonising the Human Nasal Cavity: Current Knowledge and Future Prospects.Metabolites · 2022Review
- Rhea, the reaction knowledgebase in 2022.Nucleic acids research · 2022Article
- A New Perspective on the Antimicrobial Mechanism of Berberine Hydrochloride AgainstFrontiers in microbiology · 2022Article
- First Genome-Scale Metabolic Model ofMetabolites · 2021Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes. Furthermore, all models were quality-controlled using MEMOTE, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.
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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.