Renz2021 - Collection of 13 SBML L3V1 constraint-based models (FBC Version 2) of Staphylococcus aureus

  public model
Model Identifier
MODEL2007150001
Short description
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 Mᴇᴍᴏᴛᴇ, 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.
Format
COMBINE archive (0.1)
Related Publication
  • Curating and Comparing 114 Strain-Specific Genome-Scale Metabolic Models of Staphylococcus aureus
  • Alina Renz, Andreas Dräger
  • Preprints , 4/ 2021 , DOI: 10.20944/preprints202104.0244.v1
  • 1 Computational Systems Biology of Infections and Antimicrobial-Resistant Pathogens, Institute for Bioinformatics and Medical Informatics (IBMI), University of Tübingen, 72076 Tübingen, Germany 2 Department of Computer Science, University of Tübingen, 72076 Tübingen, Germany 3 Cluster of Excellence ‘Controlling Microbes to Fight Infections,’ University of Tübingen, 72076 Tübingen, Germany 4 German Center for Infection Research (DZIF), Partner site Tübingen, 72076 Tübingen, Germany
  • 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 Mᴇᴍᴏᴛᴇ, 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.
Contributors
Andreas Dräger, Kausthubh Ramachandran

Metadata information


Curation status
Non-curated

Modelling approach(es)

Name Description Size Actions

Model files

Staphyloccus_aureus_(Lee_et_al._2009).omex Renz2020 (Staphylococcus aureus model collection): OMEX file with 13 files in SBML Level 3 Version 1 with FBC Version 2 format by Lee et al. 2009 1.46 MB Preview | Download

  • Model originally submitted by : Andreas Dräger
  • Submitted: 13-Apr-2021 08:24:16
  • Last Modified: 13-Apr-2021 08:24:16
Revisions
  • Version: 3 public model Download this version
    • Submitted on: 13-Apr-2021 08:24:16
    • Submitted by: Kausthubh Ramachandran
    • With comment: Updated model submission name and short description as per the BioModels submission guidelines