@techreport{Leonidou2023e,
  author       = {Leonidou, Nantia and Renz, Alina and Winnerling, Benjamin and Grekova,
    Anastasiia and Grein, Fabian and Dr\"ager, Andreas},
  title        = {{Genome-scale metabolic model of \emph{Staphylococcus epidermidis} ATCC 12228
    matches \emph{in vitro} conditions}},
  elocation-id = {2023.12.19.572329},
  year         = {2023},
  doi          = {10.1101/2023.12.19.572329},
  publisher    = {Cold Spring Harbor Laboratory},
  url          = {https://www.biorxiv.org/content/early/2023/12/20/2023.12.19.572329},
  eprint       = {https://www.biorxiv.org/content/early/2023/12/20/2023.12.19.572329.full.pdf},
  institution  = {bioRxiv},
  type         = {Preprint},
  abstract     = {Staphylococcus epidermidis a commensal bacterium inhabiting collagen-rich areas,
    like human skin, has gained significance due toits probiotic potential in the nasal microbiome
    and as a leading cause of nosocomial infections. While infrequently leading to severe
    illnesses, S. epidermidis exerts a significant influence, particularly in its close association
    with implant-related infections and its role as a classic opportunistic biofilm former.
    Understanding its opportunistic nature is crucial for developing novel therapeutic strategies,
    addressing both its beneficial and pathogenic aspects, and alleviating the burdens it imposes
    on patients and healthcare systems. Here, we employ genome-scale metabolic modeling as a
    powerful tool to elucidate the lifestyle and capabilities of S. epidermidis. We created a
    comprehensive computational resource for understanding the organism's growth conditions within
    diverse habitats by reconstructing and analyzing a manually curated and experimentally 
    validated metabolic model. The final network, iSep23, incorporates 1,415 reactions, 1,051
    metabolites, and 705 genes, adhering to established community standards and modeling
    guidelines. Benchmarking with the MEMOTE test suite yields a high score, highlighting the
    model's high semantic quality. Following the FAIR data principles, iSep23 becomes a valuable
    and publicly accessible asset for subsequent studies. Growth simulations and carbon source
    utilization predictions align with experimental results, showcasing the model's predictive
    power. This metabolic model advances our understanding of S. epidermidis as a commensal and
    potential probiotic and enhances insights into its opportunistic pathogenicity against other
    microorganisms.Competing Interest StatementThe authors have declared no competing interest.},
}