@techreport{Anton2023,
  author      = {Anton, Mihail and Almaas, Eivind and Benfeitas, Rui and Benito-Vaquerizo, Sara and Blank, 
    Lars M. and Dr\"ager, Andreas and Hancock, John M. and Kittikunapong, Cheewin and K\"onig, Matthias
    and Li, Feiran and Liebal, Ulf W. and Lu, Hongzhong and Ma, Hongwu and Mahadevan, Radhakrishnan and
    Mardinoglu, Adil and Nielsen, Jens and Nogales, Juan and Pagni, Marco and Papin, Jason A. and Patil,
    Kiran Raosaheb and Price, Nathan D. and Robinson, Jonathan L. and S\'anchez, Benjam{\'\i}n J. and
    Suarez Diez, Maria and Sulheim, Snorre and Svensson, L. Thomas and Teusink, Bas and Vongsangnak,
    Wanwipa and Wang, Hao and Zeidan, Ahmad A. and Kerkhoven, Eduard J.},
  title        = {{standard-GEM: standardization of open-source genome-scale metabolic models}},
  elocation-id = {2023.03.21.512712},
  year         = {2023},
  doi          = {10.1101/2023.03.21.512712},
  publisher    = {Cold Spring Harbor Laboratory},
  URL          = {https://www.biorxiv.org/content/early/2023/03/23/2023.03.21.512712},
  eprint       = {https://www.biorxiv.org/content/early/2023/03/23/2023.03.21.512712.full.pdf},
  journal      = {bioRxiv},
  abstract     = {The field of metabolic modelling at the genome-scale continues to grow with more models
    being created and curated. This comes with an increasing demand for adopting common principles
    regarding transparency and versioning, in addition to standardisation efforts regarding file formats,
    annotation and testing. Here, we present a standardised template for git-based and GitHub-hosted
    genome-scale metabolic models (GEMs) supporting both new models and curated ones, following FAIR
    principles (findability, accessibility, interoperability, and reusability), and incorporating
    best-practices. Standard-GEM facilitates the reuse of GEMs across web services and platforms in the
    metabolic modelling field and enables automatic validation of GEMs. The use of this template for new
    models, and its adoption for existing ones, paves the way for increasing model quality, openness, and
    accessibility with minimal effort.Competing Interest StatementThe authors have declared no competing
    interest.},
}