Application of a quantitative framework to improve the accuracy of a bacterial infection model.
Pseudomonas aeruginosa
calprotectin
cystic fibrosis
epithelial cell model
preclinical model
Journal
Proceedings of the National Academy of Sciences of the United States of America
ISSN: 1091-6490
Titre abrégé: Proc Natl Acad Sci U S A
Pays: United States
ID NLM: 7505876
Informations de publication
Date de publication:
09 05 2023
09 05 2023
Historique:
medline:
3
5
2023
pubmed:
1
5
2023
entrez:
1
5
2023
Statut:
ppublish
Résumé
Laboratory models are critical to basic and translational microbiology research. Models serve multiple purposes, from providing tractable systems to study cell biology to allowing the investigation of inaccessible clinical and environmental ecosystems. Although there is a recognized need for improved model systems, there is a gap in rational approaches to accomplish this goal. We recently developed a framework for assessing the accuracy of microbial models by quantifying how closely each gene is expressed in the natural environment and in various models. The accuracy of the model is defined as the percentage of genes that are similarly expressed in the natural environment and the model. Here, we leverage this framework to develop and validate two generalizable approaches for improving model accuracy, and as proof of concept, we apply these approaches to improve models of
Identifiants
pubmed: 37126703
doi: 10.1073/pnas.2221542120
pmc: PMC10175807
doi:
Substances chimiques
Culture Media
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
e2221542120Subventions
Organisme : HHS | National Institutes of Health (NIH)
ID : K99DE031018
Organisme : HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID)
ID : R01AI101171
Organisme : HHS | NIH | NIAID | Division of Microbiology and Infectious Diseases, National Institute of Allergy and Infectious Diseases (DMID)
ID : R01AI127793
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