Quantifying antibiotic susceptibility and inoculum effects using transient dynamics of Pseudomonas aeruginosa

Sarah Sundius, Jennifer Farrell, Kelly L. Eick, Rachel Kuske, Sam P. Brown

Open source

DOI: 10.1371/journal.pcbi.1014827
Published: 2026-10-05
Container: PLOS Computational Biology
Publisher: Public Library of Science (PLoS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1371/journal.pcbi.1014827,
  title = {Quantifying antibiotic susceptibility and inoculum effects using transient dynamics of Pseudomonas aeruginosa},
  author = {Sarah Sundius and Jennifer Farrell and Kelly L. Eick and Rachel Kuske and Sam P. Brown},
  year = {2026},
  journal = {PLOS Computational Biology},
  doi = {10.1371/journal.pcbi.1014827},
  url = {https://doi.org/10.1371/journal.pcbi.1014827}
}

RIS

TY  - JOUR
TI  - Quantifying antibiotic susceptibility and inoculum effects using transient dynamics of Pseudomonas aeruginosa
AU  - Sarah Sundius
AU  - Jennifer Farrell
AU  - Kelly L. Eick
AU  - Rachel Kuske
AU  - Sam P. Brown
PY  - 2026
JO  - PLOS Computational Biology
DO  - 10.1371/journal.pcbi.1014827
UR  - https://doi.org/10.1371/journal.pcbi.1014827
ER  -

APA

Sundius, S., Farrell, J., Eick, K. L., Kuske, R., & Brown, S. P. (2026). Quantifying antibiotic susceptibility and inoculum effects using transient dynamics of Pseudomonas aeruginosa. PLOS Computational Biology. https://doi.org/10.1371/journal.pcbi.1014827

Source records

crossref · retrieved 2026-11-10T20:59:58.039Z