Wellcome Trust ISSF: Best Practice in Mathematical Model Parametrisation for PredictiveMedicine (1516ISSFFEL9)

Jabbari, Sara (Principal Investigator)

Mathematics

Short title	Wellcome Trust ISSF: Best Practice in Mathematical Model Parametrisation for PredictiveMedicine (1516ISSFFEL9)
Status	Finished
Effective start/end date	20/06/16 → 19/08/16

Funding

THE WELLCOME TRUST

5 Article

Mathematical model predicts anti-adhesion–antibiotic–debridement combination therapies can clear an antibiotic resistant infection
Roberts, P., Huebinger, R. M., Keen, E., Krachler, A. M. & Jabbari, S., 23 Jul 2019, In: PLoS Computational Biology. 15, 7, e1007211.
Research output: Contribution to journal › Article › peer-review

Open Access
File
7 Citations (Scopus)

118 Downloads (Pure)
Synergistic impacts of organic acids and pH on growth of Pseudomonas aeruginosa: a comparison of parametric and Bayesian non-parametric methods to model growth
Bushell, F., Tonner, P., Jabbari, S., Schmid, A. & Lund, P., 8 Jan 2019, In: Frontiers in Microbiology. 09, 3196.
Research output: Contribution to journal › Article › peer-review

Open Access
File
16 Citations (Scopus)

194 Downloads (Pure)
Mathematical modelling of the antibiotic-induced morphological transition of Pseudomonas aeruginosa
Spalding, C., Keen, E., Smith, D. J., Krachler, A-M. & Jabbari, S., 26 Feb 2018, In: PLoS Computational Biology. 14, 2, 28 p., e1006012.
Research output: Contribution to journal › Article › peer-review

Open Access
File
9 Citations (Scopus)

241 Downloads (Pure)