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Wellcome Trust ISSF: Best Practice in Mathematical Model Parametrisation for PredictiveMedicine (1516ISSFFEL9)
Jabbari, Sara
(Principal Investigator)
Mathematics
Overview
Fingerprint
Research output
(5)
Project Details
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
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Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
Antibiotics
Mathematics
100%
Therapy
Mathematics
69%
Bacteria
Mathematics
61%
Adhesion
Mathematics
59%
mathematical models
Agriculture & Biology
58%
Pseudomonas aeruginosa
Medicine & Life Sciences
53%
therapeutics
Agriculture & Biology
53%
antibiotics
Agriculture & Biology
51%
Research output
Research output per year
2018
2018
2019
2019
5
Article
Research output per year
Research output per year
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
Antibiotics
100%
Therapy
77%
therapy
72%
antibiotics
67%
adhesion
66%
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
Pseudomonas aeruginosa
100%
Acids
81%
Growth
54%
propionic acid
17%
Logistic Models
17%
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
Antibiotics
100%
Mathematical Modeling
67%
antibiotics
67%
Pseudomonas aeruginosa
66%
mathematical models
62%
9
Citations (Scopus)
241
Downloads (Pure)