The experimental data of Midazolam, OH-Midazolam, Caffein, Codeine, Norcodeine, Codein-6Glucuronide, Morphine-3Glucuronide and Morphine was analyzed via a Bayesian uncertainty quantification. An underlying model describing the bolus injection, followed by the exponential decay was written in sbml and a PEtab problem was created. The sampling and ensemble creation was conducted with the python toolbox pyPESTO.
For further details, please take a look at the methods section of the paper.
SEEK ID: https://fairdomhub.org/assays/1956
Modelling analysis
Projects: SteaPKMod
Investigation: hidden item
Study: Periportal steatosis in mice affects distinct parameters of pericentral drug metabolism
Assay position:
Biological problem addressed: Model Analysis Type
Organisms: No organisms
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Created: 22nd Jul 2022 at 07:42
Last updated: 8th Dec 2022 at 17:39
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Projects: Working Group Nicole Radde, SteaPKMod
Institutions: University of Stuttgart
https://orcid.org/0000-0002-5300-0915Currently I focuse on the integration of data into multi-scale models with statistical methods and uncertainty tracking in the research unit QuaLiPerF.
Projects: SteaPKMod
Web page: https://www.uniklinikum-jena.de/avc/Forschung/Experimentelle+Chirurgie.html
Investigation regarding the impact of periportal steatosis on selected parameters of drug metabolism.
Programme: Experimental Transplantation Surgery
Public web page: https://qualiperf.de/
Organisms: Mus musculus
Submitter: Uta Dahmen
Investigation: 1 hidden item
Assays: Animal experiment and Pharmacokinetics data, Bayesian uncertainty quantification, Hematoxylin-Eosin (HE) staining, Immunohistochemistry _ CYP1A2, Immunohistochemistry _ CYP2D6, Immunohistochemistry _ CYP2E1, Immunohistochemistry _ CYP3A4, SteaPk-data Histology Quant., CYP activity, Protein, TG, AUC
Snapshots: Snapshot 1
This code uses the PEtab problem (written in the yaml file) to perform MCMC sampling with pyPESTO. Afterwards an ensemble is created, that allows to compute the posterior predictive distribution and therefore the credibility intervals for the model output.
The sampling.py scipt of pyPESTO visulize was adjusted for an improved visualization. The used code was also uploaded.
A Conda environment file (.yml) containing the specific version of Python and installed packages with the according versions, ...
Investigations: 2 hidden items
Studies: Periportal steatosis in mice affects distinct p... and 1 hidden item
Assays: Bayesian uncertainty quantification, Bayesian uncertainty quantification
Complete posterior distributions for each drug and condition.
The files are in the hdf5 format and contain the complete information of the analysis for a FAIR data sharing.
Investigations: 2 hidden items
Studies: Periportal steatosis in mice affects distinct p... and 1 hidden item
Assays: Bayesian uncertainty quantification, Bayesian uncertainty quantification
The parameters A and B were estimated for the function f(x)=x⋅e^(B-Ax) and the AUC was calculated for the decay phase only. For OH-Midazolam the fourth replicate in the 2 weeks condition has an outlier in the measurement after six hours (more than 2SD above the mean of this condition) and was omitted for the AUC calculation as this could not be fitted to an exponential decay.
Investigations: 2 hidden items
Studies: Periportal steatosis in mice affects distinct p... and 1 hidden item
Assays: Bayesian uncertainty quantification, Bayesian uncertainty quantification
For each drug and condition, there is one:
- Visualization of the marginals and the sampling traces
- The credibility intervals for the parameters with the median
- Visualization of the individual ensemble output predictions with boxplots of the underlying data
Furthermore all conditions of the ensemble output predictions for one drug are visulized in one plot.
Investigations: 2 hidden items
Studies: Periportal steatosis in mice affects distinct p... and 1 hidden item
Assays: Bayesian uncertainty quantification, Bayesian uncertainty quantification
The exponential decay model with all parameters, observables and conditions was specified in a yaml file.
This yaml file is converted with yaml2sbml (2020 Jakob Vanhoefer, Marta R. A. Matos, Dilan Pathirana, Yannik Schaelte and Jan Hasenauer) to a PEtab problem, which contains also the SBML model.
Creator: Sebastian Höpfl
Submitter: Sebastian Höpfl
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Not specified
Organism: Mus musculus
Investigations: 2 hidden items
Studies: Periportal steatosis in mice affects distinct p... and 1 hidden item
Assays: Bayesian uncertainty quantification, Bayesian uncertainty quantification
The SOP creates a separate SBML model for each drug and condition, as the PEtab problem contains diffrent experimental data for them.
However, the SBML models only differ in their name as for all drugs and conditions, the same exponential decay model was assumed.
The SBMLs are automatically created by yaml2sbml, when the SOP is executed. Therefore, these files are for completeness only and are not necessary to replicate the analysis.
Creator: Sebastian Höpfl
Submitter: Sebastian Höpfl
Model type: Ordinary differential equations (ODE)
Model format: SBML
Environment: Not specified
Organism: Mus musculus
Investigations: 2 hidden items
Studies: Periportal steatosis in mice affects distinct p... and 1 hidden item
Assays: Bayesian uncertainty quantification, Bayesian uncertainty quantification