Working Group Nicole Radde

The German Network for Bioinformatics Infrastructure - de.NBI offers first class bioinformatics services including training and education to users in basic and applied life sciences research. In this network 40 projects belonging to eight service centers provide services that cover a wide variety of methods (genomics, proteomics, ...) and applications (from plants to humans). de.NBI-SysBio is the Systems Biology Service Center of de.NBI. In collaboration with FAIRDOM, de.NBI-SysBio serves the ...

This study investigates the citations of reproducible vs. not reproducible papers and is based on 328 published models, classified by Tiwari et al. based on their reproducibility are analyzed in this study. Hypothese testing is performed using a flexible Bayesian approach for a complete assessment of posteriors. The approach handels outliers via a non-central t distribution. Results show that reproducible papers are significantly more citet between 2013 and 2020, i.e. 10 years after the introduction ...

Motivated by an increasing population and the desire to grow plants more efficiently, attention has turned to the use of Light Emitting Diodes (LEDs) to illuminate plants which are grown indoors. Indoor growing facilities enable closely controlled and mon- itored environmental conditions. More and more of these facilities exchange High Pressure Sodium (HPS) lamps for LED lighting since they provide more efficient lighting and the possibility to control light intensity and quality in order to ...

Motivated by an increasing population and the desire to grow plants more efficiently, attention has turned to the use of Light Emitting Diodes (LEDs) to illuminate plants which are grown indoors. Indoor growing facilities enable closely controlled and mon- itored environmental conditions. More and more of these facilities exchange High Pressure Sodium (HPS) lamps for LED lighting since they provide more efficient lighting and the possibility to control light intensity and quality in order to ...

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.

The statistical analysis was performed in a jupyter notebook. This notebook contains the commands for all performed analyses (Statistical_analysis_of_FAIR_citations.ipynb)

The Bayesian Estimation Superseeds the t Test (BEST) method of Kruschke 2013 was used for the Bayesian significance testing. The method was implemented in a python class together with visualization and distributional analysis methods (BEST_method_python_Kruschke2012.py). Also the bayesian multiple comparison analysis can be ...

The Results of the analysis are structured in three parts:

1. The results of the main analysis
2. The results with a broader prior (Sensitivity analysis)
3. The Results of the multiple period comparison

For each part, full posterior traces for all analysis and visualizations of the paper are avalable.

Furthermore the diagnostics and traces were added for the different analysis. The trace for the mulitple comparison was to large to upload it and is available on request.

The classification in reproducible and not reproducible models was made by Tiwari et al.

Citations were looked up in Scopus, Web of Science and Google Scholar.

The following journals had to be excluded, as Journal Impact Factors (JIF) were missing or papers were discontinued:

• Experientia was closed 1996 and continued as Cellular and Molecular Life Sciences 1997
• The American journal of physiology – split into fields 1977, further splits in 1980 and 1989
• IFAC Proceedings Volumes – last issue ...

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, ...

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.

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.

Please see the Readme file for information regarding the simulation enivronment and file contents.

We recommend to use a virtual environment with a python 3.11 distribution to reproduce our results. Using anaconda and the environment file eulerpi_env.yml, the working virtual environment is set up through the prompt

conda env create -f eulerpi_env.yml

The environment file eulerpi_env.yml contains a human-readable list of all required dependencies. Consequently, these dependencies can also be installed manually.

In addition to this README and the environment file, the downloaded .zip ...

The folder contains the jupyter notebook for the execution of all analyses of the study. The BEST method is used in the notebook and is added in a separate python skript.

There is a class for the BEST method according to Kruschke and a class für the BEST multiple comparison.

A conda environment file with all libraries that are necessary to perform the analysis, including the package version was created. It can be easily installed via conda env create -f pymc_env.yml

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.

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.

The Folder contains:

• The MCMC and simulation results, as well as the synthetic data of the Chemical Reaction Network model (DoubleDecayIndep)
• The MCMC and simulation results, as well as the synthetic data of the Lotka-Volterra model (LotkaVolterraJoint)

Together with an executable ipynb script (Exe.ipynb) and the MCMC plotting and execution functions (MCMCFunctions.py).

There are three files:

• executable.py
• functions.py
• plots.py

The first file leads you through my data creation and processing step by step. You can choose how many of my data you want to use or reproduce by yourself.

The executable-file needs the functions from "functions" and "plots" in order to run correctly.

This contains the final version of my thesis as well as the implemented figures as .pgf and .svg files.