Models

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.

Underlying R script for the investigation of immune cells. Script contains basic data processing, as well as a DE and monocle analysis.

Stoichiometric model in SBML format using the acetate-aerobic standard scenario.

Please note that SBML was exported using the sbmlwriter class of Metano. This file was not used for the actual analyses.

This stoichiometric model of Aromatoleum aromaticum EbN1 is a genome-scale model and comprises 655 enzyme-catalyzed reactions and 731 distinct metabolites.

The model is in the plain-text reaction format of Metano that is human-readable and can be opened with every text editor. To run this version of the model, please use the Metano Modeling Toolbox (mmtb.brenda-enzymes.org) and the associated scenario files.

Atlantic salmon (Salmo salar) is the most valuable farmed fish globally and there is much interest in optimizing its genetics and rearing conditions for growth and feed efficiency. Marine feed ingredients must be replaced to meet global demand, with challenges for fish health and sustainability. Metabolic models can address this by connecting genomes to metabolism, which converts nutrients in the feed to energy and biomass, but such models are currently not available for major aquaculture species ...

A model of the circadian regulation of starch turnover, as published in Seaton, Ebenhoeh, Millar, Pokhilko, "Regulatory principles and experimental approaches to the circadian control of starch turnover", J. Roy. Soc. Interface, 2013. This model is referred to as "Model Variant 2". The other model variants are all available from www.plasmo.ed.ac.uk as stated in the publication. Note that the 'P2011' circadian clock model was modified for this publication (as described), in order to replicate the ...

Matlab model (could not be represented in SBML) from publication with abstract: Clock-regulated pathways coordinate the response of many developmental processes to changes in photoperiod and temperature. We model two of the best-understood clock output pathways in Arabidopsis, which control key regulators of flowering and elongation growth. In flowering, the model predicted regulatory links from the clock to CYCLING DOF FACTOR 1 (CDF1) and FLAVIN-BINDING, KELCH REPEAT, F-BOX 1 (FKF1) transcription. ...

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).

A population of turtles have between 1 and 3 genes contributing to the strength of selective destruction, which can either cause ageing or allow for negligible senescence.

Model of selective destruction in a single population of cells with differing sensitivities for growth. Fast growing cells can be epigenetically converted to slower cells rather than simple cell death as in previous models.

Model of unselective destruction in a single population of cells with differing sensitivities for growth

Model of selective destruction in a single population of cells with differing sensitivities for growth

Originally submitted model file for PLaSMo accession ID PLM_1030, version 1

Model derived from U2019.2, fitted to TiMet data mutants data set. Fixed parameters are scaling factors, COP1 and cP parameters. The rest of the parameters were left optimisable. The networks used in the fitting include WT, lhycca1, prr79, toc1, gi and ztl. The ztl network was only used for fixing the period in this mutant. Then final parameter values for transcription rated were obtained by taking the product of scaling factor and either transcription or translation, the latter required for ...

Model derived from U2019.1 in which the transcription rates were rescaled to match the scale of TiMet data set for absolute units of RNA concentration. The gmX scaling parameters in the model were fitted numerically. This model has equivalent dynamics to P2011.1.2.

Model derived from U2020.2, fitted to the TiMet RNA data for wild-type and clock mutants. Fixed parameters are scaling factors, COP1 and cP parameters. The rest of the parameters were left optimisable. The networks used in the fitting include WT, lhycca1, prr79, toc1, gi and ztl. The ztl network was only used for fixing the period in this mutant. Then final parameter values for transcription rates were obtained by taking the product of scaling factor and either transcription or translation, the ...

Model derived from U2020.1 by fitting the scaling factors for matching TiMet data set for wild-type and clock mutants, in absolute units.

Model derived from U2019.1, in which the way the PRR genes are regulated is modified. Repression mechanism introduced Instead of activation between the PRRs for producing the wave of expression. This is inspired in the result of three models P2012, F2014 and F2016. P2012 introduced TOC1 repression in earlier genes relative to its expression. F2014 introduced also the backward repression of PRR9 |-- PRR7 |--- PRR5, TOC1. However little attention was given to why there is a sharper expression ...

Model written in Antimony human-readable language and then translate into SBML using Tellurium

Model written in Antimony human-readable language, Model used in Pokhilko et al 2012

autogenerated equation listing from the SBML of U2020.3, as a .PDF file

autogenerated equation listing from the SBML of U2019.3, as a .PDF file

NLRP3 inflammasome activation

Thrombotic complications and coagulopathy in COVID-19