Data files

Contains time series metabolomics measurements in mM from different experiments. Measurements of these internal metabolites should be combined with Growth curve A measurements forthe external metabolites. Contains mean and standard deviation for a few but not all mutants based on multiple time series experiments carried out over the years. Daniel 3rd experiment data is the most complete and is together with Wodke and Tobias measurements used as training data for the model.

Contains for all samples, mean metabolite concentration and shows enzyme concentration used in model fitting and simmulations. Only metabolite present in the model are shown.

Simulation of double mutants and perturbations and time series samples using for Sample 1 only OE mutants of which we update the enzyme concentrations. For each second mutant the enzyme concentrations in case of OE and KO mutants in updated and the metabolite concentrations of the second sample are loaded in the model. Using this approach the model approximately predicts combinatorial effects of OE mutations with other mutations, perturbations and time series concentrations.

Comparison of model SS metabolite concentrations with measured values using 1000x sampling from the Gausian distribution of the measured values based on multiple replicates per measured conditions. Graphs showing the distribution of measured and simulated metabolite concentration for 95 mutand (KO, OE), perturbation and time series measurements. Model simulations performed using 24h proteomics with modification of enzyme parameters for KO and OE mutants.

Contains: -Relative metabolite measurements at different time points from all experiments -Absolute metabolite measurements for amino-acid analysis of the proteome and the cytosol -Effect on adding CaCl2, KCl or NaCl to the medium on growth -Effect of spiking of growth medium with additional amino acids

Contains all 10 parameter sets, loaded with proteomics measurements for three time points (6h,24h, 48h). Contains all parameter sets exported from COPASI, an overview of the parameter sets in the three conditions and how well they perform as well as scripts to load parameter sets as well as an R script to generate an overview of the model error in predicting for all 10 parameter sets.

Contains a Jupyter notebook file that uses libroadrunner and tellurium to run all simmulations and analysis based on the 40 independent samples. The Readme.txt file contains information on how to recreate the complete modelling environment used for all simmulations and analysis using Anaconda.

Local sensitivity analysis based on 40 samples using 1000x sampling from measurement distribution. The control shown is the control over flux through glycolysis represented by flux through PRK. Th plot summarized the control for each parameter over all observed metabolite concentrations encountered for the 40 samples. As such the metabolic control analysis is local but shows the distribution taking into account measurement error as well as biological variation over the 40 samples.

Tab seperated file containing the raw output of the local sensitivity analysis based on 40 samples (based on 1000x sampled metabolite values) from the MEAN and SD of the metabolite measurements. Sensitivity analysis is based on the flux through PFK as objective and as proxy for flux through glycolysis. Data can be plotted using the R script "plotLocalGlobalSensitivity1.5.R" associated to the same assay.

Follow the instructions in this file to get the necessary software packages to run the Simulation Foundry Version 1.5.

First MESI-STRAT Newsletter June 2019

Small set of values for entering into NetLogo Simulation

This mini-symposium, colocated at the ECMTB in Gothenburg, presents the state-of-the-art in computational methods for multicellular systems biology. It brings together developers and users of this software to identify common approaches and future challenges concerning multiscale integration, model specification, model exchange, scalability, workflow management as well as compliance to standards and guidelines. The session thus aims to provide an overview of the available modeling and simulation ...

Very simple unrealistic values for Metformin kinetics (Km). For use in a simple graphical model.

Very simple unrealistic values for Fisetin kinetics (Km). For use in a simple graphical model.

Master table describing all the samples included in MOA Investigation; studies are descrubed using MIAPPE (Minimum Information About a Plant Phenotyping Experiment).

Very simple unrealistic values for Everolimus kinetics (Km). For use in a simple graphical model.

Very simplistic unrealistic values for tamoxifen Kinetics (Km). For use in a simple graphical model.

Digitized pharmacokinetics data.

Digitized pharmacokinetics data.