Data files1289 Data files visible to you, out of a total of 2095
Contains relative metabolite concentrations for 40 samples based on technical triplicates. Medium and SD values were calculated and used for 1000 sampled simmulations (sampling from the measurement distribution per metabolite) per sample.
Also contains annotion to link metabolite concentrations and protein fold change measurements for OE and KO mutants to the model as well as external glucose, acetate and lactate concentrations. A SBtab like format was used to easily load the MEAN and SD metabolite
Comparison of Kcat values model and values from literature. Model values are based on Vmax enzyme parameters (maximum activity per enzyme molecule).
Literature values are largely based on whole cell enzyme extract assays and do not take into account allosteric control. In addition activity is measured at varying time points and varying conditions. The error based on differences in enzyme concentrations at different time points and the error in protein copy number measurements is taken into account
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.
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.
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.
Contains growth curve data such as Glucose uptake rate, lactate and acetate production at different time points.
Growth curve A was used train the model with external glucose concentration as well as external lactate, acetate concentration and estimated glucose acetate and lactate flux
Violin plot of the metabolic control of model parameters on the flux through PFK (as a proxy for flux through glycolysis) based on a 100.000 Latin Hypercube samples from the parameter space (range 0.001-100 for Km values and 0.001-1000 for Vmax values).
Shows the correlation in metabolic control between parameters.
The plot shows central carbon metabolis basically consist of a few control hubs of reactions of which the parameters are correlated. In other words, just the reaction network combined with allosteric control and equilibrium constants impose some constrains on possible parameter value combinations that lead to certain behaviour (such as the flux and concentrations measured in vivo/vitro).
Contains relative mutant (OE, KO) perturbation and time series samples metabolite concentrations and enzyme fold change of targeted enzymes used for model validation.
Measured are the relative fold change, Mean and SD of log2 fold change values are based on multiple measurements per sample (minimum of three).
Contains input data for Automated Model simulations pipeline to load and update the models metabolite concentrations and enzyme parameters to simulate all sample using a custom python script
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.
Master file, aggregates metabolite concentrations inside and outside the cell, protein copy number and flux estimates for metabolites in the core model. Based on all internal metabolite concentrations, external metabolite concentrations from growth curve data, flux of glucose, lactate and acetate based on growth curve data and protein copy number data for enzyme concentrations. Combines absolute and relative measurements and metabolomics measurements from different experiment to get an as complete
Mean Absolute Percentage Error between measured and simulated metabolite concentrations using 1000x sampling from the Gausian distribution of the measured values based on multiple replicates per measured conditions. SS simulations was performed.
Graphs showing the Mean Absolute Percentage Error 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.
-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 the estimated oxygen concentration and metabolite concentrations as wel as the model with addition of an oxygen inhibition parameter.
Results: Addition of the oxygen inhibition term does not improve the modell with the current parameter set