Data files1289 Data files visible to you, out of a total of 2095
The abscissa of the plots shows the percentage of aerobiosis that is a physiological measure for oxygen availability (http://www.ncbi.nlm.nih.gov/pubmed/11844770).
1) Grey Boxes: Enzymes & Reactions
blue lines/symbols: flux in mmol per gramm dry cell weight an hour
red lines/symbols: mRNA levels
2) White Boxes: Intracellular and extracellular metabolites
blue lines/symbols: concentration of the metabolites (extracellular: mM, intracellular: AU)
3) Yellow Boxes:
Aggregated Quantities as yield,
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
. L. lactis (NZ9000), E. faecalis (V538) and S. pyogenes (M49) wild type strain and their ldh- mutants were grown in batch cultures at 37°C in anaerobic 96 wells plates in either TH-broth supplemented with 0.5% (w/v) yeast (THY) or a chemically defined medium for LAB (pH 7.4) (CDM-LAB (10)). Both media were buffered with either 100 mM MES buffer or 100 mM MOPS buffer for growth at pH 6.5 and 7.5 respectively.
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.
Steady state concentrations of extracellular metabolites in yeast Saccharomyces cerevisiae anaerobic chemostat at D = 0.1 h-1 on minimal medium. All metabolite concentrations are in mmol/L(R) except CO2, which is in parts of the partial pressure.
Steady state metabolic fluxes measured in glucose-limited chemostat of Saccharomyces cerevisiae at D = 0.1 h-1 growing on minimal medium. Fluxes are: glucose, ethanol, glycerol, acetate, succinate, pyruvate, lactate, citrate, malate, a-ketoglutarate, fumarate
AFG3, AKR1, BRP1, COG6, ERG6, HRK1, LST7, NHX1, PEP5, PIG1, PTK2, RCK2, RCY1, REF2, RIC1, RTS1, SGF11, SAP185, SKY1, SNC2, SUR2,
Untargeted and targeted metabolic analysis on T. b. brucei 427 grown under oxidative stress with methylene blue has been carried out. This work has been completed with 11 bio-reps and found significant metabolic changes as you can see in the IDEOM file attached. 'Comparison' tab in the data spread sheet shows heat maps and fold change analysis regarding different metabolite levels (T: T brucei, TMB: T. brucei exposed to methylene blue, numbers: time points, 0, 5, 60 & 120min). If you double click
-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.
The figure contains information necessary to understand the mathematical model of experiments in BSA115. In these experiments sigB response is artificially initiated by the addition of IPTG while sigB is downstream of a Pspac promoter. The figure shows a flow-chart diagram that combines three hypotheses to explain experiments. It contains the ODEs and the fit of the respective models to the data.
This files contains the parameter values, life-times, half-lives and errors associated with modeling the decay of the transcriptome, based on 3 models described in Deneke et al. "Complex degradation processes lead to non-exponential decay patters and age-dependent decay rates of messenger RNA". PLoS One. 2013;8(2):e55442
Growth yeast with 0mM K+ andlong timing collection