Studies

Data analysis and modelling scripts and results for the Seaton et al. 2017 study, from Daniel Seaton.

Experimental data reported in the Seaton et al. 2017 study; data processing by Alex Graf. Part of the EU FP7 TiMet project.

Literature data and associated scripts analysed in the Seaton et al. 2017 study; data processing by Daniel Seaton.

Literature data used in the Seaton et al. 2017 study; data processing by Daniel Seaton.

This stores:
Processed data files
Links to raw data files
Links to repositories containing applied workflows

s-core/ s-core+ network peeling is a methodology to identify cores of weighted complex networks.

PGK-GAPDH reactions were studied in vitro at 30 and 70 using yeast or Sso enzymes

SED-ML:
https://jjj.bio.vu.nl/models/experiments/kouril2017_fig4b/simulate
https://jjj.bio.vu.nl/models/experiments/kouril2017_fig4c/simulate

PGK reaction at 30 C. Yeast PGK was incubated at 30 C, in the presence or absence of the ATP recycling system, and the conversion of 3 PG to BPG was followed.
SED-ML simulations
Fig. 1A: https://jjj.bio.vu.nl/models/experiments/kouril2017_fig1a/simulate

PGK reaction at 70C. Sulfolobus solfataricus PGK was incubated at 70C in presence and absence of an ATP recycling system.
Changes in metabolite concentrations was followed via 31P NMR or enzymatic analyses.

SED-ML:
https://jjj.bio.vu.nl/models/experiments/kouril2017_fig3b/simulate
https://jjj.bio.vu.nl/models/experiments/kouril2017_fig3c/simulate
https://jjj.bio.vu.nl/models/experiments/kouril2017_fig3d/simulate

BPG produced with yeast PGK was incubated at 70 C,upon which BPG rapidly dephosphorylates to 3PG.
SED-ML:
https://jjj.bio.vu.nl/models/experiments/kouril2017_fig2b/simulate

After the removal of the extracellular antibiotic, efflux and inhibition dynamics combine to delay the synthesis of ribosomes in a concentration-dependent manner (panel ii). Colors indicate increasing antibiotic concentration, as shown in panel ii.

SED-ML simulation
https://jjj.bio.vu.nl/models/experiments/srimani2017_fig2cii/simulate

Predictions made using the core model for combinatorial perturbations to the model simulating combined effects from OE, KO mutants, perturbations and time series concentrations.

Contains copy number per locus tag at different times of Growth between 0.25h and 96 hours.
M. pneumoniae was grown in Batch, cells attached to the bottom of the flask (single cell layer), non stirred, non aerated.

Personalized liver function tests: A Multiscale Computational Model Predicts Individual Human Liver Function From Single-Cell Metabolism

Understanding how liver function arises from the complex interaction of morphology, perfusion, and metabolism from single cells up to the entire organ requires systems-levels computational approaches. We report a multiscale mathematical model of the Human liver comprising the scales from single hepatocytes, over representation of ultra-structure and micro-circulation
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Computational modeling of human caffeine clearance by the liver.

More information available from
https://github.com/matthiaskoenig/caffeine

Internal metabolites concentrations for time series data (not pulse experiments) and for mutant OE, KO mutants and perturbations
External metabolite concentrations for time series data (not pulse experiments) and for mutant OE, KO mutants and perturbations
Mutant (OE, KO, perturbation) metabolite measurements

Growth-factor deprived mCFU-E cells (5x106 cells per condition) and BaF3-EpoR cells (1x107 cells per condition) were stimulated with different Epo doses and absolute concentrations were determined for pEpoR (B), pAKT (C), ppERK (D). The scale for pS6 (E) was estimated in arbitrary units. GTP-Ras (F) and ppERK were determined upon stimulation with indicated, color-coded Epo doses. pEpoR was analyzed by immunoprecipitation followed by immunoblotting, GTP-Ras was analyzed after pulldown using a
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Genotype: Wildtype (M145E)
Medium: Phosphate-limited (F134)

Experimental study for hands on session

Here you will find guidelines for creating MIAPE compliant proteomics data files as well as examples and links to online tools and resources

Here you will find guidelines for creating MAGE-TAB compliant transcriptomics data files as well as examples and links to online tools and resources.

Transcriptional response to a sudden increase in extracellular ligand (hormone), for the six network designs of (A). The transcriptional response is taken to equal the ratio ReNrL/Retotal, i.e., the fraction of REs attaching ligand-bound NR. The ligand concentration was increased from 0 to 0.005 nM and maintained constant at the latter level. The observation that design 6 is higher than all other designs at long times is robust for parameter changes up to a factor of 3.

Lactic acid bacteria generally use homolactic fermentation for generation of ATP. Here we studied the role of Arginine and Glutamine metabolism on the general physiology of the lactic acid bacteria Streptococcus pyogenes.
A deletion mutant of glnA (glutamine synthetase) has been constructed in the S. pyogenes M49 591 background. The glnA mutant strain shows decreased growth in low glutamine and excess glutamate conditions and no growth at all in low glutamine and low glutamate conditions.
An arcA
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Determination of essential amino acids for Streptococcus pyogenes M49

Pyruvate kinase (PYK, EC 2.7.1.40) is a key step in glycolysis converting phosphoenolpyruvate into pyruvate. The activity of PYK is activator-dependent, with the allosteric activation mostly being due to fructose-1,6-bisphosphate (FBP).