Studies

Altering the light:dark cycle of standard growth conditions and standard 'wild-type' Arabidopsis accession, with sucrose, starch and biomass data for whole rosettes

Standard growth conditions and standard 'wild-type' Arabidopsis accession, with biomass data for whole rosettes, and in some cases, individual leaf area and leaf biomass data

Standard growth conditions and 'wild-type' Arabidopsis accessions other than Col-0 and the 35S:miR156 transgenics, with biomass data for whole rosettes, and in some cases, individual leaf area and leaf biomass data

The FMv1 was constructed from 4 existing models, with Matlab and Simile versions.

D Biphasic control of stem-cell expansion, where stem-cell expansion is low both at high and low concentrations of y. The system has a stable fixed point at the concentration of y where pr = 0.5 and an unstable fixed point at some lower concentration of y.

SED-ML simulation
https://jjj.bio.vu.nl/models/experiments/karin2017_fig4d/simulate

C A mutated stem cell with a strong inactivation of the sensing of y has a
growth advantage (differentiates less), and therefore, it invades the stem- cell population. As a result, both the stem-cell pool and the number of terminally differentiated cells increase.

SED-ML simulation
https://jjj.bio.vu.nl/models/experiments/karin2017_fig4c/simulate

Mathematical simulation of a tamoxifen-induced conditional knock-in of a sixfold activating GCK mutant in beta cells. (C) The percentage of beta cells with mutated GCK increases to ~25% after 3 days, but then decreases and is eliminated after a few weeks. (D) Glucose levels initially decrease after the tamoxifen injection, but return to normal after a few weeks. Insets: Experimental results of Tornovsky-Babeay et al (2014).

SED-ML simulation
https://jjj.bio.vu.nl/models/experiments/karin2017_fig2/simulate
...

C Trajectories of Z from different initial concentrations of cells (Z) (i) or y (ii) for the circuit of (B). The healthy concentration Z = ZST is reached regardless of initial
concentration of Z, as long as it is nonzero, and regardless of the initial concentration of y.

SED-ML simulation
https://jjj.bio.vu.nl/models/experiments/karin2017_fig1c/simulate

D An arrow marks the time when a mutant with a strong activation of the sensing of y arises (for the circuit depicted in B). This mutant has a selective advantage and
takes over the population.

SED-ML simulation
https://jjj.bio.vu.nl/models/experiments/karin2017_fig1d/simulate

G Trajectories of Z from different initial concentrations of Z (i) or y (ii) for the circuit depicted in (F). The healthy concentration Z = ZST is not reached for small values of
Z (Z << ZST) or large values of y (y >> yUST).

SED-ML simulation
https://jjj.bio.vu.nl/models/experiments/karin2017_fig1g/simulate

H The arrows mark the times when a mutant with a strong activation of the sensing of y arises (for the biphasic circuit depicted in F). This mutant has a selective
disadvantage and is thus eliminated.

SED-ML simulation
https://jjj.bio.vu.nl/models/experiments/karin2017_fig1h/simulate

C Numerical simulations of the RpoD6 wild-type network show a shoulder of expression trailing the main peak (red line). All the parameters describing the clock and
SigC are as in Fig 4B, and only the threshold of activation of the rpoD6 promoter by the clock was modified. Numerical simulations of a SigC knock-out model (in
which the terms representing the regulation of RpoD6 by SigC are set to zero) show only single-peaked oscillations (blue line).
D The incoherent feedforward loop circuit that
...

Numerical simulations of the wild-type network show double peaks of expression (red line), and numerical simulations of a SigC knock-out model (in which the terms representing the regulation of PsbAI by SigC are set to zero) show only single-peaked oscillations (blue line)..

SED-ML simulation
https://jjj.bio.vu.nl/models/experiments/martins2016_fig4b/simulate

Here is a collection of examples of possibilities for API communication in R

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