Molecular Systems Biology

Biphasic response as a mechanism against mutant takeover in tissue homeostasis circuits

Frequency doubling in the cyanobacterial circadian clock

Drug detoxification dynamics explain the postantibiotic effect

Protein abundance of AKT and ERK pathway components governs cell-type- specific regulation of proliferation

Design principles of nuclear receptor signaling: how complex networking improves signal transduction

Division of labor by dual feedback regulators controls JAK2/STAT5 signaling over broad ligand range

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

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

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
...

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.

For all experiments, primary CFU-E cells were starved and stimulated with 5 U/ml Epo. At the indicated time points, samples were subjected to quantitative immunoblotting. Experimental data (black circles) with estimated standard errors and trajectories of the best fit (solid lines) are represented. Mass spectrometry data represent replicates of four independent experiments.

SED-ML simulation: https://jjj.bio.vu.nl/models/experiments/bachmann2011/simulate

SED-ML simulation: https://jjj.bio.vu.nl/models/experiments/bachmann2011/simulate

See Figure 2 caption.

See Figure 2 caption.

See Figure 2 caption.

See Figure 2 caption.

See Figure 2 caption.

See Figure 2 caption

See Figure 2 caption.

See 'Figure 3 legend' and 'Materials and methods - Time course experiments, cell lysis and quantitative immunoblotting' for details.