Additional file 2 of Mathematical model of TGF-βsignalling: feedback coupling is consistent with signal switching
Creators: name: Shabnam Khatibi
name: Zhu, Hong-Jian
name: Wagner, John
name: Tan, Chin Wee
name: Manton, Jonathan
name: Burgess, Antony
Publisher: name: Figshare
Year: 2017
Subjects: subject: Biophysics
subject: Biochemistry
subject: Cell Biology
subject: Molecular Biology
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Developmental Biology
subject: Infectious Diseases
subject: FOS: Health sciences
subject: FOS: Health sciences

description: Which provides information about extra figures (Figures S1-S7) of model simulation and experimental data that help understanding the signalling system. Figure S1 Dynamics of feedback loops in RF- model. A) The negative feedback loop time-course for a time-delay τ N =20 minutes B) The positive feedback loop time-course for a time-delay τ P =120 minutes. N changes proportionally with (S)3 as ((S)3) 2 while P is inversely proportional to (S)3 as 1/(1+((S)3)2). Figure S2 The effects of feedback loops on the TGF-βreceptor concentration dynamics. The time-delays are either for both τ N and τ P =45 minutes or τ P =120 and τ N =20 minutes. The effects of individual feedback loops on the receptor levels are studied. The peaks and the valleys are due to positive and negative feedback loops respectively. The time-delays shift the peaks and valleys in time. The strength of the feedback loops change the amplitude of the peaks and valleys. Figure S3 The predicted effects of different concentrations of TGF-β on PSMAD levels when the negative feedback loop influences PC only. These effects are shown for the short-term (50 min) and long-term (500 min) responses of the TGF-βsignalling system. Note that the dots are derived from model simulation and the curves show the Hill equations which are fitted by MATLAB. Figure S4 PSMAD responses to changes in TGF-β concentration at different simulation times. The Hill coefficient increases with the increase in the simulation time. The PSMAD response does not switch before 200 min, where the PSMAD level starts saturating (Fig. 4). The curves of 50 min and 500 min simulation times correspond to the curves in Fig. 5. Figure S5 PSMAD time-course for different production rates of SMAD The SMAD production rate (v S) determines the steady-state of the PSMAD response of RF- model. Higher v S SMAD concentration during the signalling. The PSMAD time-course experiences damped oscillation for high v S. The oscillations appear to delay the system reaching its steady-state. Figure S6 The representative Western blots for PSMAD2 analysed in Fig. 9. A)wild type MEFs B)Gp130 F/F MEFs. In each panel, the top bands show the PSMAD2 signals of the double-stimulation experiment. The bottom bands show the actin signals for loading control to which the PSMAD2 levels are normalised to produce the results shown in Fig. 9. Figure S7 The validation of the simplified model with experimental data. The dots show the level of PSMAD2 concentration obtained from experiment and the curves specify the model predictions. A) PSMAD2 time-course for 0-1h on SV40-immortalised MEFs stimulated with TGF-β and its corresponding blot B) PSMAD2 time-course for 0-4h on SV40-immortalised MEFs stimulated with TGF-β and its corresponding blot C) PSMAD2 time-course for 0-4h on wild type MEFs stimulated with TGF-β and its corresponding blot. (PDF 1750 kb)