Fig. 1

Implemented pipelines in SBpipe. a Example of work flow using the parameter estimation pipeline. Parameter estimations were performed using data sets of different sizes. The Identifiable column shows the results using a data set sufficient for estimating the parameters with their confidence intervals, whereas the column Non-identifiable illustrates the results using the same model but a reduced data set, insufficient for identifying parameter values. Size of the fit sequence: N=1000. For the complete results generated by this pipeline, see Additional file 1: Tables S2–S4, Figures S2–S8. b Deterministic and stochastic model time courses for the phosphorylated IR_beta species obtained with the model simulation pipeline. For stochastic simulations, mean (black), 95% confidence interval for the mean (cyan), and 1 standard deviation (light blue) are reported. Experimental data are added and indicated as red circles. For the complete results, see Additional file 1: Figures S9–S10. c Single parameter scan pipeline. The k1 parameter regulating the IR_beta phosphorylation was scanned within its 95% estimated confidence interval. The blue area is the results of 100 time course simulations over this interval. For the complete results, see Additional file 1: Figures S11–S12. d Double parameter scan pipeline. Signal intensities for the phosphorylated IR_beta receptor different levels of Insulin (x axis) and IR_beta receptor (y axis) at 1, 2, 5, and 10 minutes upon insulin stimulation. The colour representation indicates how the readout signal intensity varies upon two model parameter levels. For the complete results, see Additional file 1: Figures S13–S15. All the results can be replicated using the test files provided within the SBpipe package available online on the GitHub repository