Table 2.

Fit statistics for the various mathematical models^*

Model	Type1	Assumptions	NPar2	SSE3	MSE4	RMSE5	L6	AIC7
1	2C	ku,s = ku,e; pfs = pfe	2	2.39	0.0443	0.2105	−12.00	28.01
2	2C	ku,s ≠ ku,e; pfs = pfe	3	1.85	0.0343	0.1852	−9.01	24.02
3	2C	ku,s = ku,e; pfs ≠ pfe	3	1.06	0.0197	0.1402	−2.47	10.95
4	2C	ku,s ≠ ku,e; pfs ≠ pfe	4	1.03	0.0192	0.1384	−2.17	12.34
5	3C	ku,s = ku,e; pfs = pfe	3	0.65	0.0120	0.1096	3.31	−0.62
6	3C	ku,s ≠ ku,e; pfs = pfe	4	0.62	0.0115	0.1072	3.82	0.36
7	3C	ku,s = ku,e; pfs ≠ pfe	4	0.65	0.0120	0.1095	3.33	1.34
8	3C	ku,s ≠ ku,e; pfs ≠ pfe	5	0.59	0.0109	0.1044	4.43	1.13

^*Statistics are based on a weighted residual vector wherein residuals for phosphorylation level, and receptor mass measurements were divided by the respective maximum values for each of these measurements. There were a total of 54 experimental data points used for parameter estimation (see Figs. 2A & 2B). The model with the lowest AIC is highlighted in bold.

¹Model type – 2C = two compartment model; 3C = three compartment model

²N_Par – Number of model parameters

³SSE – Sum of squares error (or residual sum of squares)

⁴MSE – Mean-squared error = SSE/N_pts where N_pts is the number of data points (= 54)

⁵RMSE – root-mean squared error

⁶L – log-likelihood function = − (N_pts/2)[log(2π MSE)+1]

⁷AIC – Akaike information criterion = 2N_Par − 2L