Table 2.

Corrected Akaike Information criteria AICc_i and Akaike weights w_i for models with and without rapamycin for datasets 1 to 4, with k_i = p + 1 the number of parameters considered to compute the AICc_i, with i denoting the model considered. The weights corresponding to each assumption are obtained by summing the weights of the models verifying the assumption (see Table 1).

Models		Dataset 1		Dataset 2		Dataset 3		Dataset 4
	k i	AICci	w i	AICci	w i	AICci	w i	AICci	w i
(a) Without rapamycin
NT	11	−6.9	0.062	−101.9	0.033	−67.2	0.045	−109.2	0.379
NTt	10	−12.4	0.936	−108.6	0.967	−73.5	0.957	−110.2	0.609
NTf	12	−0.7	0.003	−92.9	10−4	−61.2	0.002	−102.4	0.012
Assumptions
Feedback			0.003		0.0004		0.002		0.01
No feedback			0.997		0.9996		0.998		0.99
(b) With rapamycin
NTRr	13	−2.3	0.393	−107.1	10−4	−75.9	0.025	−58.8	0.407
NTtRr	12	−1.4	0.248	−124.9	0.997	−83.1	0.929	−58.2	0.305
NTRi	13	−2.0	0.351	−113.5	0.003	−76.7	0.039	−58.1	0.282
NTRrf	14	7.0	0.004	−91.6	10−8	−73.2	0.007	−48.0	0.002
NTRif	14	6.6	0.005	−102.4	10−5	−66.6	10−4	−49.4	0.004
Assumptions
Reversible			0.64		0.9967		0.961		0.714
Irreversible			0.36		0.0033		0.039		0.286
Feedback			0.008		10−5		0.007		0.006
No feedback			0.992		0.99999		0.993		0.994