TABLE 2.

Robustness with respect to parameter variations evaluated by Monte Carlo approach (11)

	Cooperative	Inhibitor		Zero-order
n = 1	0.00	kf = 0.1, kb = 10−2	0.00	KM = 10−1	0.02
n = 2	0.41	kf = 1, kb = 10−3	0.09	KM = 10−2	0.07
n = 3	0.37	kf = 1, kb = 10−4	0.13	KM = 10−3	0.16
n = 4	0.35	kf = 10, kb = 10−3	0.18	KM = 10−4	0.21
n = 5	0.34	kf = 102, kb = 10−5	0.32	KM = 10−5	0.29
n = 10	0.29	kf = 106, kb = 0	0.37	KM = 10−10	0.36

We generate random parameter sets for k₁, k₂, and k_d (the parameters common to all three models when assuming k_m to correspond to k_d), so that each parameter is uniformly distributed in logarithmic space in the interval from 10⁻⁵ to 1. We evaluate 10,000 parameter sets for each model. This provides accurate values whose asymmetric binomial 95% confidence intervals are ∼2% around the values provided.