TABLE 1

Average parameter estimates for time-dependent inhibition models with the Michaelis-Menten scheme

Experiments (n = 100 repeats) were simulated with varying number of inhibitor concentrations × preincubation times. Data were simulated with a K_I = 10 µM and k_inact = 0.025 min⁻¹.

Experimental Design	Error in Simulated Data (%)	Numerical Method^{^a}			Replot Method
6 × 6, nondiluted		K_I, µM Range^{^b}	k_inact, min⁻¹ Range^{^b}	Percent Converged	K_I, µM Range^{^b}	k_inact, min⁻¹ Range^{^b}	Percent Converged
	2.5	9.7–10.3	0.024–0.026	100	8.0–13.1	0.024–0.027	100
	5	9.5–10.6	0.023–0.027	100	6.2–15.4	0.023–0.028	100
	10	8.4–10.8	0.020–0.029	100	3.2–32.3	0.020–0.034	98
	20	6.7–11.5	0.011–0.039	100	1.6–58.3	0.017–0.045	86
6 × 6, diluted
	2.5	9.4–10.6	0.024–0.026	100	8.1–13.3	0.024–0.027	100
	5	8.6–11.1	0.023–0.027	100	5.7–15.6	0.022–0.028	100
	10	7.6–12.2	0.021–0.030	100	4.4–26.7	0.020–0.035	100
	20	5.5–14.6	0.018–0.036	100	1.4–43.2	0.019–0.050	82
6 × 2, nondiluted
	2.5	9.6–10.4	0.024–0.026	100	7.9–12.8	0.024–0.027	100
	5	9.2–10.8	0.023–0.028	100	5.0–15.2	0.023–0.029	100
	10	8.3–11.4	0.019–0.031	100	3.2–28.7	0.018–0.038	96
	20	6.25–12.7	0.011–0.040	100	2.2–43.3	0.014–0.058	76
6 × 2, diluted
	2.5	9.2–11.1	0.024–0.026	100	7.7–14.2	0.024–0.027	100
	5	8.3–11.5	0.022–0.027	100	5.6–19.6	0.022–0.029	100
	10	7.2–13.8	0.020–0.031	100	3.3–28.7	0.019–0.036	95
	20	4.3–17.7	0.013–0.040	100	1.5–44.3	0.011–0.052	76

↵^a Ordinary differential equations for Fig. 3A were used.
↵^b Range (±S.D.) determined from the log normal distribution of 100 runs.