1967
DOI: 10.1002/9780470122747.ch1
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The Statistical Analysis of Enzyme Kinetic Data

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Cited by 716 publications
(359 citation statements)
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“…Velocities were determined in duplicate at each of the 16 conditions defined by a 4 ϫ 4 matrix of acceptor and nucleotide concentrations. The averaged data were fit using the SEQUENO program, which uses a weighted least square fitting algorithm (25).…”
Section: Methodsmentioning
confidence: 99%
“…Velocities were determined in duplicate at each of the 16 conditions defined by a 4 ϫ 4 matrix of acceptor and nucleotide concentrations. The averaged data were fit using the SEQUENO program, which uses a weighted least square fitting algorithm (25).…”
Section: Methodsmentioning
confidence: 99%
“…To compare competitive, noncompetitive, and uncompetitive models statistically, each pair was contrasted by using Akaike's method (14) by using GraphPad Prism, which reports the relative percent chance that a model is correct. To determine best-fit K is and K ii values for a mixed inhibition model, data were globally fitted to a mixed inhibition model with the program KinetAsyst (Intellikinetics), which uses the algorithm of Cleland (15).…”
Section: Methodsmentioning
confidence: 99%
“…Initial velocities for each reaction were determined by plotting the SAH concentration versus time and were fit to the Michaelis-Menten equation v ¼ VA/(K þ A). The kinetic constants were calculated using non-linear regression, 15 where K was the Michaelis constant for the varied substrate and V was the maximum velocity.…”
Section: Activity Assaysmentioning
confidence: 99%