Puromycin
Reaction Velocity of an Enzymatic Reaction
The Puromycin
data frame has 23 rows and 3 columns of the
reaction velocity versus substrate concentration in an enzymatic
reaction involving untreated cells or cells treated with Puromycin.
 Keywords
 datasets
Usage
Puromycin
Details
Data on the velocity of an enzymatic reaction were obtained by Treloar (1974). The number of counts per minute of radioactive product from the reaction was measured as a function of substrate concentration in parts per million (ppm) and from these counts the initial rate (or velocity) of the reaction was calculated (counts/min/min). The experiment was conducted once with the enzyme treated with Puromycin, and once with the enzyme untreated.
Format
This data frame contains the following columns:
conc
 a numeric vector of substrate concentrations (ppm)
rate
 a numeric vector of instantaneous reaction rates (counts/min/min)
state

a factor with levels
treated
untreated
Source
Bates, D.M. and Watts, D.G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley, Appendix A1.3. Treloar, M. A. (1974), Effects of Puromycin on Galactosyltransferase in Golgi Membranes, M.Sc. Thesis, U. of Toronto.
See Also
SSmicmen
for other models fitted to this dataset.
Examples
library(datasets)
require(stats); require(graphics)
plot(rate ~ conc, data = Puromycin, las = 1,
xlab = "Substrate concentration (ppm)",
ylab = "Reaction velocity (counts/min/min)",
pch = as.integer(Puromycin$state),
col = as.integer(Puromycin$state),
main = "Puromycin data and fitted MichaelisMenten curves")
## simplest form of fitting the MichaelisMenten model to these data
fm1 < nls(rate ~ Vm * conc/(K + conc), data = Puromycin,
subset = state == "treated",
start = c(Vm = 200, K = 0.05))
fm2 < nls(rate ~ Vm * conc/(K + conc), data = Puromycin,
subset = state == "untreated",
start = c(Vm = 160, K = 0.05))
summary(fm1)
summary(fm2)
## add fitted lines to the plot
conc < seq(0, 1.2, length.out = 101)
lines(conc, predict(fm1, list(conc = conc)), lty = 1, col = 1)
lines(conc, predict(fm2, list(conc = conc)), lty = 2, col = 2)
legend(0.8, 120, levels(Puromycin$state),
col = 1:2, lty = 1:2, pch = 1:2)
## using partial linearity
fm3 < nls(rate ~ conc/(K + conc), data = Puromycin,
subset = state == "treated", start = c(K = 0.05),
algorithm = "plinear")