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.
This data frame contains the following columns:
a numeric vector of substrate concentrations (ppm)
a numeric vector of instantaneous reaction rates (counts/min/min)
a factor with levels
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.
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.
SSmicmen for other models fitted to this dataset.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28
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 Michaelis-Menten curves") ## simplest form of fitting the Michaelis-Menten 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")