Puromycin                package:nls                R Documentation

Reaction velocity of an enzymatic reaction

Description:

     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.

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' 

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.

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.

Examples:

     data(Puromycin)
     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), trace = TRUE)
     fm2 <- nls(rate ~ Vm * conc/(K + conc), data = Puromycin,
                subset = state == "untreated",
                start = c(Vm = 160, K = 0.05), trace = TRUE)
     summary(fm1)
     summary(fm2)
     ## using partial linearity
     fm3 <- nls(rate ~ conc/(K + conc), data = Puromycin,
                subset = state == "treated", start = c(K = 0.05),
                algorithm = "plinear", trace = TRUE)
     ## using a self-starting model
     fm4 <- nls(rate ~ SSmicmen(conc, Vm, K), data = Puromycin,
                subset = state == "treated")
     summary(fm4)
     ## add fitted lines to the plot
     conc <- seq(0, 1.2, len = 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)

