Puromycin              package:datasets              R Documentation

_R_e_a_c_t_i_o_n _v_e_l_o_c_i_t_y _o_f _a_n _e_n_z_y_m_a_t_i_c _r_e_a_c_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_U_s_a_g_e:

     Puromycin

_F_o_r_m_a_t:

     This data frame contains the following columns:

     _c_o_n_c a numeric vector of substrate concentrations (ppm)

     _r_a_t_e a numeric vector of instantaneous reaction rates
          (counts/min/min)

     _s_t_a_t_e a factor with levels 'treated'  'untreated' 


_D_e_t_a_i_l_s:

     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.

_S_o_u_r_c_e:

     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.

_E_x_a_m_p_l_e_s:

     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), 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, 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)

