| % File src/library/datasets/man/Puromycin.Rd |
| % Part of the R package, https://www.R-project.org |
| % Copyright 1995-2014 R Core Team |
| % Distributed under GPL 2 or later |
| |
| \name{Puromycin} |
| \docType{data} |
| \alias{Puromycin} |
| \title{Reaction Velocity of an Enzymatic Reaction} |
| \description{ |
| The \code{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. |
| } |
| \usage{Puromycin} |
| \format{ |
| This data frame contains the following columns: |
| \describe{ |
| \item{\code{conc}}{ |
| a numeric vector of substrate concentrations (ppm) |
| } |
| \item{\code{rate}}{ |
| a numeric vector of instantaneous reaction rates (counts/min/min) |
| } |
| \item{\code{state}}{ |
| a factor with levels |
| \code{treated} |
| \code{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), |
| \emph{Nonlinear Regression Analysis and Its Applications}, |
| Wiley, Appendix A1.3. |
| |
| Treloar, M. A. (1974), \emph{Effects of Puromycin on |
| Galactosyltransferase in Golgi Membranes}, M.Sc. Thesis, U. of |
| Toronto. |
| } |
| |
| \seealso{ |
| \code{\link{SSmicmen}} for other models fitted to this dataset. |
| } |
| \examples{ |
| require(stats); require(graphics) |
| \dontshow{options(show.nls.convergence=FALSE)} |
| 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") |
| } |
| \keyword{datasets} |