R Dataset / Package datasets / Puromycin
On this Rdata statistics page, you will find information about the Puromycin data set which pertains to Reaction Velocity of an Enzymatic Reaction. The Puromycin data set is found in the datasets R package. You can load the Puromycin data set in R by issuing the following command at the console data("Puromycin"). This will load the data into a variable called Puromycin. If R says the Puromycin data set is not found, you can try installing the package by issuing this command install.packages("datasets") and then attempt to reload the data with the library() command. If you need to download R, you can go to the R project website. You can download a CSV (comma separated values) version of the Puromycin R data set. The size of this file is about 470 bytes.
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.
Usage
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.
See Also
SSmicmen
for other models fitted to this dataset.
Examples
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")
Dataset imported from https://www.rproject.org.