On this Picostat.com 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. 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
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
SSmicmen for other models fitted to this dataset.
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))
## 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.r-project.org.