+-------------+-------------------+
| Puromycin   | 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.

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 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")

