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<table width="100%" summary="page for Puromycin"><tr><td>Puromycin</td><td align="right">R Documentation</td></tr></table>

<h2>Reaction Velocity of an Enzymatic Reaction</h2>

<h3>Description</h3>


<p>The <code>Puromycin</code> 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.
</p>


<h3>Usage</h3>

<pre>Puromycin</pre>


<h3>Format</h3>


<p>This data frame contains the following columns:
</p>

<dl>
<dt>conc</dt><dd>
<p>a numeric vector of substrate concentrations (ppm)
</p>
</dd>
<dt>rate</dt><dd>
<p>a numeric vector of instantaneous reaction rates (counts/min/min)
</p>
</dd>
<dt>state</dt><dd>
<p>a factor with levels
<code>treated</code> 
<code>untreated</code> 
</p>
</dd>
</dl>



<h3>Details</h3>


<p>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.
</p>


<h3>Source</h3>


<p>Bates, D.M. and Watts, D.G. (1988),
<EM>Nonlinear Regression Analysis and Its Applications</EM>,
Wiley, Appendix A1.3.
</p>
<p>Treloar, M. A. (1974), <EM>Effects of Puromycin on
Galactosyltransferase in Golgi Membranes</EM>, M.Sc. Thesis, U. of
Toronto. 
</p>


<h3>See Also</h3>


<p><code>SSmicmen</code> for other models fitted to this dataset.
</p>


<h3>Examples</h3>

<pre>
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 &lt;- nls(rate ~ Vm * conc/(K + conc), data = Puromycin,
           subset = state == "treated",
           start = c(Vm = 200, K = 0.05))
fm2 &lt;- 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 &lt;- 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 &lt;- nls(rate ~ conc/(K + conc), data = Puromycin,
           subset = state == "treated", start = c(K = 0.05),
           algorithm = "plinear")
</pre>


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