EVOLUTION-MANAGER
Edit File: Puromycin.html
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd"><html xmlns="http://www.w3.org/1999/xhtml"><head><title>R: Reaction Velocity of an Enzymatic Reaction</title> <meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <link rel="stylesheet" type="text/css" href="R.css" /> </head><body> <table width="100%" summary="page for Puromycin {datasets}"><tr><td>Puromycin {datasets}</td><td style="text-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><code>conc</code></dt><dd> <p>a numeric vector of substrate concentrations (ppm) </p> </dd> <dt><code>rate</code></dt><dd> <p>a numeric vector of instantaneous reaction rates (counts/min/min) </p> </dd> <dt><code>state</code></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><a href="../../stats/html/SSmicmen.html">SSmicmen</a></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 <- 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") </pre> <hr /><div style="text-align: center;">[Package <em>datasets</em> version 3.6.0 <a href="00Index.html">Index</a>]</div> </body></html>