Puromycin(datasets)
Puromycin()所属R语言包：datasets
Reaction Velocity of an Enzymatic Reaction
酶促反应的反应速度
译者：生物统计家园网 机器人LoveR
描述----------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.
Puromycin数据框有23行和第3列，在未经处理的单元或单元，嘌呤霉素治疗的一种酶反应的反应速度与底物浓度。
用法----------Usage----------
格式----------Format----------
This data frame contains the following columns:
这个数据框包含下列资料：
a numeric vector of substrate concentrations (ppm)
向量的底物浓度的数值（PPM）
a numeric vector of instantaneous reaction rates (counts/min/min)
一个瞬时反应速率的数字矢量（计数/分/分钟）
a factor with levels treated untreated
因素与水平treateduntreated
Details
详情----------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.
对酶促反应速度的数据，获得由Treloar（1974）。计数每分钟放射性产品从反应的底物浓度在百万分之几（PPM）功能，并从这些反应的初始速率（或速度）计数（计数/分/分钟计算） 。嘌呤霉素治疗的酶进行一次实验，一旦未经处理的酶。
源----------Source----------
Bates, D.M. and Watts, D.G. (1988), Nonlinear Regression Analysis and Its Applications, Wiley, Appendix A1.3.
贝茨，D.M.和瓦，D.G. （1988年），非线性回归分析及其应用，威利，附录A1.3。
Treloar, M. A. (1974), Effects of Puromycin on Galactosyltransferase in Golgi Membranes, M.Sc. Thesis, U. of Toronto.
treloar，MA（1974），嘌呤在高尔基体膜对半乳糖基转移酶的影响，硕士论文中，多伦多大学。
参见----------See Also----------
SSmicmen for other models fitted to this dataset.
SSmicmen安装到该数据集的其他车型。
举例----------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[＃最简单的形式米氏模型拟合这些数据]
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[＃添加拟合线的图]
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[＃使用局部线性]
fm3 <- nls(rate ~ conc/(K + conc), data = Puromycin,
subset = state == "treated", start = c(K = 0.05),
algorithm = "plinear")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-2-16 18:33:21
