Data from heavy metal mixture experiments

Data are from a study of the response of the cyanobacterial self-luminescent metallothionein-based whole-cell biosensor Synechoccocus elongatus PCC 7942 pBG2120 to binary mixtures of 6 heavy metals (Zn, Cu, Cd, Ag, Co and Hg).

data("metals")

Format

A data frame with 543 observations on the following 3 variables.

metal

a factor with levels Ag AgCd Cd Co CoAg CoCd Cu CuAg CuCd CuCo CuHg CuZn Hg HgCd HgCo Zn ZnAg ZnCd ZnCo ZnHg

conc

a numeric vector of concentrations

BIF

a numeric vector of luminescence induction factors

Details

Data are from the study described by Martin-Betancor et al. (2015).

Source

Martin-Betancor, K. and Ritz, C. and Fernandez-Pinas, F. and Leganes, F. and Rodea-Palomares, I. (2015) Defining an additivity framework for mixture research in inducible whole-cell biosensors, Scientific Reports 17200.

Examples

# NOT RUN {
library(drc)

## One example from the paper by Martin-Betancor et al (2015)

## Figure 2

## Fitting a model for "Zn"
Zn.lgau <- drm(BIF ~ conc, data = subset(metals, metal == "Zn"),
fct = lgaussian(), bcVal = 0, bcAdd = 10)

## Plotting data and fitted curve
plot(Zn.lgau, log = "", type = "all",
xlab = expression(paste(plain("Zn")^plain("2+"), " ", mu, "", plain("M"))))

## Calculating effective doses
ED(Zn.lgau, 50, interval = "delta")
ED(Zn.lgau, -50, interval = "delta", bound = FALSE)
ED(Zn.lgau, 99.999,interval = "delta")  # approx. for ED0

## Fitting a model for "Cu"
Cu.lgau <- drm(BIF ~ conc, data = subset(metals, metal == "Cu"),
fct = lgaussian())

## Fitting a model for the mixture Cu-Zn
CuZn.lgau <- drm(BIF ~ conc, data = subset(metals, metal == "CuZn"),
fct = lgaussian())

## Calculating effects needed for the FA-CI plot
CuZn.effects <- CIcompX(0.015, list(CuZn.lgau, Cu.lgau, Zn.lgau),
c(-5, -10, -20, -30, -40, -50, -60, -70, -80, -90, -99, 99, 90, 80, 70, 60, 50, 40, 30, 20, 10))

## Reproducing the FA-cI plot shown in Figure 5d
plotFACI(CuZn.effects, "ED", ylim = c(0.8, 1.6), showPoints = TRUE)
# }