TK1 {ccems} | R Documentation |
Human thymidine kinase 1 (i.e. cytosolic) data.
data(TK1)
A data frame with the following columns.
E
S
X
v
fg
year
jrnl
PEP
is Protein Expression and Purification,
EJB
is European Journal of Biochemistry,
BBRC
is Biochem Biophys Res Commun,
JBC
is Journal of Biological Chemistry.
vol
page
frstAut
index
k
All concentrations are in micromolar.
The figure number, year, first author, journal, volume and page number are all included in the dataframe.
## Note that two windows devices will end up exactly on top of each other. ## Please move device 3 below device 2 to compare their residual plots. library(ccems) ## Warning: the next line clears all existing figures!! if (!is.null(dev.list())) for (i in 2:max(dev.list())) dev.off(i); for (j in 1:2) { if (.Platform$OS.type=="windows") windows(width = 10, height = 4,restoreConsole = TRUE, ypos=ifelse(j==2,-50,0)) else X11(width=10,height=4) par(mfcol=c(2,5),mar=c(4,4,2,1)+.1) for (i in 1:5) { d=subset(TK1,index==i,select=c(E,S,k,frstAut,year)) if (j==1) hillda<-nls(k~kmax*(S/S50)^h/(1+(S/S50)^h),d,start=list(kmax=5,S50=1,h=1)) if (j==2) hillda<-nls(k~kmax*(S/S50)^h/(1+(S/S50)^h),d, start=list(kmax=5,S50=1,h=1),weights=1/k^2) print(hillda) plot(d$S,d$k,xlab="Total [dT]",log="xy", ylab="k (1/sec)", main=paste(d[1,"frstAut"],d[1,"year"])) mtext(paste("N =",length(d$k)),line=-3,side=1,font=1,cex=0.7) mtext(paste("Hill Coeff = ",format(hillda$m$getPars()["h"],digits=3),sep="") ,line=-2,side=1,font=1,cex=0.7) ## Note that the specific activity is ~16 fold higher in non-Birringer data lgx=log(d$S) upr=range(lgx)[2] lwr=range(lgx)[1] del=(upr-lwr)/50 fineX=exp(seq(lwr,upr,by=del)) lines(fineX,predict(hillda,list(S=fineX)),col="black",lwd=1) plot(hillda$m$fitted(),hillda$m$resid(),xlab="Fitted Value", ylab="Residual",mar=c(2,2,0,1)+.1) ## Note that variance increases with the mean in non-Birringer data ## and that the 2000 and 1993 Hill fits are poor at low k (and [S]) } } ## Not run: if (.Platform$OS.type=="windows") # now create a window for ccems fits windows(width = 10, height = 4,restoreConsole = TRUE, ypos=50) else X11(width=10,height=4) library(ccems) topology <- list( heads=c("E1S0"), # E1S0 = substrate free E sites=list( c=list( # c for catalytic site t=c("E1S1","E1S2","E1S3","E1S4") ) # t for tetramer ) ) # in transform below, TK1 is 25kDa => 25mg/umole g <-mkg(topology, activity=TRUE,TCC=FALSE) getKk <- function(x) {t(x$report[c(paste("E1S",0:3,"_S",sep=""), paste("kE1S",1:4,sep="")),"final",drop=FALSE])} getAIC <- function(x) { x$report["AIC","final"]} getSSE <- function(x) { x$report["SSE","final"]} outs=list(NULL) par(mfcol=c(2,5),mar=c(4,4,2,1)+.1) for (i in 1:5) { d=subset(TK1,index==i,select=c(E,S,k,frstAut,year)) plot(d$S,d$k,xlab="Total [dT]",log="xy", ylab="k (1/sec)", main=paste(d[1,"frstAut"],d[1,"year"])) names(d)[1:2]= c("ET","ST") tops=ems(d,g,maxTotalPs=3,doSpurs=FALSE)# takes ~15 sec for each dataset lgx=log(d$ST) upr=range(lgx)[2] lwr=range(lgx)[1] del=(upr-lwr)/50 fineX=exp(seq(lwr,upr,by=del)) predict <- data.frame(ET = rep(d$ET[1],length(fineX)), ST = fineX) df <- simulateData(tops[[1]],predict=predict,typeYP="k")$predict lines(df$ST,df$EY) Kk=lapply(tops,getKk) nms=names(Kk) rowList=data.frame(NULL) for (j in nms) { rowList=rbind(rowList,Kk[[j]]) } rownames(rowList)<-nms aic=sapply(tops,getAIC) sse=sapply(tops,getSSE) eDelAIC=exp(-(aic-min(aic))) wgts=eDelAIC/sum(eDelAIC) print(sum(wgts)) df=data.frame(aic,sse,wgts,rowList) M=as.matrix(rowList) ma=exp(wgts%*%log(M)) # average in space of gibbs free energy changes dataID=paste(d[1,"frstAut"],d[1,"year"],sep="") outs[[dataID]]$df=df outs[[dataID]]$ma=ma plot(tops[[1]]$d$EY,tops[[1]]$res,xlab="Fitted Value", ylab="Residual",main=tops[[1]]$mid) ## Note that the 2000 and 1993 fits are now improved } outs=outs[-1] # remove leading NULL print(outs) # compare model averages across datasets par(mfrow=c(1,1)) ## End(Not run)