rHVDM (version 1.38.0)

fitgene.nl: Fits the optimal kinetic parameter values for a particular gene and model.

Description

This method fits the three kinetic parameter values for a particular gene. It returns a list containing the results.

Usage

fitgene.nl(eset,gene,tHVDM,transforms,firstguess,model)

Arguments

eset

an ExpressionSet object (Biobase)

gene

the gene identifier in character format

transforms

a vector containing the kinetic parameter identifiers that have to be transformed during optimisation (optional)

tHVDM

the output of the training set

firstguess

first guess for the fitting (optional, see details)

model

model specification of the production function in character format" MM (Michelis-Menten, default) or hill (hill function)

Value

a list containing the results (see documentation for more details).

Details

An exponential transform is set by default for both the basal (Bj), degradation (Dj) and Kj rates (through the transforms argument). This forces the values for both these parameters to be positive. It also helps to reach a better fit. To turn this off let transforsm=c(). Even in this case the degradation rate will not be allowed to take non positive values as it causes problems with the differential operator used internally. The value in the vector indicates the parameter to be transformed: "Bj": basal rate of transcription, "Vj": sensitivity, "Dj": degrdation rate. The entry label indicates the transform to be applied.

This fitgene.nl() step can only be applied after a training() step. The output to the training.nl() step has to be fed through the tHVDM argument.

The firstguess argument is optional (a first guess is generated internally by default). However a first guess can be supplied by the user which can take several forms. It can either be a vector with three entries containing a first guess for the basal rate, the sensitivity, the degradation rate (in that order). Alternatively, another output from the fitgene() function (for example from a gene that has a similar expression profile) can be supplied as a firstguess argument.

The model argument is only used to choose the model (MM: Michelis-Menten, hill: Hill function).

References

M. Barenco, D. Tomescu, D. Brewer, R. Callard, J. Stark, M. Hubank (2006) Ranked predictions of p53 targets using Hidden Variable Dynamic Modelling. Genome Biology, V7(3), R25.

See Also

training,screening,HVDMreport

Examples

#load data and fit a linear model
data(HVDMexample)
rm(fiveGyMAS5)
data(HVDMexample2)
tp532<-training(eset=twodosesMAS5,genes=p53traingenes,degrate=0.8)
#formulate constraints
CONSTRAINTS<-c(350,35)
names(CONSTRAINTS)<-c("trfact1.5Gy.1.4","trfact1.hGy.1.4")
#specify individual gene models
GENEMODELS<-rep("MM",5)
GENEMODELS[c(1,3)]<-"hill"
names(GENEMODELS)<-p53traingenes
#run the model
tp53hyb<-training.nl(inputHVDM=tp532,constraints=CONSTRAINTS,genemodels=GENEMODELS)

#fit an individual gene
sHVDMex<-fitgene.nl(eset=twodosesMAS5,gene="213293_s_at",tHVDM=tp53hyb)