range_delta: Bounds of delta_i

Description

Computes the bounds of the actual mutation effect \(\delta_i\) such as all mutant concentrations are between 0 and total concentration, for a mutation targeting enzyme \(i\)

Usage

range_delta(E_res,alpha_fun,i_fun,tol_fun=0.0001)

Arguments

E_res

Numeric vector of resident enzyme concentrations

alpha_fun

Numeric matrix of redistribution coefficients

i_fun

Integer number indicating the enzyme targeted by the mutation

tol_fun

Numeric and positive value. Accuracy for delta bounds. Default is 0.0001

Value

Numeric vector of the inferior and the superior bounds of actual mutation effect \(\delta_i\)

Details

This function range.delta computes the bounds of \(\delta_i\) such as all mutant concentrations are between 0 and total concentration \(Etot\), for a mutation targeting enzyme i_fun. Mutant concentrations are equal to resident concentrations plus \(\alpha_ij * \delta_i\) \((see function \code{\link{mut.E.indirect}})\). For any enzyme \(j\), mutant value is \(E_j^r + \alpha_ij * \delta_i\).

The inferior (resp. superior) bound of \(\delta_i\) corresponds to minimal (resp. maximal) value of \(\delta_i\) such as all mutant concentrations are superior or equal to 0 and inferior or equal to \(Etot\), with at least one mutant concentration equal to 0 or \(Etot\).

tol_fun is the accuracy (or allowed tolerance) for \(\delta\) bounds. It allows to avoid asymptote problem when computing the RNV.

Examples

Run this code

# NOT RUN {
beta <- matrix(c(1,10,5,0.1,1,0.5,0.2,2,1),nrow=3)
Er <- c(30,30,30)
correl <- "CRPos"
alpha <- alpha_ij(Er,correl,beta)

#mutant enzyme
i <- 1

range_delta(Er,alpha,i)

# }