predict_eff_allE0: Prediction of effective equilibrium for all possible initial...
In SimEvolEnzCons: Simulation of Evolution of Enzyme Under Constraints

Description Usage Arguments Details Value

Gives the effective equilibrium for relative concentrations for various initial concentrations

1	predict_eff_allE0(B_fun,A_fun,correl_fun,Etot_fun=100,X_fun=1, tol=0.00000001)

`B_fun`	Numeric vector of global co-regulation coefficients. Same length as `E_ini_fun`.
`A_fun`	Numeric vector of activities
`correl_fun`	Character string indicating the abbreviation of the constraint applied on the system
`Etot_fun`	Numeric value of total concentration
`X_fun`	Numeric value. Default is `1`
`tol`	Tolerance for function `uniroot`

Effective equilibrium is computed with function predict_eff.

WARNING: Function predict_eff_allE0 is only available for three enzymes! Length of A_fun and B_fun need to be 3.

Each relative concentration is taken between 0 and 1 by 0.01, then triplet of relative concentrations are sorted to have a sum equal to 1. Then relative concentrations are multiplied by Etot_fun to have initial concentrations.

For parameter correl_fun, authorized input are "RegNeg", "CRPos" and "CRNeg".

Invisible list of 3 elements:

$all_eq_eff: Dataframe of 4 851 rows and eight columns (named e1,e2,e3,tau,E1,E2,E3,J) for effective equilibrium from possible initial concentrations. Each row corresponds to a set of initial concentrations, and columns are respectively relative concentrations ($e1,$e2,$e3), driving variable τ ($tau), absolute concentrations $E1,$E2,$E3 and flux $J at effective equilibrium;
$all_E0: Dataframe of 4 851 rows and three columns corresponding to initial concentrations. Each row is a triplet of initial concentrations;
$param: List of input parameters