Gives the effective equilibrium for relative concentrations for various initial concentrations
predict_eff_allE0(B_fun,A_fun,correl_fun,Etot_fun=100,X_fun=1, tol=0.00000001)Numeric vector of global co-regulation coefficients. Same length as E_ini_fun.
Numeric vector of activities
Character string indicating the abbreviation of the constraint applied on the system
Numeric value of total concentration
Numeric value. Default is 1
Tolerance for function uniroot
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 \(\tau\) ($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
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".