Transcription-based Lasso Objective Finder(TLOF) is an optimization based method to obtain a context-specific objective function for a given condition. For the complete tutorial see here
This function can be called simply, by a single line of code:
using COBRA
c,obj = TLOF(metabolic_model,lambda,flux_estimation,module_flux,selected_rxns,carbon_uptake_rxn,carbon_uptake_rate,sd)metabolic model: The Metabolic model in .xml format.
lambda: Regularization coefficient for the L1 norm term.
flux_estimation: The flux (or estimation of flux) data which is going to be used to calcualte the context-specific objective function.
It is a dataframe that has two columns, the first one contains the name of the reactions and the second one flux values.
*The next two arguments can either be given by the user or assessed by TLOF_Preprocess function
module_flux: A matrix whose dot product with the predicted flux vector returns the appropriate vector required to solve the problem.
rxn_names: A vector containing the name of the reactions with measured or estimated flux.
selected_rxns: Reactions that are meant to be included in potential cellular objective set.
carbon_uptake_rxn: The name of the reaction through which carbon is uptaken by a cell.
carbon_uptake_rate: The exchange flux associated with the carbon source, measured experimentally.
sd: The standard deviation value for carbon uptake rate measurement.
c: It is the objective function found byTLOF.
obj: The optimal value for objective function
TLOF_Preprocess can be used to find rxn_names and module_flux
rxn_names,module_flux=TLOF_Preprocess(flux_estimation)