- INIT(model, weights, tol, runtime, logfile, epsilon)¶
Use the INIT algorithm (Agren et al., 2012) to extract a context specific model using data. INIT algorithm find the optimal trade-off between inluding and removing reactions based on their given weights. If desired, accumulation of certain metabolites can be allowed or even forced.
tissueModel = INIT(model, weights, tol, runtime, logfile)
model: input model (COBRA model structure) weights: column with positive and negative weights for each reaction
positive weights are reactions with high expression, negative weigths for reaction with low expression (must be same length as model.rxns)
- OPTIONAL INPUTS:
- tol: minimum flux threshold for “expressed” reactions
logfile: name of the file to save the MILP log (string) runtime: maximum solve time for the MILP (default value - 7200s) epsilon: value added/subtracted to upper/lower bounds
tissueModel: extracted model
Agren et al. (2012). Reconstruction of genome-scale active metabolic networks for 69 human cell types and 16 cancer types using INIT. PLoS Comput. Biol. 8, e1002518.