iMAT(model, expressionRxns, threshold_lb, threshold_ub, tol, core, logfile, runtime, epsilon)

Uses the iMAT algorithm (Zur et al., 2010) to extract a context specific model using data. iMAT algorithm find the optimal trade-off between inluding high-expression reactions and removing low-expression reactions.


tissueModel = iMAT(model, expressionRxns, threshold_lb, threshold_ub)


model: input model (COBRA model structure) expressionRxns: reaction expression, expression data corresponding to model.rxns.

Note : If no gene-expression data are available for the reactions, set the value to -1

threshold_lb: lower bound of expression threshold, reactions with

expression below this value are “non-expressed”

threshold_ub: upper bound of expression threshold, reactions with

expression above this value are “expressed”

tol: minimum flux threshold for “expressed” reactions

(default 1e-8)

core: cell with reaction names (strings) that are manually put in

the high confidence set (default - no core 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

(default 1)


tissueModel: extracted model

Zur et al. (2010). iMAT: an integrative metabolic analysis tool. Bioinformatics 26, 3140-3142.

(createTissueSpecificModel.m) by S. Opdam and A. Richelle, May 2017