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

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”

Optional inputs

  • 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