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iMAT: Integrative Metabolic Analysis Tool (MIP)

Example implementation of iMAT using MIOM. Note that this implementation supports also custom weights for the reactions. By default, weights for reactions in the "active" set are set to 1, and reactions in the "inactive" set are set to -1, so the objective function is exactly the same as the original iMAT.

Cite

Shlomi, T., Cabili, M. N., Herrgård, M. J., Palsson, B. Ø., & Ruppin, E. (2008). Network-based prediction of human tissue-specific metabolism. Nature biotechnology, 26(9), 1003-1010.

import miom

# Use the iHuman-GEM model
m = miom.load_gem('@SysBioChalmers/Human-GEM/v1.9.0')

# Add all the reactions from the Cholesterol pathway to the highly expressed set
RH = m.find_reactions_from_pathway("Cholesterol metabolism")
# Add reactions from the pyruvate metabolism to the lowly expressed set
RL = -1 * m.find_reactions_from_pathway("Pyruvate metabolism")
w = RH + RL
print("RH:", sum(RH), "RL:", sum(abs(RL)))

m = (miom
     .load(m, solver=miom.Solvers.GUROBI)
     .setup(int_tol=1e-8, opt_tol=0.01, verbosity=1)
     .steady_state()
     .subset_selection(w)
     .solve(max_seconds=30)
     .select_subnetwork(
          mode=miom.ExtractionMode.ABSOLUTE_FLUX_VALUE,
          comparator=miom.Comparator.GREATER_OR_EQUAL,
          value=1e-8
     )
     .network)
print(m.num_reactions)