def test_moma_minimal_fba(self):
p = moma.MOMAProblem(self.model, self.solver)
fluxes = p.get_minimal_fba_flux('rxn_6')
self.assertAlmostEqual(fluxes['rxn_1'], 500)
self.assertAlmostEqual(fluxes['rxn_2'], 0)
self.assertAlmostEqual(fluxes['rxn_3'], 1000)
self.assertAlmostEqual(fluxes['rxn_6'], 1000)
def test_linear_moma2(self):
p = moma.MOMAProblem(self.model, self.solver)
with p.constraints(p.get_flux_var('rxn_3') == 0):
p.lin_moma2('rxn_6', 1000)
self.assertAlmostEqual(p.get_flux('rxn_1'), 500)
self.assertAlmostEqual(p.get_flux('rxn_2'), 0)
self.assertAlmostEqual(p.get_flux('rxn_3'), 0)
self.assertAlmostEqual(p.get_flux('rxn_4'), 1000)
self.assertAlmostEqual(p.get_flux('rxn_5'), 1000)
self.assertAlmostEqual(p.get_flux('rxn_6'), 1000)
def test_linear_moma(self):
p = moma.MOMAProblem(self.model, self.solver)
with p.constraints(p.get_flux_var('rxn_3') == 0):
p.lin_moma({
'rxn_3': 1000,
'rxn_4': 0,
'rxn_5': 0,
})
# The closest solution when these are constrained is for
# rxn_6 to take on a flux of zero.
self.assertAlmostEqual(p.get_flux('rxn_6'), 0)
