def testBasic(self):
reaction_energies = {'R1': 1, 'R2': -10, 'R3': 395.120}
thermo_data = thermodynamic_data.ReactionBasedThermoData(
reaction_energies)
for id, r_energy in reaction_energies.iteritems():
self.assertEquals(r_energy, thermo_data.GetDGrTagZero_ForID(id))
self.assertTrue(np.isnan(thermo_data.GetDGrTagZero_ForID('FAKE_ID')))
# Fetch compounds that have data.
reaction_ids = reaction_energies.keys()
expected_energies = [reaction_energies[k] for k in reaction_ids]
actual_energies = thermo_data.GetDGrTagZero_ForIDs(reaction_ids)
self.assertEqual(expected_energies, list(actual_energies))
# Fetch mixture of compounds, one that has no data.
reaction_ids.append('FAKE_ID')
actual_energies = list(thermo_data.GetDGrTagZero_ForIDs(reaction_ids))
self.assertEqual(expected_energies, actual_energies[:-1])
self.assertTrue(np.isnan(actual_energies[-1]))
# Check that you can't get compound energies by ID.
self.assertRaises(NotImplementedError, thermo_data.GetDGfTagZero_ForID,
'FAKE_ID')
# Check that we can get thermo data from the model.
model = FakeStoichModel()
reaction_ids = model.GetReactionIDs()
expected_energies = thermo_data.GetDGrTagZero_ForIDs(reaction_ids)
actual_energies = thermo_data.GetDGrTagZero_ForModel(model)
self.assertEqual(list(expected_energies), list(actual_energies))
def testInfeasibleDummyProblem(self):
stoich_model = FakeStoichModel()
thermo = FakeInfeasibleThermoData()
kdata = kinetic_data.UniformKineticData()
opt = protein_optimizer.ProteinOptimizer(stoich_model, thermo, kdata)
res = opt.FindOptimum()
self.assertEqual(optimized_pathway.OptimizationStatus.INFEASIBLE,
res.status.status)
def testDummyProblemWideBounds(self):
stoich_model = FakeStoichModel()
thermo = FakeThermoData()
b = self.WideBounds()
iter = feasible_concentrations_iterator.FeasibleConcentrationsIterator(
stoich_model, thermo, b)
for concs in iter:
self.assertTrue(iter.Feasible(concs))
def testDummyProblem(self):
stoich_model = FakeStoichModel()
thermo = FakeThermoData()
kdata = kinetic_data.UniformKineticData()
opt = protein_optimizer.ProteinOptimizer(stoich_model, thermo, kdata)
res = opt.FindOptimum()
self.assertEqual(optimized_pathway.OptimizationStatus.SUCCESSFUL,
res.status.status)
result = res.opt_val
self.assertAlmostEqual(0.048032, result, 3)
def testDummyProblem(self):
stoich_model = FakeStoichModel()
thermo = FakeThermoData()
opt = mtdf_optimizer.MTDFOptimizer(stoich_model, thermo)
res = opt.FindMTDF()
transformed_dgr = res.dGr_tag
expected_mtdf = -np.min(transformed_dgr)
mtdf = res.opt_val
self.assertAlmostEqual(6.90989, mtdf, 3)
self.assertAlmostEqual(expected_mtdf, mtdf, 3)
def testBasic(self):
stoich_model = FakeStoichModel()
thermo = FakeThermoData()
dG0_r_prime = thermo.GetDGrTagZero_ForModel(stoich_model)
S = stoich_model.GetStoichiometricMatrix()
Ncompounds, _ = S.shape
injector = DummyInjector()
minus_dg = protein_optimizer.MinusDG(S, dG0_r_prime, injector)
# 1M concentrations should have no effect.
x = np.ones(Ncompounds)
transformed = minus_dg(x)
expected_minus_dg = -np.matrix(dG0_r_prime)
self.assertTrue((transformed == expected_minus_dg).all())
# Biological concentrations should effect the dGrs.
x = np.ones(Ncompounds) * 1e-3
transformed = minus_dg(x)
expected_minus_dg = -np.matrix(dG0_r_prime +
protein_optimizer.RT * x * S)
self.assertTrue((transformed == expected_minus_dg).all())
def testDummyProblemDifferentBounds(self):
stoich_model = FakeStoichModel()
thermo = FakeThermoData()
b = self.MyBounds()
opt = mtdf_optimizer.MTDFOptimizer(stoich_model, thermo)
res = opt.FindMTDF(concentration_bounds=b)
transformed_dgr = res.dGr_tag
expected_mtdf = -np.min(transformed_dgr)
mtdf = res.opt_val
self.assertAlmostEqual(13.117132, mtdf, 3)
self.assertAlmostEqual(expected_mtdf, mtdf, 3)
def testBasic(self):
formation_energies = {'C1': 1, 'C2': -10, 'C3': 395.120}
thermo_data = thermodynamic_data.FormationBasedThermoData(
formation_energies)
for id, formation in formation_energies.iteritems():
self.assertEquals(formation, thermo_data.GetDGfTagZero_ForID(id))
self.assertTrue(np.isnan(thermo_data.GetDGfTagZero_ForID('FAKE_ID')))
# Fetch compounds that have data.
compound_ids = formation_energies.keys()
expected_formation = np.array(
[formation_energies[k] for k in compound_ids])
actual_formation = thermo_data.GetDGfTagZero_ForIDs(compound_ids)
self.assertTrue((expected_formation == actual_formation).all())
# Fetch mixture of compounds, one that has no data.
compound_ids.append('FAKE_ID')
actual_formation = thermo_data.GetDGfTagZero_ForIDs(compound_ids)
self.assertTrue((expected_formation == actual_formation[0, :-1]).all())
self.assertTrue(np.isnan(actual_formation[0, -1]))
# Check that you can't get reaction energies by ID.
self.assertRaises(NotImplementedError, thermo_data.GetDGrTagZero_ForID,
'FAKE_ID')
# Check that we can get thermo data from the model.
model = FakeStoichModel()
S = model.GetStoichiometricMatrix()
formation_energies = thermo_data.GetDGfTagZero_ForModel(model)
expected_reaction_energies = np.dot(formation_energies, S)
reaction_energies = thermo_data.GetDGrTagZero_ForModel(model)
self.assertTrue(
(expected_reaction_energies == reaction_energies).all())