def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
save_sbml_model(model, TEST_MODEL_COPY)
model_copy = load_sbml_model(TEST_MODEL_COPY, kind=GPR_CONSTRAINED)
self.assertEqual(model.id, model_copy.id)
self.assertListEqual(model.metabolites.keys(), model_copy.metabolites.keys())
self.assertListEqual(model.reactions.keys(), model_copy.reactions.keys())
self.assertDictEqual(model.stoichiometry, model_copy.stoichiometry)
self.assertDictEqual(model.bounds, model_copy.bounds)
self.assertListEqual(model.genes.keys(), model_copy.genes.keys())
self.assertDictEqual(model.rules, model_copy.rules)
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
simplify(model)
solution = FBA(model)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
solution = gene_deletion(model, ROOM_GENE_KO, 'ROOM')
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.values[model.detect_biomass_reaction()], ROOM_GROWTH_RATE, 3)
self.assertAlmostEqual(solution.values['R_EX_succ_e'], ROOM_SUCC_EX, 3)
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
make_irreversible(model)
self.assertTrue(all([not reaction.reversible for reaction in model.reactions.values()]))
solution = FBA(model)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=CONSTRAINT_BASED)
fix_bigg_model(model)
write_model_to_file(model, PLAIN_TEXT_COPY)
model_copy = read_model_from_file(PLAIN_TEXT_COPY, kind=CONSTRAINT_BASED)
solution = FBA(model_copy)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def benchmark_methods(modelpath):
model = load_sbml_model(modelpath, GPR_CONSTRAINED)
fix_bigg_model(model)
benchmark_method('FBA', model)
benchmark_method('pFBA', model)
benchmark_method('qpFBA', model)
benchmark_method('MOMA', model)
benchmark_method('lMOMA', model)
benchmark_method('ROOM', model)
def test_gapFind_glpk_core_sbml_b005(self):
solver = GlpkSolverLazy()
model = '../../../examples/models/gapFill/ecoli_core_model_test.xml'
reactionsDB = '../../../examples/models/gapFill/ecoli_core_model.xml'
self.model = load_sbml_model(model, kind=CONSTRAINT_BASED)
fix_bigg_model(self.model)
framed_result = GapFill(self.model, reactionsDB, solver, "R_Biomass_Ecoli_core_w_GAM", 0.05, 'sbml')
self.assertEquals(framed_result[1], ['R_PGK', 'R_PGM', 'R_PYK'])
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=CONSTRAINT_BASED)
write_model_to_file(model, PLAIN_TEXT_COPY)
model_copy = read_model_from_file(PLAIN_TEXT_COPY, kind=CONSTRAINT_BASED)
self.assertListEqual(sorted(model.metabolites.keys()),
sorted(model_copy.metabolites.keys()))
self.assertListEqual(model.reactions.keys(), model_copy.reactions.keys())
self.assertDictEqual(dict(model.stoichiometry),
dict(model_copy.stoichiometry))
self.assertDictEqual(model.bounds, model_copy.bounds)
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
r_id1 = 'R_PGI'
r_id2 = 'R_G6PDH2r'
ratio = 2.0
add_ratio_constraint(model, r_id1, r_id2, ratio)
solution = FBA(model, get_shadow_prices=True, get_reduced_costs=True)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertEqual(solution.values[r_id1] / solution.values[r_id2], ratio)
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
simplify(model)
make_irreversible(model)
simplify(model) #remove directionally blocked reactions
model2 = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model2)
make_irreversible(model2)
simplify(model2)
self.assertEqual(model.id, model2.id)
self.assertListEqual(model.metabolites.keys(), model2.metabolites.keys())
self.assertListEqual(model.reactions.keys(), model2.reactions.keys())
self.assertDictEqual(model.stoichiometry, model2.stoichiometry)
self.assertDictEqual(model.bounds, model2.bounds)
self.assertListEqual(model.genes.keys(), model2.genes.keys())
self.assertDictEqual(model.rules, model2.rules)
def benchmark_build_problem(modelpath, n=10):
model = load_sbml_model(modelpath, GPR_CONSTRAINED)
fix_bigg_model(model)
print 'benchmarking build problem for', n, 'instances:',
tstart = time()
for i in range(n):
solver = solver_instance()
solver.build_problem(model)
tend = time()
print 'took', tend - tstart
def test_gapFind_glpk_full_sbml_b087(self):
solver = GlpkSolver()
model = '../../../examples/models/gapFill/ecoli_core_model_test.xml'
reactionsDB = '../../../examples/models/gapFill/iAF1260.xml'
self.model = load_sbml_model(model, kind=CONSTRAINT_BASED)
fix_bigg_model(self.model)
framed_result = GapFill(self.model, reactionsDB, solver, "R_Biomass_Ecoli_core_w_GAM", 0.87, 'sbml')
# R_Htex is required to produce M_h_p, which is a reactant in R_ATPS4rpp
self.assertEquals(framed_result[1], ['R_ATPS4rpp', 'R_Htex', 'R_PGK', 'R_PGM'])
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
solution1 = qpFBA(model)
solution2 = FBA(model)
self.assertEqual(solution1.status, Status.OPTIMAL)
self.assertEqual(solution2.status, Status.OPTIMAL)
growth1 = solution1.values[model.detect_biomass_reaction()]
growth2 = solution2.values[model.detect_biomass_reaction()]
self.assertAlmostEqual(growth1, growth2, places=4)
norm1 = sum([abs(solution1.values[r_id]) for r_id in model.reactions])
norm2 = sum([abs(solution2.values[r_id]) for r_id in model.reactions])
self.assertLessEqual(norm1, norm2 + 1e-6)
def benchmark_solving_stage(modelpath, n=10):
model = load_sbml_model(modelpath, GPR_CONSTRAINED)
fix_bigg_model(model)
print 'benchmarking solving stage for', n, 'repetitions:',
solver = solver_instance()
solver.build_problem(model)
tstart = time()
for i in range(n):
FBA(model, solver=solver)
tend = time()
print 'took', tend - tstart
def test_gapFind_glpk_lazy(self):
""" Finds gaps in 7 models using Glpk
"""
for i in range(1,8):
solver = GlpkSolverLazy(tol_int="default")
model = '../../../examples/models/gapFind/ecoli_core_model' + str(i) + '.xml'
self.model = load_sbml_model(model, kind=CONSTRAINT_BASED)
fix_bigg_model(self.model)
framed_result = GapFind(self.model, solver)
COBRA_mets_result_filename = '../../../examples/models/gapFind/gapFind_results/ecoli0' + str(i) + '_mets_glpk.txt'
COBRA_rxns_result_filename = '../../../examples/models/gapFind/gapFind_results/ecoli0' + str(i) + '_rxns_glpk.txt'
result = self.compare_to_COBRA(framed_result, COBRA_mets_result_filename, COBRA_rxns_result_filename)
self.assertTrue(result)
def test_gapFind_gurobi(self):
""" Finds gaps in 7 models using Gurobi
"""
for i in range(1,8):
if i == 6:
continue
solver = GurobiSolver()
model = '../../../examples/models/gapFind/ecoli_core_model' + str(i) + '.xml'
self.model = load_sbml_model(model, kind=CONSTRAINT_BASED)
fix_bigg_model(self.model)
framed_result = GapFind(self.model, solver)
COBRA_mets_result_filename = '../../../examples/models/gapFind/gapFind_results/ecoli0' + str(i) + '_mets_gurobi.txt'
COBRA_rxns_result_filename = '../../../examples/models/gapFind/gapFind_results/ecoli0' + str(i) + '_rxns_gurobi.txt'
result = self.compare_to_COBRA(framed_result, COBRA_mets_result_filename, COBRA_rxns_result_filename)
self.assertTrue(result)
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
variability = FVA(model)
self.assertTrue(all([lb <= ub if lb is not None and ub is not None else True
for lb, ub in variability.values()]))
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
solution = FBA(model, get_shadow_prices=True, get_reduced_costs=True)
self.assertEqual(solution.status, Status.OPTIMAL)
self.assertAlmostEqual(solution.fobj, GROWTH_RATE, places=2)
def update(self):
BR = self.environment
# calculating and updating the glucose uptake constraint
rid = BR.metabolites.index('R_EX_glc_e')
vlb_glc = float(-10 * BR.S[rid] / (BR.S[rid] + 1))
self.fba_constraints['R_EX_glc_e'] = (vlb_glc, 0)
# ideal aerobic condition
self.fba_constraints['R_EX_o2_e'] = (-15, None)
### Setup
EC_1260_MODEL = 'models/Ec_iAF1260_gene_names.xml'
ec1260 = load_sbml_model(EC_1260_MODEL, kind=CONSTRAINT_BASED)
fix_bigg_model(ec1260)
# Add 1-3-PDO pathway
add_reaction_from_str(ec1260, 'R_glycerol_dehydratase: M_glyc_c + M_nadh_c + M_h_c --> M_1_3_pdo_c + M_nad_c')
add_reaction_from_str(ec1260, 'R_1_3_PDOtr: M_1_3_pdo_c <-> M_1_3_pdo_e')
add_reaction_from_str(ec1260, 'R_EX_1_3_pdo_e: M_1_3_pdo_e -->')
ec = Ecoli(ec1260, id='ecoli')
# make envelope strains
r_substrate, r_target1, r_target2, r_oxygen = 'R_EX_glc_e', 'R_EX_1_3_pdo_e', 'R_EX_3_hp_e', 'R_EX_o2_e'
aerobic_constraints = {r_substrate: (-10, 0), r_oxygen: (-15, None)}
strains = dyssco.make_envelope_strains(ec, 'R_EX_glc_e', 'R_EX_1_3_pdo_e', N=5, constraints=aerobic_constraints)
def testRun(self):
model = load_sbml_model(SMALL_TEST_MODEL, kind=GPR_CONSTRAINED)
fix_bigg_model(model)
essential = essential_genes(model)
self.assertListEqual(essential, ESSENTIAL_GENES)
def setUp(self):
self.model = load_sbml_model(SMALL_TEST_MODEL, kind=CONSTRAINT_BASED)
fix_bigg_model(self.model)
@Author: Kai Zhuang
"""
__author__ = 'kaizhuang'
import matplotlib.pyplot as plt
from framed.io_utils.sbml import load_sbml_model, CONSTRAINT_BASED
from framed.core.fixes import fix_bigg_model
from framed.bioreactor import *
from framed.bioreactor.bioreactors import *
### Basic Setup
SMALL_TEST_MODEL = 'models/ecoli_core_model.xml'
ec_core_model = load_sbml_model(SMALL_TEST_MODEL, kind=CONSTRAINT_BASED)
fix_bigg_model(ec_core_model)
### Defining the Ecoli class
# In the Ecoli class, the method update(), which is an abstract method in the superclass Organism, is defined.
# The update() method describes how Ecoli will respond to changes in metabolite concentrations in its environment.
# Usually, the relevant FBA uptake constraints are calculated and updated in the update() method.
class Ecoli(Organism):
def update(self):
BR = self.environment
# calculating and updating the glucose uptake constraint
rid = BR.metabolites.index('R_EX_glc_e')
"""
__author__ = 'kaizhuang'
import matplotlib.pyplot as plt
from framed.io_utils.sbml import load_sbml_model, CONSTRAINT_BASED
from framed.core.fixes import fix_bigg_model
from framed.analysis.dfba import *
from framed.bioreactor.bioreactors import *
from framed.bioreactor import *
### Basic Setup
SMALL_TEST_MODEL = 'models/Ec_iAF1260_gene_names.xml'
ec1260 = load_sbml_model(SMALL_TEST_MODEL, kind=CONSTRAINT_BASED)
fix_bigg_model(ec1260)
### Defining the Ecoli class
# In the Ecoli class, the method update(), which is an abstract method in the superclass Organism, is defined.
# The update() method describes how Ecoli will respond to changes in metabolite concentrations in its environment.
# Usually, the relevant FBA uptake constraints are calculated and updated in the update() method.
class Ecoli(Organism):
def update(self):
BR = self.environment
# calculating and updating the glucose uptake constraint
rid = BR.metabolites.index('R_EX_glc_e')
vlb_glc = float(-10 * BR.S[rid] / (BR.S[rid] + 1))
