def test_convert_objective_to_constraint(self):
the_solver = gim3e.check_solver()
tolerance_integer = gim3e.integer_tolerances[the_solver]
if cobra_version == '0.2.0':
milp_model = deepcopy(self.model)
else:
milp_model = self.model.copy()
gim3e.convert_to_irreversible_with_indicators(
milp_model, mutually_exclusive_directionality_constraint=True)
constraint_test_model = gim3e.convert_objective_to_constraint(
milp_model,
objective_sense='maximize',
fraction_of_optimum=0.9,
copy_model=True,
bound_best_optimum=False,
new_reaction_name="test_objective",
solver=the_solver,
tolerance_optimality=solver_tolerance,
tolerance_feasibility=solver_tolerance,
tolerance_barrier=0.0001 * solver_tolerance,
tolerance_integer=tolerance_integer)
constraint_test_model.reactions.get_by_id(
"test_objective").objective_coefficient = 1.
gim3e.gim3e_optimize(milp_model,
objective_sense='maximize',
the_problem=None,
solver=the_solver,
error_reporting=None,
tolerance_optimality=solver_tolerance,
tolerance_feasibility=solver_tolerance,
tolerance_barrier=0.0001 * solver_tolerance,
tolerance_integer=tolerance_integer)
solution = milp_model.solution
self.assertEqual(solution.status, "optimal")
def test_optimize_turnover_metabolite(self):
the_solver = gim3e.check_solver()
tolerance_integer = gim3e.integer_tolerances[the_solver]
if cobra_version == '0.2.0':
milp_model = deepcopy(self.model)
else:
milp_model = self.model.copy()
gim3e.convert_to_irreversible_with_indicators(
milp_model, mutually_exclusive_directionality_constraint=False)
epsilon = 1.01 * solver_tolerance
gim3e.add_turnover_metabolites(milp_model, ['glc__D_c'], epsilon)
for x in milp_model.reactions:
x.objective_coefficient = 0
milp_model.reactions.get_by_id(
'TMS_glc__D_c').objective_coefficient = 1
gim3e.gim3e_optimize(milp_model,
objective_sense='maximize',
the_problem=None,
solver=the_solver,
error_reporting=None,
tolerance_optimality=solver_tolerance,
tolerance_feasibility=solver_tolerance,
tolerance_barrier=0.0001 * solver_tolerance,
tolerance_integer=tolerance_integer)
solution = milp_model.solution
self.assertEqual(solution.status, "optimal")
def test_convert_objective_to_constraint(self):
the_solver = gim3e.check_solver()
tolerance_integer = gim3e.integer_tolerances[the_solver]
if cobra_version == '0.2.0':
milp_model = deepcopy(self.model)
else:
milp_model = self.model.copy()
gim3e.convert_to_irreversible_with_indicators(milp_model, mutually_exclusive_directionality_constraint = True)
constraint_test_model = gim3e.convert_objective_to_constraint(milp_model,
objective_sense = 'maximize',
fraction_of_optimum = 0.9,
copy_model = True,
bound_best_optimum = False,
new_reaction_name = "test_objective",
solver=the_solver,
tolerance_optimality = solver_tolerance,
tolerance_feasibility = solver_tolerance,
tolerance_barrier = 0.0001 * solver_tolerance,
tolerance_integer = tolerance_integer)
constraint_test_model.reactions.get_by_id("test_objective").objective_coefficient = 1.
gim3e.gim3e_optimize(milp_model, objective_sense = 'maximize',
the_problem=None, solver=the_solver,
error_reporting=None,
tolerance_optimality = solver_tolerance,
tolerance_feasibility = solver_tolerance,
tolerance_barrier=0.0001 * solver_tolerance,
tolerance_integer = tolerance_integer)
solution = milp_model.solution
self.assertEqual(solution.status, "optimal")
def test_solve_feasible(self):
the_solver = gim3e.check_solver()
tolerance_integer = gim3e.integer_tolerances[the_solver]
if cobra_version == '0.2.0':
test_model = deepcopy(self.model)
else:
test_model = self.model.copy()
gim3e.gim3e_optimize(test_model, objective_sense = 'maximize',
the_problem=None, solver=the_solver,
error_reporting=None,
tolerance_optimality = solver_tolerance,
tolerance_feasibility = solver_tolerance,
tolerance_barrier=0.0001 * solver_tolerance,
tolerance_integer = tolerance_integer)
solution = test_model.solution
self.assertEqual(solution.status, "optimal")
def test_solve_milp_feasible(self):
the_solver = gim3e.check_solver()
tolerance_integer = gim3e.integer_tolerances[the_solver]
if cobra_version == '0.2.0':
milp_model = deepcopy(self.model)
else:
milp_model = self.model.copy()
gim3e.convert_to_irreversible_with_indicators(milp_model, mutually_exclusive_directionality_constraint = True)
gim3e.gim3e_optimize(milp_model,objective_sense = 'maximize',
the_problem=None, solver=the_solver,
error_reporting=None,
tolerance_optimality = solver_tolerance,
tolerance_feasibility = solver_tolerance,
tolerance_barrier=0.0001 * solver_tolerance,
tolerance_integer = tolerance_integer)
solution = milp_model.solution
self.assertEqual(solution.status, "optimal")
def test_solve_feasible(self):
the_solver = gim3e.check_solver()
tolerance_integer = gim3e.integer_tolerances[the_solver]
if cobra_version == '0.2.0':
test_model = deepcopy(self.model)
else:
test_model = self.model.copy()
gim3e.gim3e_optimize(test_model,
objective_sense='maximize',
the_problem=None,
solver=the_solver,
error_reporting=None,
tolerance_optimality=solver_tolerance,
tolerance_feasibility=solver_tolerance,
tolerance_barrier=0.0001 * solver_tolerance,
tolerance_integer=tolerance_integer)
solution = test_model.solution
self.assertEqual(solution.status, "optimal")
def test_solve_milp_feasible(self):
the_solver = gim3e.check_solver()
tolerance_integer = gim3e.integer_tolerances[the_solver]
if cobra_version == '0.2.0':
milp_model = deepcopy(self.model)
else:
milp_model = self.model.copy()
gim3e.convert_to_irreversible_with_indicators(
milp_model, mutually_exclusive_directionality_constraint=True)
gim3e.gim3e_optimize(milp_model,
objective_sense='maximize',
the_problem=None,
solver=the_solver,
error_reporting=None,
tolerance_optimality=solver_tolerance,
tolerance_feasibility=solver_tolerance,
tolerance_barrier=0.0001 * solver_tolerance,
tolerance_integer=tolerance_integer)
solution = milp_model.solution
self.assertEqual(solution.status, "optimal")
def test_optimize_turnover_metabolite(self):
the_solver = gim3e.check_solver()
tolerance_integer = gim3e.integer_tolerances[the_solver]
if cobra_version == '0.2.0':
milp_model = deepcopy(self.model)
else:
milp_model = self.model.copy()
gim3e.convert_to_irreversible_with_indicators(milp_model, mutually_exclusive_directionality_constraint = False)
epsilon = 1.01 * solver_tolerance
gim3e.add_turnover_metabolites(milp_model, ['glc__D_c'], epsilon)
for x in milp_model.reactions:
x.objective_coefficient = 0
milp_model.reactions.get_by_id('TMS_glc__D_c').objective_coefficient = 1
gim3e.gim3e_optimize(milp_model, objective_sense = 'maximize',
the_problem=None, solver=the_solver,
error_reporting=None,
tolerance_optimality = solver_tolerance,
tolerance_feasibility = solver_tolerance,
tolerance_barrier=0.0001 * solver_tolerance,
tolerance_integer = tolerance_integer)
solution = milp_model.solution
self.assertEqual(solution.status, "optimal")
gim3e_dir = gim3e.__file__
n_remove_chars = len('/core/gim3e.py')
gim3e_dir = gim3e_dir[:(-1 * (n_remove_chars))]
data_dir = gim3e_dir + "data/"
# develop the sampling algorithm with E coli core as a first approach
sbml_file = 'E_coli_core_M9.xml'
cobra_model = create_cobra_model_from_sbml_file(data_dir + 'E_coli_core_M9.xml', print_time=True)
cobra_model.reactions.get_by_id('ATPM').objective_coefficient = 1
cobra_model.optimize()
cobra_model.solution.f
gim3e.gim3e_optimize(cobra_model, objective_sense = 'maximize',
the_problem = None, solver = 'cplex',
error_reporting = None,
tolerance_optimality = selected_tolerance,
tolerance_feasibility = selected_tolerance,
tolerance_barrier = 0.0001 * selected_tolerance,
tolerance_integer = 0)
cobra_model.solution.f
# Make this into a GIM3E model
gim3e_model, gim3e_FVA, penalty_score = gim3e.gim3e(cobra_model, expression_dict = {},
expression_threshold = 0,
metabolite_list = [],
fraction_growth = selected_growth,
relative_penalty_bound = selected_penalty,
solver_tolerance = selected_tolerance,
metabolite_flux_requirement = False,
monitor_all_cellular_metabolites = True,
MILP_formulation = True,
data_dir = gim3e_dir + "data/"
# develop the sampling algorithm with E coli core as a first approach
sbml_file = 'E_coli_core_M9.xml'
cobra_model = create_cobra_model_from_sbml_file(data_dir +
'E_coli_core_M9.xml',
print_time=True)
cobra_model.reactions.get_by_id('ATPM').objective_coefficient = 1
cobra_model.optimize()
cobra_model.solution.f
gim3e.gim3e_optimize(cobra_model,
objective_sense='maximize',
the_problem=None,
solver='cplex',
error_reporting=None,
tolerance_optimality=selected_tolerance,
tolerance_feasibility=selected_tolerance,
tolerance_barrier=0.0001 * selected_tolerance,
tolerance_integer=0)
cobra_model.solution.f
# Make this into a GIM3E model
gim3e_model, gim3e_FVA, penalty_score = gim3e.gim3e(
cobra_model,
expression_dict={},
expression_threshold=0,
metabolite_list=[],
fraction_growth=selected_growth,
relative_penalty_bound=selected_penalty,
solver_tolerance=selected_tolerance,
