def test_change_coefficient(self, solver_test):
solver, old_solution, infeasible_model = solver_test
c = Metabolite("c")
c._bound = 6
x = Reaction("x")
x.lower_bound = 1.
y = Reaction("y")
y.lower_bound = 0.
x.add_metabolites({c: 1})
z = Reaction("z")
z.add_metabolites({c: 1})
z.objective_coefficient = 1
m = Model("test_model")
m.add_reactions([x, y, z])
# change an existing coefficient
lp = solver.create_problem(m)
solver.solve_problem(lp)
sol1 = solver.format_solution(lp, m)
assert sol1.status == "optimal"
solver.change_coefficient(lp, 0, 0, 2)
solver.solve_problem(lp)
sol2 = solver.format_solution(lp, m)
assert sol2.status == "optimal"
assert abs(sol1.f - 5.0) < 10 ** -3
assert abs(sol2.f - 4.0) < 10 ** -3
# change a new coefficient
z.objective_coefficient = 0.
y.objective_coefficient = 1.
lp = solver.create_problem(m)
solver.change_coefficient(lp, 0, 1, 2)
solver.solve_problem(lp)
solution = solver.format_solution(lp, m)
assert solution.status == "optimal"
assert abs(solution.x_dict["y"] - 2.5) < 10 ** -3
def test_solve_mip(self, solver_test):
solver, old_solution, infeasible_model = solver_test
if not hasattr(solver, "_SUPPORTS_MILP") or not solver._SUPPORTS_MILP:
pytest.skip("no milp support")
cobra_model = Model('MILP_implementation_test')
constraint = Metabolite("constraint")
constraint._bound = 2.5
x = Reaction("x")
x.lower_bound = 0.
x.objective_coefficient = 1.
x.add_metabolites({constraint: 2.5})
y = Reaction("y")
y.lower_bound = 0.
y.objective_coefficient = 1.
y.add_metabolites({constraint: 1.})
cobra_model.add_reactions([x, y])
float_sol = solver.solve(cobra_model)
# add an integer constraint
y.variable_kind = "integer"
int_sol = solver.solve(cobra_model)
assert abs(float_sol.f - 2.5) < 10 ** -5
assert abs(float_sol.x_dict["y"] - 2.5) < 10 ** -5
assert int_sol.status == "optimal"
assert abs(int_sol.f - 2.2) < 10 ** -3
assert abs(int_sol.x_dict["y"] - 2.0) < 10 ** -3
def test_quadratic(self, solver_test):
solver, old_solution, infeasible_model = solver_test
if not hasattr(solver, "set_quadratic_objective"):
pytest.skip("no qp support")
c = Metabolite("c")
c._bound = 2
x = Reaction("x")
x.objective_coefficient = -0.5
x.lower_bound = 0.
y = Reaction("y")
y.objective_coefficient = -0.5
y.lower_bound = 0.
x.add_metabolites({c: 1})
y.add_metabolites({c: 1})
m = Model()
m.add_reactions([x, y])
lp = solver.create_problem(m)
quadratic_obj = scipy.sparse.eye(2) * 2
solver.set_quadratic_objective(lp, quadratic_obj)
solver.solve_problem(lp, objective_sense="minimize")
solution = solver.format_solution(lp, m)
assert solution.status == "optimal"
# Respecting linear objectives also makes the objective value 1.
assert abs(solution.f - 1.) < 10 ** -3
assert abs(solution.x_dict["y"] - 1.) < 10 ** -3
assert abs(solution.x_dict["y"] - 1.) < 10 ** -3
# When the linear objectives are removed the objective value is 2.
solver.change_variable_objective(lp, 0, 0.)
solver.change_variable_objective(lp, 1, 0.)
solver.solve_problem(lp, objective_sense="minimize")
solution = solver.format_solution(lp, m)
assert solution.status == "optimal"
assert abs(solution.f - 2.) < 10 ** -3
# test quadratic from solve function
solution = solver.solve(m, quadratic_component=quadratic_obj,
objective_sense="minimize")
assert solution.status == "optimal"
assert abs(solution.f - 1.) < 10 ** -3
c._bound = 6
z = Reaction("z")
x.objective_coefficient = 0.
y.objective_coefficient = 0.
z.lower_bound = 0.
z.add_metabolites({c: 1})
m.add_reaction(z)
solution = solver.solve(m, quadratic_component=scipy.sparse.eye(3),
objective_sense="minimize")
# should be 12 not 24 because 1/2 (V^T Q V)
assert solution.status == "optimal"
assert abs(solution.f - 6) < 10 ** -3
assert abs(solution.x_dict["x"] - 2) < 10 ** -6
assert abs(solution.x_dict["y"] - 2) < 10 ** -6
assert abs(solution.x_dict["z"] - 2) < 10 ** -6
def construct_ll_test_model():
test_model = Model()
test_model.add_metabolites(Metabolite("A"))
test_model.add_metabolites(Metabolite("B"))
test_model.add_metabolites(Metabolite("C"))
EX_A = Reaction("EX_A")
EX_A.add_metabolites({test_model.metabolites.A: 1})
DM_C = Reaction("DM_C")
DM_C.add_metabolites({test_model.metabolites.C: -1})
v1 = Reaction("v1")
v1.add_metabolites({
test_model.metabolites.A: -1,
test_model.metabolites.B: 1
})
v2 = Reaction("v2")
v2.add_metabolites({
test_model.metabolites.B: -1,
test_model.metabolites.C: 1
})
v3 = Reaction("v3")
v3.add_metabolites({
test_model.metabolites.C: -1,
test_model.metabolites.A: 1
})
test_model.add_reactions([EX_A, DM_C, v1, v2, v3])
DM_C.objective_coefficient = 1
return test_model
def test_add_reactions(self, model):
r1 = Reaction('r1')
r1.add_metabolites({Metabolite('A'): -1, Metabolite('B'): 1})
r1.lower_bound, r1.upper_bound = -999999., 999999.
r2 = Reaction('r2')
r2.add_metabolites({
Metabolite('A'): -1,
Metabolite('C'): 1,
Metabolite('D'): 1
})
r2.lower_bound, r2.upper_bound = 0., 999999.
model.add_reactions([r1, r2])
r2.objective_coefficient = 3.
assert r2.objective_coefficient == 3.
assert model.reactions[-2] == r1
assert model.reactions[-1] == r2
assert isinstance(model.reactions[-2].reverse_variable,
model.problem.Variable)
coefficients_dict = model.objective.expression. \
as_coefficients_dict()
biomass_r = model.reactions.get_by_id('Biomass_Ecoli_core')
assert coefficients_dict[biomass_r.forward_variable] == 1.
assert coefficients_dict[biomass_r.reverse_variable] == -1.
assert coefficients_dict[model.reactions.r2.forward_variable] == 3.
assert coefficients_dict[model.reactions.r2.reverse_variable] == -3.
def test_gapfilling(self):
try:
get_solver_name(mip=True)
except SolverNotFound:
pytest.skip("no MILP solver found")
m = Model()
m.add_metabolites(map(Metabolite, ["a", "b", "c"]))
r = Reaction("EX_A")
m.add_reaction(r)
r.add_metabolites({m.metabolites.a: 1})
r = Reaction("r1")
m.add_reaction(r)
r.add_metabolites({m.metabolites.b: -1, m.metabolites.c: 1})
r = Reaction("DM_C")
m.add_reaction(r)
r.add_metabolites({m.metabolites.c: -1})
r.objective_coefficient = 1
U = Model()
r = Reaction("a2b")
U.add_reaction(r)
r.build_reaction_from_string("a --> b", verbose=False)
r = Reaction("a2d")
U.add_reaction(r)
r.build_reaction_from_string("a --> d", verbose=False)
# GrowMatch
result = gapfilling.growMatch(m, U)[0]
assert len(result) == 1
assert result[0].id == "a2b"
# SMILEY
result = gapfilling.SMILEY(m, "b", U)[0]
assert len(result) == 1
assert result[0].id == "a2b"
# 2 rounds of GrowMatch with exchange reactions
result = gapfilling.growMatch(m, None, ex_rxns=True, iterations=2)
assert len(result) == 2
assert len(result[0]) == 1
assert len(result[1]) == 1
assert {i[0].id for i in result} == {"SMILEY_EX_b", "SMILEY_EX_c"}
def test_gapfilling(self):
try:
get_solver_name(mip=True)
except SolverNotFound:
pytest.skip("no MILP solver found")
m = Model()
m.add_metabolites(map(Metabolite, ["a", "b", "c"]))
r = Reaction("EX_A")
m.add_reaction(r)
r.add_metabolites({m.metabolites.a: 1})
r = Reaction("r1")
m.add_reaction(r)
r.add_metabolites({m.metabolites.b: -1, m.metabolites.c: 1})
r = Reaction("DM_C")
m.add_reaction(r)
r.add_metabolites({m.metabolites.c: -1})
r.objective_coefficient = 1
U = Model()
r = Reaction("a2b")
U.add_reaction(r)
r.build_reaction_from_string("a --> b", verbose=False)
r = Reaction("a2d")
U.add_reaction(r)
r.build_reaction_from_string("a --> d", verbose=False)
# GrowMatch
result = gapfilling.growMatch(m, U)[0]
assert len(result) == 1
assert result[0].id == "a2b"
# SMILEY
result = gapfilling.SMILEY(m, "b", U)[0]
assert len(result) == 1
assert result[0].id == "a2b"
# 2 rounds of GrowMatch with exchange reactions
result = gapfilling.growMatch(m, None, ex_rxns=True, iterations=2)
assert len(result) == 2
assert len(result[0]) == 1
assert len(result[1]) == 1
assert {i[0].id for i in result} == {"SMILEY_EX_b", "SMILEY_EX_c"}
def construct_ll_test_model():
test_model = Model()
test_model.add_metabolites(Metabolite("A"))
test_model.add_metabolites(Metabolite("B"))
test_model.add_metabolites(Metabolite("C"))
EX_A = Reaction("EX_A")
EX_A.add_metabolites({test_model.metabolites.A: 1})
DM_C = Reaction("DM_C")
DM_C.add_metabolites({test_model.metabolites.C: -1})
v1 = Reaction("v1")
v1.add_metabolites({test_model.metabolites.A: -1,
test_model.metabolites.B: 1})
v2 = Reaction("v2")
v2.add_metabolites({test_model.metabolites.B: -1,
test_model.metabolites.C: 1})
v3 = Reaction("v3")
v3.add_metabolites({test_model.metabolites.C: -1,
test_model.metabolites.A: 1})
test_model.add_reactions([EX_A, DM_C, v1, v2, v3])
DM_C.objective_coefficient = 1
return test_model
def test_add_reactions(model):
r1 = Reaction("r1")
r1.add_metabolites({Metabolite("A"): -1, Metabolite("B"): 1})
r1.lower_bound, r1.upper_bound = -999999.0, 999999.0
r2 = Reaction("r2")
r2.add_metabolites({
Metabolite("A"): -1,
Metabolite("C"): 1,
Metabolite("D"): 1
})
r2.lower_bound, r2.upper_bound = 0.0, 999999.0
model.add_reactions([r1, r2])
r2.objective_coefficient = 3.0
assert r2.objective_coefficient == 3.0
assert model.reactions[-2] == r1
assert model.reactions[-1] == r2
assert isinstance(model.reactions[-2].reverse_variable,
model.problem.Variable)
coefficients_dict = model.objective.expression.as_coefficients_dict()
biomass_r = model.reactions.get_by_id("Biomass_Ecoli_core")
assert coefficients_dict[biomass_r.forward_variable] == 1.0
assert coefficients_dict[biomass_r.reverse_variable] == -1.0
assert coefficients_dict[model.reactions.r2.forward_variable] == 3.0
assert coefficients_dict[model.reactions.r2.reverse_variable] == -3.0
def test_add_reactions(self, model):
r1 = Reaction('r1')
r1.add_metabolites({Metabolite('A'): -1, Metabolite('B'): 1})
r1.lower_bound, r1.upper_bound = -999999., 999999.
r2 = Reaction('r2')
r2.add_metabolites(
{Metabolite('A'): -1, Metabolite('C'): 1, Metabolite('D'): 1})
r2.lower_bound, r2.upper_bound = 0., 999999.
model.add_reactions([r1, r2])
r2.objective_coefficient = 3.
assert r2.objective_coefficient == 3.
assert model.reactions[-2] == r1
assert model.reactions[-1] == r2
assert isinstance(model.reactions[-2].reverse_variable,
model.problem.Variable)
coefficients_dict = model.objective.expression. \
as_coefficients_dict()
biomass_r = model.reactions.get_by_id('Biomass_Ecoli_core')
assert coefficients_dict[biomass_r.forward_variable] == 1.
assert coefficients_dict[biomass_r.reverse_variable] == -1.
assert coefficients_dict[
model.reactions.r2.forward_variable] == 3.
assert coefficients_dict[
model.reactions.r2.reverse_variable] == -3.
def optimize_minimum_flux(model, objective_sense='maximize',
tolerance_optimality=1e-8, tolerance_feasibility=1e-8):
# return the flux distribution in which the total amount of fluxes is minimum while the growth is maximum
#Get the optimal wt objective value and adjust based on optimality tolerances
model.optimize()
optimal_value = deepcopy(model.solution.f)
# print('opt val: %s' % optimal_value)
if objective_sense == 'maximize':
optimal_value = floor(optimal_value/tolerance_optimality)*tolerance_optimality
else:
optimal_value = ceil(optimal_value/tolerance_optimality)*tolerance_optimality
# print('adjusted opt val: %s' % optimal_value)
#Add in the virtual objective metabolite to constrain the wt_model to the space where
#the objective was maximal
objective_metabolite = Metabolite('objective metabolite')
objective_metabolite._bound = optimal_value
if objective_sense == 'maximize':
objective_metabolite._constraint_sense = 'G'
else:
objective_metabolite._constraint_sense = 'L'
# print('objm const sense: %s, objm bound: %s' % (objective_metabolite._constraint_sense, objective_metabolite._bound))
# construct irreversible model to assure all flux values are positive
irreve_model = model.copy()
# this is necessary to avoid invalid bound error when model is changed to irreversible
for r in irreve_model.reactions:
if r.upper_bound < 0:
reverse_reaction = Reaction(r.id + "_reverse")
reverse_reaction.lower_bound = r.upper_bound * -1
reverse_reaction.upper_bound = r.lower_bound * -1
reverse_reaction.objective_coefficient = r.objective_coefficient * -1
reaction_dict = dict([(k, v*-1)
for k, v in r.metabolites.items()])
reverse_reaction.add_metabolites(reaction_dict)
irreve_model.add_reaction(reverse_reaction)
r.upper_bound, r.lower_bound = 0, 0
cobra.manipulation.modify.convert_to_irreversible(irreve_model)
objective_reaction_coefficient_dict = dict([(x.id, x.objective_coefficient)
for x in model.reactions
if x.objective_coefficient])
# this couples the objective reaction to the virtual metabolite
[irreve_model.reactions.get_by_id(k).add_metabolites({objective_metabolite: v})
for k, v in objective_reaction_coefficient_dict.items()]
# print('irregular metabolites: %s' % [(m.id, m._constraint_sense, m._bound)
# for m in irreve_model.metabolites if m._constraint_sense != 'E' or m._bound != 0])
# minimize the sum of fluxes
for r in irreve_model.reactions:
r.objective_coefficient = 1
# print([r.id for r in irreve_model.reactions if r.objective_coefficient != 1])
# print(tolerance_feasibility)
irreve_model.optimize(objective_sense='minimize',
tolerance_feasibility=tolerance_feasibility)
# adjust this to the solution of wt_model
original_flux = model.solution.x_dict
irreve_flux = irreve_model.solution.x_dict
for k in original_flux.keys():
original_flux[k] = irreve_flux[k]
# if reverse reaction exists and its flux is not zero, assign as a negative flux in wt_flux
if k + '_reverse' in irreve_flux.keys() and irreve_flux[k + '_reverse'] != 0:
if irreve_flux[k] != 0:
print('Attention: non-optimal solution')
original_flux[k] = -irreve_flux[k + '_reverse']
model.solution.status = irreve_model.solution.status
model.solution.f = sum([irreve_model.reactions.get_by_id(k).x * v for k, v in
objective_reaction_coefficient_dict.items()])
return model.solution
