def min_uptake_objective(model):
objective = {}
for r_id in model.get_exchange_reactions():
compounds = model.reactions[r_id].get_substrates()
metabolite = model.metabolites[compounds[0]]
formulas = metabolite.metadata['FORMULA'].split(';')
weight = molecular_weight(formulas[0])
if weight is not None:
objective['f_' + r_id] = weight
return objective
def biomass_weight(biomass_id, coeffs, model):
bio_weight = 0
for m_id, coeff in coeffs.items():
metabolite = model.metabolites[m_id]
if 'FORMULA' in metabolite.metadata:
formulae = metabolite.metadata['FORMULA'].split(';')
met_weight = np.mean([molecular_weight(formula) for formula in formulae])
contribution = -coeff * met_weight
bio_weight += contribution
# print '\t'.join([biomass_id, m_id, str(met_weight), str(coeff), str(contribution)])
else:
warn('Unable to normalize {} due to missing formula for {}:'.format(biomass_id, m_id))
break
return bio_weight
def minimal_medium(model,
exchange_reactions=None,
direction=-1,
min_mass_weight=False,
min_growth=1,
max_uptake=100,
max_compounds=None,
n_solutions=1,
validate=True,
abstol=1e-6):
""" Minimal medium calculator. Determines the minimum number of medium components for the organism to grow.
Notes:
There are two options provided:
* simply minimize the total number of components
* minimize nutrients by molecular weight (as implemented by Zarecki et al, 2014)
Args:
model (CBModel): model
exchange_reactions: list of exchange reactions (if not provided all model exchange reactions are used)
direction (int): direction of uptake reactions (negative or positive, default: -1)
min_mass_weight (bool): minimize by molecular weight of nutrients (default: False)
min_growth (float): minimum growth rate (default: 1)
max_uptake (float): maximum uptake rate (default: 100)
max_compounds (int): limit maximum number of compounds (optional)
n_solutions (int): enumerate multiple solutions (default: 1)
validate (bool): validate solution using FBA (for debugging purposes, default: False)
abstol (float): tolerance for detecting a non-zero exchange flux (default: 1e-6)
Returns:
list: minimal set of exchange reactions
Solution: solution from solver
"""
# TODO: 2_program_MMsolverClone.prof
if exchange_reactions is None:
exchange_reactions = list(model.get_exchange_reactions())
solver = solver_instance(model)
# TODO: 2_program_MMsolver.prof
#bck_bounds = {r_id: (model.reactions[r_id].lb, model.reactions[r_id].ub) for r_id in exchange_reactions}
if direction < 0:
solver.set_lower_bounds(
{r_id: -max_uptake
for r_id in exchange_reactions})
else:
solver.set_upper_bounds(
{r_id: max_uptake
for r_id in exchange_reactions})
solver.set_lower_bounds({model.biomass_reaction: min_growth})
#bck_bounds[model.biomass_reaction] = (model.reactions[model.biomass_reaction].lb, model.reactions[model.biomass_reaction].ub)
for r_id in exchange_reactions:
solver.add_variable('y_' + r_id,
0,
1,
vartype=VarType.BINARY,
update_problem=False)
solver.update()
for r_id in exchange_reactions:
if direction < 0:
solver.add_constraint('c_' + r_id, {
r_id: 1,
'y_' + r_id: max_uptake
},
'>',
0,
update_problem=False)
else:
solver.add_constraint('c_' + r_id, {
r_id: 1,
'y_' + r_id: -max_uptake
},
'<',
0,
update_problem=False)
if max_compounds:
lhs = {'y_' + r_id: 1 for r_id in exchange_reactions}
solver.add_constraint('max_cmpds',
lhs,
'<',
max_compounds,
update_problem=False)
solver.update()
if min_mass_weight:
objective = {}
for r_id in exchange_reactions:
if direction < 0:
compounds = model.reactions[r_id].get_substrates()
else:
compounds = model.reactions[r_id].get_products()
if len(compounds) > 1:
warn('Multiple compounds in exchange reaction (ignored)')
continue
if len(compounds) == 0:
warn('No compounds in exchange reaction (ignored)')
continue
metabolite = model.metabolites[compounds[0]]
if 'FORMULA' not in metabolite.metadata:
warn('No formula for compound (ignored)')
continue
formulas = metabolite.metadata['FORMULA'].split(';')
if len(formulas) > 1:
warn('Multiple formulas for compound')
weight = molecular_weight(formulas[0])
objective['y_' + r_id] = weight
else:
objective = {'y_' + r_id: 1 for r_id in exchange_reactions}
solution = solver.solve(objective, minimize=True)
if solution.status != Status.OPTIMAL:
# warn('No solution found')
return None, solution
medium = set(r_id for r_id in exchange_reactions
if (direction < 0 and solution.values[r_id] < -abstol
or direction > 0 and solution.values[r_id] > abstol))
if validate:
validate_solution(model, medium, exchange_reactions, direction,
min_growth, max_uptake)
if n_solutions == 1:
return medium, solution
else:
medium_list = [medium]
solutions = [solution]
for i in range(1, n_solutions):
constr_id = 'iteration_{}'.format(i)
previous_sol = {'y_' + r_id: 1 for r_id in medium}
solver.add_constraint(constr_id, previous_sol, '<',
len(previous_sol) - 1)
solution = solver.solve(objective, minimize=True)
if solution.status != Status.OPTIMAL:
break
medium = set(
r_id for r_id in exchange_reactions
if (direction < 0 and solution.values[r_id] < -abstol
or direction > 0 and solution.values[r_id] > abstol))
medium_list.append(medium)
solutions.append(solution)
return medium_list, solutions
def minimal_medium(model,
exchange_reactions=None,
direction=-1,
min_mass_weight=False,
min_growth=1,
max_uptake=100,
max_compounds=None,
n_solutions=1,
validate=True,
abstol=1e-6,
warnings=True,
use_pool=False,
pool_gap=None,
solver=None):
""" Minimal medium calculator. Determines the minimum number of medium components for the organism to grow.
Notes:
There are two options provided:
* simply minimize the total number of components
* minimize nutrients by molecular weight (as implemented by Zarecki et al, 2014)
Args:
model (CBModel): model
exchange_reactions: list of exchange reactions (if not provided all model exchange reactions are used)
direction (int): direction of uptake reactions (negative or positive, default: -1)
min_mass_weight (bool): minimize by molecular weight of nutrients (default: False)
min_growth (float): minimum growth rate (default: 1)
max_uptake (float): maximum uptake rate (default: 100)
max_compounds (int): limit maximum number of compounds (optional)
n_solutions (int): enumerate multiple solutions (default: 1)
validate (bool): validate solution using FBA (for debugging purposes, default: False)
abstol (float): tolerance for detecting a non-zero exchange flux (default: 1e-6)
Returns:
list: minimal set of exchange reactions
Solution: solution from solver
"""
def warn_wrapper(message):
if warnings:
warn(message)
if exchange_reactions is None:
exchange_reactions = list(model.get_exchange_reactions())
if not solver:
solver = solver_instance(model)
persistent = True
else:
persistent = False
solver.set_lower_bounds({model.biomass_reaction: min_growth})
for r_id in exchange_reactions:
solver.add_variable('y_' + r_id,
0,
1,
vartype=VarType.BINARY,
update_problem=False,
persistent=persistent)
solver.update()
for r_id in exchange_reactions:
if direction < 0:
solver.add_constraint('c_' + r_id, {
r_id: 1,
'y_' + r_id: max_uptake
},
'>',
0,
update_problem=False,
persistent=persistent)
else:
solver.add_constraint('c_' + r_id, {
r_id: 1,
'y_' + r_id: -max_uptake
},
'<',
0,
update_problem=False,
persistent=persistent)
if max_compounds:
lhs = {'y_' + r_id: 1 for r_id in exchange_reactions}
solver.add_constraint('max_cmpds',
lhs,
'<',
max_compounds,
update_problem=False,
persistent=persistent)
solver.update()
if min_mass_weight:
objective = {}
for r_id in exchange_reactions:
if direction < 0:
compounds = model.reactions[r_id].get_substrates()
else:
compounds = model.reactions[r_id].get_products()
if len(compounds) > 1:
warn_wrapper(
'Multiple compounds in exchange reaction (ignored)')
continue
if len(compounds) == 0:
warn_wrapper('No compounds in exchange reaction (ignored)')
continue
metabolite = model.metabolites[compounds[0]]
if 'FORMULA' not in metabolite.metadata:
warn_wrapper('No formula for compound (ignored)')
continue
formulas = metabolite.metadata['FORMULA'].split(';')
if len(formulas) > 1:
warn_wrapper('Multiple formulas for compound')
weight = molecular_weight(formulas[0])
objective['y_' + r_id] = weight
else:
objective = {'y_' + r_id: 1 for r_id in exchange_reactions}
result, ret_sols = None, None
if direction < 0:
constraints = {
r_id: (-max_uptake, model.reactions[r_id].ub)
for r_id in exchange_reactions
}
else:
constraints = {
r_id: (model.reactions[r_id].lb, max_uptake)
for r_id in exchange_reactions
}
if n_solutions == 1:
solution = solver.solve(objective,
minimize=True,
constraints=constraints,
get_values=exchange_reactions)
if solution.status != Status.OPTIMAL:
warn_wrapper('No solution found')
result, ret_sols = None, solution
else:
medium = get_medium(solution, exchange_reactions, direction,
abstol)
if validate:
validate_solution(model, medium, exchange_reactions, direction,
min_growth, max_uptake)
result, ret_sols = medium, solution
elif use_pool:
solutions = solver.solve(objective,
minimize=True,
constraints=constraints,
get_values=exchange_reactions,
pool_size=n_solutions,
pool_gap=pool_gap)
if solutions is None:
result, ret_sols = [], []
else:
media = [
get_medium(solution, exchange_reactions, direction, abstol)
for solution in solutions
]
result, ret_sols = media, solutions
else:
media = []
solutions = []
for i in range(0, n_solutions):
if i > 0:
constr_id = 'iteration_{}'.format(i)
previous_sol = {'y_' + r_id: 1 for r_id in medium}
solver.add_constraint(constr_id, previous_sol, '<',
len(previous_sol) - 1)
solution = solver.solve(objective,
minimize=True,
constraints=constraints,
get_values=exchange_reactions)
if solution.status != Status.OPTIMAL:
break
medium = get_medium(solution, exchange_reactions, direction,
abstol)
media.append(medium)
solutions.append(solution)
result, ret_sols = media, solutions
if not persistent:
solver.clean_up()
return result, ret_sols
def minimal_medium(model, exchange_reactions=None, direction=-1, min_mass_weight=False, min_growth=1,
max_uptake=100, max_compounds=None, n_solutions=1, validate=True, abstol=1e-6,
warnings=True, use_pool=False, pool_gap=None, solver=None, milp=True):
""" Minimal medium calculator. Determines the minimum number of medium components for the organism to grow.
Notes:
There are two options provided:
* simply minimize the total number of components
* minimize nutrients by molecular weight (as implemented by Zarecki et al, 2014)
Args:
model (CBModel): model
exchange_reactions: list of exchange reactions (if not provided all model exchange reactions are used)
direction (int): direction of uptake reactions (negative or positive, default: -1)
min_mass_weight (bool): minimize by molecular weight of nutrients (default: False)
min_growth (float): minimum growth rate (default: 1)
max_uptake (float): maximum uptake rate (default: 100)
max_compounds (int): limit maximum number of compounds (optional)
n_solutions (int): enumerate multiple solutions (default: 1)
validate (bool): validate solution using FBA (for debugging purposes, default: False)
abstol (float): tolerance for detecting a non-zero exchange flux (default: 1e-6)
Returns:
list: minimal set of exchange reactions
Solution: solution from solver
"""
def warn_wrapper(message):
if warnings:
warn(message)
if exchange_reactions is None:
exchange_reactions = model.get_exchange_reactions()
if not solver:
solver = solver_instance(model)
persistent = True
else:
persistent = False
if not milp and max_compounds is not None:
raise RuntimeError("max_compounds can only be used with MILP formulation")
if not milp and n_solutions > 1:
raise RuntimeError("n_solutions can only be used with MILP formulation")
if milp:
for r_id in exchange_reactions:
solver.add_variable('y_' + r_id, 0, 1, vartype=VarType.BINARY, update_problem=False, persistent=persistent)
else:
for r_id in exchange_reactions:
solver.add_variable('f_' + r_id, 0, max_uptake, update_problem=False, persistent=persistent)
solver.update()
if milp:
for r_id in exchange_reactions:
if direction < 0:
solver.add_constraint('c_' + r_id, {r_id: 1, 'y_' + r_id: max_uptake}, '>', 0,
update_problem=False, persistent=persistent)
else:
solver.add_constraint('c_' + r_id, {r_id: 1, 'y_' + r_id: -max_uptake}, '<', 0,
update_problem=False, persistent=persistent)
if max_compounds:
lhs = {'y_' + r_id: 1 for r_id in exchange_reactions}
solver.add_constraint('max_cmpds', lhs, '<', max_compounds, update_problem=False, persistent=persistent)
else:
for r_id in exchange_reactions:
if direction < 0:
solver.add_constraint('c_' + r_id, {r_id: 1, 'f_' + r_id: 1}, '>', 0,
update_problem=False, persistent=persistent)
else:
solver.add_constraint('c_' + r_id, {r_id: 1, 'f_' + r_id: -1}, '<', 0,
update_problem=False, persistent=persistent)
solver.update()
valid_reactions = []
if min_mass_weight:
objective = {}
multiple_compounds =[]
no_compounds = []
no_formula = []
multiple_formulas = []
invalid_formulas = []
for r_id in exchange_reactions:
if direction < 0:
compounds = model.reactions[r_id].get_substrates()
else:
compounds = model.reactions[r_id].get_products()
if len(compounds) > 1:
multiple_compounds.append(r_id)
continue #TODO should not allow reaction to be used
if len(compounds) == 0:
no_compounds.append(r_id)
continue #TODO should not allow reaction to be used
metabolite = model.metabolites[compounds[0]]
if 'FORMULA' not in metabolite.metadata:
no_formula.append(metabolite.id)
continue #TODO should not allow reaction to be used
formulas = metabolite.metadata['FORMULA'].split(';')
if len(formulas) > 1:
multiple_formulas.append(metabolite.id)
weight = molecular_weight(formulas[0])
if weight is None:
invalid_formulas.append(metabolite.id)
continue
if milp:
objective['y_' + r_id] = weight
else:
objective['f_' + r_id] = weight
valid_reactions.append(r_id)
if multiple_compounds:
warn_wrapper("Reactions ignored (multiple compounds): " + ','.join(multiple_compounds))
if no_compounds:
warn_wrapper("Reactions ignored (no compounds): " + ','.join(no_compounds))
if multiple_compounds:
warn_wrapper("Compounds ignored (no formula): " + ','.join(no_formula))
if multiple_formulas:
warn_wrapper("Coupounds with multiple formulas (using first): " + ','.join(multiple_formulas))
if invalid_formulas:
warn_wrapper("Coupounds ignored (invalid formula): " + ','.join(invalid_formulas))
else:
if milp:
objective = {'y_' + r_id: 1 for r_id in exchange_reactions}
else:
objective = {'f_' + r_id: 1 for r_id in exchange_reactions}
valid_reactions = exchange_reactions
result, ret_sols = None, None
if direction < 0:
constraints = {r_id: (-max_uptake if r_id in valid_reactions else 0, model.reactions[r_id].ub)
for r_id in exchange_reactions}
else:
constraints = {r_id: (model.reactions[r_id].lb, max_uptake if r_id in valid_reactions else 0)
for r_id in exchange_reactions}
constraints[model.biomass_reaction] = (min_growth, None)
if n_solutions == 1:
solution = solver.solve(objective, minimize=True, constraints=constraints, get_values=exchange_reactions)
if solution.status != Status.OPTIMAL:
warn_wrapper('No solution found')
result, ret_sols = None, solution
else:
medium = get_medium(solution, exchange_reactions, direction, abstol)
if validate:
validate_solution(model, medium, exchange_reactions, direction, min_growth, max_uptake)
result, ret_sols = medium, solution
elif use_pool:
solutions = solver.solve(objective, minimize=True, constraints=constraints, get_values=exchange_reactions,
pool_size=n_solutions, pool_gap=pool_gap)
if solutions is None:
result, ret_sols = [], []
else:
media = [get_medium(solution, exchange_reactions, direction, abstol)
for solution in solutions]
result, ret_sols = media, solutions
else:
media = []
solutions = []
for i in range(0, n_solutions):
if i > 0:
constr_id = 'iteration_{}'.format(i)
previous_sol = {'y_' + r_id: 1 for r_id in medium}
solver.add_constraint(constr_id, previous_sol, '<', len(previous_sol) - 1)
solution = solver.solve(objective, minimize=True, constraints=constraints, get_values=exchange_reactions)
if solution.status != Status.OPTIMAL:
break
medium = get_medium(solution, exchange_reactions, direction, abstol)
media.append(medium)
solutions.append(solution)
result, ret_sols = media, solutions
if not persistent:
solver.clean_up()
return result, ret_sols