import six
import argparse
import time
from symengine import Add, sympify
from numpy import abs
from warnings import catch_warnings, filterwarnings, resetwarnings
from cobra.exceptions import OptimizationError
from dexom_python.model_functions import read_model, check_model_options, load_reaction_weights, DEFAULT_VALUES, check_threshold_tolerance
from dexom_python.result_functions import write_solution
[docs]class ImatException(Exception):
pass
[docs]def create_full_variable_single(model, rid, reaction_weights, epsilon, threshold):
# the x_rid variables represent a binary condition of flux activation
if 'x_' + rid not in model.solver.variables:
rxn = model.reactions.get_by_id(rid)
xtot = model.solver.interface.Variable('x_%s' % rid, type='binary')
xf = model.solver.interface.Variable('xf_%s' % rid, type='binary')
xr = model.solver.interface.Variable('xr_%s' % rid, type='binary')
model.solver.add(xtot)
model.solver.add(xf)
model.solver.add(xr)
xtot_def = model.solver.interface.Constraint(xtot - xf - xr, lb=0., ub=0., name='x_%s_def' % rid)
xf_upper = model.solver.interface.Constraint(
rxn.forward_variable - rxn.upper_bound * xf, ub=0., name='xr_%s_upper' % rid)
xr_upper = model.solver.interface.Constraint(
rxn.reverse_variable + rxn.lower_bound * xr, ub=0., name='xf_%s_upper' % rid)
temp = threshold
if rid in reaction_weights:
if reaction_weights[rid] > 0.:
temp = epsilon
xf_lower = model.solver.interface.Constraint(
rxn.forward_variable - temp * xf, lb=0., name='xf_%s_lower' % rid)
xr_lower = model.solver.interface.Constraint(
rxn.reverse_variable - temp * xr, lb=0., name='xr_%s_lower' % rid)
model.solver.add(xtot_def)
model.solver.add(xf_upper)
model.solver.add(xr_upper)
model.solver.add(xf_lower)
model.solver.add(xr_lower)
return model
[docs]def create_new_partial_variable_single(model, rid, epsilon, threshold, pos):
# the variable definition is more precise than in the original iMAT implementation
# this is done in order to avoid problems with enumeration methods, and doesn't affect the results of iMAT
# tol = model.tolerance*1000
if pos and 'rh_' + rid + '_pos' not in model.solver.variables:
rxn = model.reactions.get_by_id(rid)
xtot = model.solver.interface.Variable('x_%s' % rid, type='binary')
xf = model.solver.interface.Variable('rh_%s_pos' % rid, type='binary')
xr = model.solver.interface.Variable('rh_%s_neg' % rid, type='binary')
model.solver.add(xtot)
model.solver.add(xf)
model.solver.add(xr)
xtot_def = model.solver.interface.Constraint(xtot - xf - xr, lb=0., ub=0., name='x_%s_def' % rid)
model.solver.add(xtot_def)
up = model.solver.interface.Constraint(rxn.forward_variable - rxn.reverse_variable
- xf * epsilon - xr * rxn.lower_bound, lb=0., name='test%s_lower' % rid)
lo = model.solver.interface.Constraint(rxn.forward_variable - rxn.reverse_variable
+ xr * epsilon - xf * rxn.upper_bound, ub=0., name='test%s_upper' % rid)
model.solver.add(up)
model.solver.add(lo)
elif not pos and 'rl_' + rid not in model.solver.variables:
rxn = model.reactions.get_by_id(rid)
xtot = model.solver.interface.Variable('rl_%s' % rid, type='binary')
xf = model.solver.interface.Variable('xf_%s' % rid, type='binary')
xr = model.solver.interface.Variable('xr_%s' % rid, type='binary')
model.solver.add(xtot)
model.solver.add(xf)
model.solver.add(xr)
xtot_def = model.solver.interface.Constraint(xtot - xf - xr, lb=0., ub=0., name='x_%s_def' % rid)
model.solver.add(xtot_def)
up = model.solver.interface.Constraint(rxn.forward_variable - rxn.reverse_variable
- xf * threshold - xr * rxn.lower_bound, lb=0., name='test%s_lower' % rid)
lo = model.solver.interface.Constraint(rxn.forward_variable - rxn.reverse_variable
+ xr * threshold - xf * rxn.upper_bound, ub=0., name='test%s_upper' % rid)
model.solver.add(up)
model.solver.add(lo)
return model
[docs]def create_full_variables(model, reaction_weights, epsilon, threshold):
"""
Creates binary indicator variables in the model for every reaction.
"""
for rxn in model.reactions:
model = create_full_variable_single(model=model, rid=rxn.id, reaction_weights=reaction_weights,
epsilon=epsilon, threshold=threshold)
return model
[docs]def create_new_partial_variables(model, reaction_weights, epsilon, threshold):
"""
Creates binary indicator variables in the model for reactions with nonzero weight.
"""
for rxn in model.reactions:
if rxn.id in reaction_weights.keys():
weight = reaction_weights[rxn.id]
if weight > 0:
model = create_new_partial_variable_single(model=model, rid=rxn.id, epsilon=epsilon,
threshold=threshold, pos=True)
elif weight < 0: # the rl_rid variables represent the lowly expressed reactions
model = create_new_partial_variable_single(model=model, rid=rxn.id, epsilon=epsilon,
threshold=threshold, pos=False)
return model
[docs]def imat(model, reaction_weights=None, epsilon=DEFAULT_VALUES['epsilon'], threshold=DEFAULT_VALUES['threshold'],
full=False):
"""
Modified version of the integrative Metabolic Analysis Tool with reaction weights
Parameters
----------
model: cobra.Model
a cobrapy model
reaction_weights: dict
keys are reaction IDs, values are weights
epsilon: float
activation threshold for highly expressed reactions
threshold: float
activation threshold for all reactions
full: bool
if True, create variables for all reactions. if False, only for reactions with non-zero weights
Returns
-------
solution: cobra.Solution
"""
check_threshold_tolerance(model=model, epsilon=epsilon, threshold=threshold)
if reaction_weights is None:
reaction_weights = {}
y_variables = list()
x_variables = list()
y_weights = list()
x_weights = list()
t0 = time.perf_counter()
try:
if full: # for the full_imat implementation
model = create_full_variables(model, reaction_weights, epsilon, threshold)
for rid, weight in six.iteritems(reaction_weights):
if weight > 0 and rid in model.reactions:
y_pos = model.solver.variables['xf_' + rid]
y_neg = model.solver.variables['xr_' + rid]
y_variables.append([y_neg, y_pos])
y_weights.append(weight)
elif weight < 0 and rid in model.reactions:
x = sympify('1') - model.solver.variables['x_' + rid]
x_variables.append(x)
x_weights.append(abs(weight))
else:
model = create_new_partial_variables(model, reaction_weights, epsilon, threshold)
for rid, weight in six.iteritems(reaction_weights):
if weight > 0 and rid in model.reactions:
y_neg = model.solver.variables['rh_' + rid + '_neg']
y_pos = model.solver.variables['rh_' + rid + '_pos']
y_variables.append([y_neg, y_pos])
y_weights.append(weight)
elif weight < 0 and rid in model.reactions:
x = sympify('1') - model.solver.variables['rl_' + rid]
x_variables.append(x)
x_weights.append(abs(weight))
rh_objective = [(y[0] + y[1]) * y_weights[idx] for idx, y in enumerate(y_variables)]
rl_objective = [x * x_weights[idx] for idx, x in enumerate(x_variables)]
objective = model.solver.interface.Objective(Add(*rh_objective) + Add(*rl_objective), direction='max')
model.objective = objective
t1 = time.perf_counter()
print('%.2fs before optimize call' % (t1 - t0))
with catch_warnings():
filterwarnings('error')
try:
with model:
solution = model.optimize()
t2 = time.perf_counter()
print('%.2fs during optimize call' % (t2-t1))
return solution
except UserWarning as w:
resetwarnings()
if 'time_limit' in str(w):
print('The solver has reached the timelimit. This can happen if there are too many constraints on '
'the model, or if some of the following parameters have too low values: epsilon, threshold, '
'feasibility tolerance, MIP gap tolerance.')
# warn('Solver status is "time_limit"')
raise ImatException('Solver status is "time_limit", timelimit error')
elif 'infeasible' in str(w):
print('The solver has encountered an infeasible optimization. This can happen if there are too '
'many constraints on the model, or if some of the following parameters have too low values: '
'epsilon, threshold, feasibility tolerance, MIP gap tolerance.')
# warn('Solver status is "infeasible"')
raise ImatException('Solver status is "infeasible", feasibility error')
else:
print('An unexpected error has occured during the solver call')
# warn(w)
raise ImatException(str(w))
except OptimizationError as e:
resetwarnings()
print('An unexpected error has occured during the solver call.')
raise ImatException(str(e))
finally:
pass
[docs]def main():
"""
This function is called when you run this script from the commandline.
It performs the modified iMAT algorithm with reaction weights.
Use --help to see commandline parameters
"""
description = 'Performs the modified iMAT algorithm with reaction weights'
parser = argparse.ArgumentParser(description=description, formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('-m', '--model', help='Metabolic model in sbml, json, or matlab format')
parser.add_argument('-r', '--reaction_weights', default={},
help='Reaction weights in csv format with column names: (reactions, weights)')
parser.add_argument('-e', '--epsilon', type=float, default=DEFAULT_VALUES['epsilon'],
help='Activation threshold for highly expressed reactions')
parser.add_argument('--threshold', type=float, default=DEFAULT_VALUES['threshold'],
help='Activation threshold for all reactions')
parser.add_argument('-t', '--timelimit', type=int, default=DEFAULT_VALUES['timelimit'], help='Solver time limit')
parser.add_argument('--tol', type=float, default=DEFAULT_VALUES['tolerance'], help='Solver feasibility tolerance')
parser.add_argument('--mipgap', type=float, default=DEFAULT_VALUES['mipgap'], help='Solver MIP gap tolerance')
parser.add_argument('-o', '--output', default='imat_solution', help='Path of the output file, without format')
args = parser.parse_args()
model = read_model(args.model)
check_model_options(model, timelimit=args.timelimit, feasibility=args.tol, mipgaptol=args.mipgap)
reaction_weights = {}
if args.reaction_weights:
reaction_weights = load_reaction_weights(args.reaction_weights)
solution = imat(model, reaction_weights, epsilon=args.epsilon, threshold=args.threshold)
write_solution(model, solution, args.threshold, args.output+'.csv')
return True
if __name__ == '__main__':
main()