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precursor.py
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precursor.py
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#!python
# Copyright (c) 2012, Sven Thiele <sthiele78@gmail.com>
#
# This file is part of precursor.
#
# precursor is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# precursor is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with precursor. If not, see <http://www.gnu.org/licenses/>.
# -*- coding: utf-8 -*-
#from pyasp.asp import *
from pyasp.term import *;
import argparse
from __precursor__ import query, utils, sbml
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("net",
help="metabolic network in SBML format")
parser.add_argument("inputs",
help="targets in XML format")
#help="target, seed and ubiquitous metabolites in XML format")
parser.add_argument('--autoseeds',
help="compute possible seed metabolites",
action="store_true")
args = parser.parse_args()
netfile = args.net
targetsfile = args.inputs
print('Reading network from ',netfile,'...',end='')
net = sbml.readSBMLnetwork(netfile)
print('done.')
print('Reading inputs from ',targetsfile,'...',end='')
targets, pseeds, misc = sbml.readPITUFU(targetsfile)
print('done.')
print(" ", len(targets), "target metabolites.")
print(" ", len(pseeds), "possible seed metabolites.")
net = TermSet(net.union(misc))
#print misc
if args.autoseeds :
print('\nAutocompute possible seeds ...',end='')
autoseeds = query.get_automatic_pseeds(net, targets)
print('done.')
print(' ', len(autoseeds), 'possible seeds found.')
pseeds = TermSet(pseeds.union(autoseeds))
print('\nTesting targets for producebility ...',end='')
filtered_targets = TermSet()
for t in targets :
singlet = String2TermSet(str(t))
SAT = query.satcheck(net, pseeds, singlet)
if not SAT :
filtered_targets.add(t)
print('done.')
if len(filtered_targets) > 0:
print(' ',len(targets),' targets cannot be produced:')
for t in filtered_targets : print(' ',t.arg(0))
print('cannot be produced.')
targets = TermSet(targets.difference(filtered_targets))
if len(targets) > 0:
print(' ',len(targets),' targets can be produced:')
for t in targets : print(' ',t.arg(0))
targets = TermSet(targets.difference(filtered_targets))
else:
print(' No Target can be produced.')
utils.clean_up()
quit()
print('\nCompute cardinality minimum for a precursor sets ...',end='')
solution = query.get_card_min_precursor_set(net, pseeds, targets)
print('done.')
opt = solution.score[0]
print(' Minimal size of a precursor set is',str(opt-1)+'.')
print('\nCompute all cadinality minimal precursor sets...',end='')
precursors = query.get_card_minimal_precursor_sets(net, pseeds, targets)
print('done.')
count=1
for min_set in precursors :
print(count,end=': ')
utils.print_seeds(min_set)
count+=1
print('\nCompute subset minimal precursor sets ...',end='')
precursors = query.get_subset_min_precursor_sets(net, pseeds, targets)
print('done.')
print(len(precursors),' subset minimal precursor sets found.')
count=1
for min_set in precursors :
print(count,end=': ')
utils.print_seeds(min_set)
count+=1
utils.clean_up()