def setUp(self):
self._database = DictDatabase()
self._database.set_reaction('rxn_1', parse_reaction('A[e] => B[e]'))
self._database.set_reaction('rxn_2', parse_reaction('B[e] => C[e]'))
self._database.set_reaction('rxn_3', parse_reaction('B[e] <=> D[e]'))
self._database.set_reaction('rxn_4', parse_reaction('C[e] <=> E[e]'))
self._database.set_reaction('rxn_5', parse_reaction('D[e] => E[e]'))
self._database.set_reaction('rxn_6', parse_reaction('E[e] =>'))
self._database.set_reaction('ex_A', parse_reaction('A[e] <=>'))
self._mm = MetabolicModel.load_model(
self._database, self._database.reactions)
self._assoc = {
'rxn_1': str('gene_1'),
'rxn_2': str('gene_2'),
'rxn_3': str('gene_5'),
'rxn_4': str('gene_3 or gene_4'),
'rxn_5': str('gene_5 and gene_6')
}
self._obj_reaction = 'rxn_6'
try:
self._solver = generic.Solver()
except generic.RequirementsError:
self.skipTest('Unable to find an LP solver for tests')
try:
self.solver = generic.Solver(integer=True)
except generic.RequirementsError:
self.skipTest('Needs and integer programming solver')
def setUp(self):
self._database = DictDatabase()
self._database.set_reaction('rxn_1', parse_reaction('A[e] => B[e]'))
self._database.set_reaction('rxn_2', parse_reaction('B[e] => C[e]'))
self._database.set_reaction('rxn_3', parse_reaction('B[e] => D[e]'))
self._database.set_reaction('rxn_4', parse_reaction('C[e] => E[e]'))
self._database.set_reaction('rxn_5', parse_reaction('D[e] => E[e]'))
self._database.set_reaction('rxn_6', parse_reaction('E[e] =>'))
self._database.set_reaction('ex_A', parse_reaction('A[e] <=>'))
self._mm = MetabolicModel.load_model(self._database,
self._database.reactions)
self._assoc = {
'rxn_1': boolean.Expression('gene_1'),
'rxn_2': boolean.Expression('gene_2 or gene_3'),
'rxn_5': boolean.Expression('gene_3 and gene_4')
}
self._obj_reaction = 'rxn_6'
try:
self._solver = generic.Solver()
except generic.RequirementsError:
self.skipTest('Unable to find an LP solver for tests')
self._prob = fluxanalysis.FluxBalanceProblem(self._mm, self._solver)
def is_solver_available(self, **kwargs):
try:
generic.Solver(**kwargs)
except generic.RequirementsError:
return False
return True
def test_run_fastgapfill(self):
# Skip test if solver is GLPK because of issue #61.
try:
solver = generic.Solver()
if solver.properties['name'] == 'glpk':
self.skipTest('Test has known issue with GLPK')
except generic.RequirementsError:
pass
self.run_solver_command(FastGapFillCommand)
def setUp(self):
self.database = DictDatabase()
self.database.set_reaction('rxn_1', parse_reaction('|A| + |B| => |C|'))
self.database.set_reaction('rxn_2',
parse_reaction('|C| + |Z| => |A| + |B|'))
self.model = MetabolicModel.load_model(self.database,
self.database.reactions)
try:
self.solver = generic.Solver()
except generic.RequirementsError:
self.skipTest('Unable to find an LP solver for tests')
def setUp(self):
# TODO use mock model instead of actual model
self.database = DictDatabase()
self.database.set_reaction('rxn_1', parse_reaction('=> |A|'))
self.database.set_reaction('rxn_2',
parse_reaction('(0.000001) |A| =>'))
self.model = MetabolicModel.load_model(self.database,
self.database.reactions)
try:
self.solver = generic.Solver()
except generic.RequirementsError:
self.skipTest('Unable to find an LP solver for tests')
def test_gimme_model(self):
f = ['gene\texpression', 'gene_1\t15', 'gene_2\t20',
'gene_3\t15', 'gene_4\t25', 'gene_5\t10', 'gene_6\t25']
threshold_dict = gimme.parse_transcriptome_file(f, 20)
mm_irreversible, reversible_gene_assoc, split_rxns = \
gimme.make_irreversible(self._mm, self._assoc,
exclude_list=['ex_A', 'rxn_6'])
p = fluxanalysis.FluxBalanceProblem(mm_irreversible, generic.Solver())
final_model, used_exchange, below_threshold_ids, incon_score = \
gimme.solve_gimme_problem(p, mm_irreversible, 'rxn_6',
reversible_gene_assoc, split_rxns, threshold_dict, MaybeRelative(20))
self.assertEqual(incon_score, 100)
self.assertEqual(final_model, set(['rxn_1', 'rxn_2', 'rxn_4']))
self.assertEqual(below_threshold_ids, set(['rxn_1', 'rxn_3', 'rxn_5']))
def setUp(self):
self.database = DictDatabase()
self.database.set_reaction('rxn_1', parse_reaction('=> |A|'))
self.database.set_reaction('rxn_2', parse_reaction('|A| <=> |B|'))
self.database.set_reaction('rxn_3', parse_reaction('|A| => |C|'))
self.database.set_reaction('rxn_4', parse_reaction('|C| => |D|'))
self.database.set_reaction('rxn_5', parse_reaction('|D| <=> |E|'))
self.model = MetabolicModel.load_model(
self.database, self.database.reactions)
try:
self.solver = generic.Solver(integer=True)
except generic.RequirementsError:
self.skipTest('Unable to find an MILP solver for tests')
def setUp(self):
self.database = DictDatabase()
self.database.set_reaction('rxn_1', parse_reaction('=> |A|'))
self.database.set_reaction('rxn_2',
parse_reaction('(0.000001) |A| =>'))
self.model = MetabolicModel.load_model(
self.database, self.database.reactions)
try:
self.solver = generic.Solver()
except generic.RequirementsError:
self.skipTest('Unable to find an LP solver for tests')
# Skip these tests with GLPK because of issue #61.
if self.solver.properties['name'] == 'glpk':
self.skipTest('Test has known issue with GLPK')
def setUp(self):
self.database = DictDatabase()
self.database.set_reaction('rxn_1', parse_reaction('=> (2) |A|'))
self.database.set_reaction('rxn_2', parse_reaction('|A| <=> |B|'))
self.database.set_reaction('rxn_3', parse_reaction('|A| => |D|'))
self.database.set_reaction('rxn_4', parse_reaction('|A| => |C|'))
self.database.set_reaction('rxn_5', parse_reaction('|C| => |D|'))
self.database.set_reaction('rxn_6', parse_reaction('|D| =>'))
self.database.set_reaction('rxn_7', parse_reaction('|E| => |F|'))
self.database.set_reaction('rxn_8', parse_reaction('|F| => |E|'))
self.model = MetabolicModel.load_model(self.database,
self.database.reactions)
self.model.limits['rxn_5'].upper = 100
try:
self.solver = generic.Solver()
except generic.RequirementsError:
self.skipTest('Unable to find an LP solver for tests')
def setUp(self):
self.database = DictDatabase()
self.database.set_reaction('ex_A', parse_reaction('|A| <=>'))
self.database.set_reaction('ex_D', parse_reaction('|D| <=>'))
self.database.set_reaction('rxn_1', parse_reaction('|A| => |B|'))
self.database.set_reaction('rxn_2', parse_reaction('|B| <=> |C|'))
self.database.set_reaction('rxn_3', parse_reaction('|C| <=> |D|'))
self.database.set_reaction('rxn_4', parse_reaction('|D| <=> |E|'))
self.database.set_reaction('rxn_5', parse_reaction('|E| => |B|'))
self.model = MetabolicModel.load_model(self.database,
self.database.reactions)
self.model.limits['ex_A'].lower = -10 # Low uptake
self.model.limits['ex_D'].lower = 0 # No uptake
try:
self.solver = generic.Solver(integer=True)
except generic.RequirementsError:
self.skipTest('Unable to find an MILP solver for tests')
def setUp(self):
self.database = DictDatabase()
self.database.set_reaction('rxn_1', parse_reaction('|A| => |B|'))
self.database.set_reaction('rxn_2', parse_reaction('|B| => |G|'))
self.database.set_reaction('rxn_3', parse_reaction('|A| => |G|'))
self.database.set_reaction('rxn_4', parse_reaction('|B| => |H|'))
self.database.set_reaction('rxn_5', parse_reaction('|B| => |C| + |F|'))
self.database.set_reaction('rxn_6', parse_reaction('|C| => |D|'))
self.database.set_reaction('rxn_7', parse_reaction('|E| <=> |D|'))
self.database.set_reaction('rxn_9', parse_reaction('|I| => |J|'))
self.database.set_reaction('rxn_10', parse_reaction('|J| => |K|'))
self.database.set_reaction('rxn_A', parse_reaction('=> |A|'))
self.database.set_reaction('rxn_G', parse_reaction('|G| =>'))
self.database.set_reaction('rxn_E', parse_reaction('|E| =>'))
self.database.set_reaction(
'rxn_bio', parse_reaction('|D| + (2.5) |F| =>'))
self.model = MetabolicModel.load_model(
self.database, self.database.reactions)
try:
self.solver = generic.Solver()
except generic.RequirementsError:
self.skipTest('Unable to find an LP solver for tests')
# Notes for me
# Removing the second glucose exp for anaerobe. It yields a biomass of 0 and I am
# not sure why.
from psamm.lpsolver import generic
solver = generic.Solver()
import sys
sys.path.insert(0, '.')
import moma
import sys
import moma_test
import csv
import math
# Run the moma tests with a certain cutoff for viable/not viable
def run_tests(algorithm):
gene_dict = {}
# The medium that we are limiting in each of the experiments
medium_ids = ['R_EX_glc_e_', 'R_EX_gal_e_', 'R_EX_glyc_e_', 'R_EX_etoh_e_']
aerobe = True # Our first glucose exp will be aerobe
for exp in medium_ids:
print("Removing: " + exp)
# We are working on a glucose exp
if exp == 'R_EX_glc_e_':
if aerobe == True:
mm.limits['R_EX_o2_e_'].lower = -2.0
mm.limits['R_EX_co2_e_'].lower = 0.0
def setUp(self):
try:
self.solver = generic.Solver()
except generic.RequirementsError:
self.skipTest('Unable to find an LP solver for tests')