def test_add_reaction_for_bioscrape():
"""
Generates models for bioscrape including the particular annotations needed.
"""
S1, S2, S3 = Species("S1"), Species("S2"), Species("S3")
species = [S1, S2, S3]
for_bioscrape = True #Toggle for_bioscrape
propensities = [
MassAction(k_forward=1, k_reverse=.1),
HillPositive(k=1, n=2., K=3., s1=S1),
HillNegative(k=1, n=2., K=3., s1=S1),
ProportionalHillPositive(k=1, n=2, K=3., s1=S1, d=S2),
ProportionalHillNegative(k=1, n=2, K=3., s1=S1, d=S2)
]
for prop in propensities:
rxn_num = 0
model_id = f"{prop.name}_model_with_for_bioscrape_{for_bioscrape}"
document, model = create_sbml_model(model_id=model_id)
add_all_species(model, species)
rxn = Reaction([S1], [S2, S3],
propensity_type=prop) #create a reaction
try:
sbml_reaction = add_reaction(
model, rxn, f"r{rxn_num}",
for_bioscrape=for_bioscrape) #add reaction to SBML Model
rxn_num += 1 #increment reaction id
crn_reactants = [str(S1)]
crn_products = [str(S2), str(S3)]
sbml_products = [
p.getSpecies() for p in sbml_reaction.getListOfProducts()
]
sbml_reactants = [
p.getSpecies() for p in sbml_reaction.getListOfReactants()
]
#test that the reaction has the right inputs and outputs
assert set(crn_reactants) == set(sbml_reactants)
assert set(crn_products) == set(sbml_products)
if len(crn_reactants) > 0:
assert all(
crn_reactants.count(s.getSpecies()) == int(
s.getStoichiometry())
for s in sbml_reaction.getListOfReactants())
if len(crn_products) > 0:
assert all(
crn_products.count(s.getSpecies()) == int(
s.getStoichiometry())
for s in sbml_reaction.getListOfProducts())
assert not sbml_reaction.getReversible()
# Check annotations:
sbml_annotation = sbml_reaction.getAnnotationString()
check_var = f"type={prop.name}" in str(sbml_annotation)
assert check_var == True
#Test that the sbml annotation has keys for all species and parameters
for k in prop.propensity_dict["parameters"]:
#convert k_reverse and k_forward to just k
k = k.replace("_reverse", "").replace("_forward", "")
check_var = f"{k}=" in sbml_annotation
assert check_var == True
#be sure that "k=" only shows up once in the annotation
assert sbml_annotation.count(f"{k}=") == 1
for s in prop.propensity_dict["species"]:
check_var = f"{s}=" in sbml_annotation
assert check_var == True
#TODO is there a smart way to test that the rate formula is correct?
assert validate_sbml(document) == 0 # Validate the SBML model
except Exception as e: #collect errors
error_txt = f"Unexpected Error: in sbmlutil.add_reaction {rxn} for {model_id}. \n {str(e)}."
raise Exception(error_txt)
def test_add_reaction():
"""
Test adding reaction to SBML for combinatorially created list of models
"""
#create species
S1, S2, S3 = Species("S1"), Species("S2"), Species("S3")
species = [S1, S2, S3]
#create some parameters
key1 = ParameterKey(name="k", mechanism="m", part_id="pid")
k1 = ParameterEntry("k1", 1.11, key1)
k2 = ParameterEntry("k2", 2.22)
stochiometries = [([], [S1]), ([S1], []), ([S1], [S2]),
(2 * [S1], [S2, S3])]
propensities = [
MassAction(k_forward=1.),
MassAction(k_forward=1, k_reverse=.1),
MassAction(k_forward=k1),
MassAction(k_forward=k1, k_reverse=k2),
HillPositive(k=1, n=2., K=3., s1=S1),
HillPositive(k=k1, n=2., K=k2, s1=S1),
HillNegative(k=1, n=2., K=3., s1=S1),
HillNegative(k=k1, n=k2, K=3., s1=S1),
ProportionalHillPositive(k=1, n=2, K=3., s1=S1, d=S2),
ProportionalHillPositive(k=k1, n=2, K=k2, s1=S1, d=S2),
ProportionalHillNegative(k=1, n=2, K=3., s1=S1, d=S2),
ProportionalHillNegative(k=k1, n=2, K=k2, s1=S1, d=S2),
GeneralPropensity('k1*2 - k2/S1^2',
propensity_species=[S1],
propensity_parameters=[k1, k2]),
GeneralPropensity('S1^2 + S2^2 + S3^2',
propensity_species=[S1, S2, S3],
propensity_parameters=[])
]
for prop in propensities: #Cycle through different propensity types
for inputs, outputs in stochiometries: #cycle through different stochiometries
for stochastic in [True, False]: #Toggle Stochastic
rxn_num = 0
model_id = f"{prop.name}_model_with_stochastic_{stochastic}"
document, model = create_sbml_model(model_id=model_id)
add_all_species(model, species)
rxn = Reaction(inputs, outputs,
propensity_type=prop) #create a reaction
try:
sbml_reaction = add_reaction(
model, rxn, f"r{rxn_num}",
stochastic=stochastic) #add reaction to SBML Model
rxn_num += 1 #increment reaction id
crn_reactants = [str(s) for s in inputs]
crn_products = [str(s) for s in outputs]
sbml_products = [
p.getSpecies()
for p in sbml_reaction.getListOfProducts()
]
sbml_reactants = [
p.getSpecies()
for p in sbml_reaction.getListOfReactants()
]
#test that the reaction has the right inputs and outputs
assert set(crn_reactants) == set(sbml_reactants)
assert set(crn_products) == set(sbml_products)
if len(crn_reactants) > 0:
assert all(
crn_reactants.count(s.getSpecies()) == int(
s.getStoichiometry())
for s in sbml_reaction.getListOfReactants())
if len(crn_products) > 0:
assert all(
crn_products.count(s.getSpecies()) == int(
s.getStoichiometry())
for s in sbml_reaction.getListOfProducts())
assert not sbml_reaction.getReversible()
#TODO is there a smart way to test that the rate formula is correct?
assert validate_sbml(
document) == 0 # Validate the SBML model
except Exception as e: #collect errors to display
error_txt = f"Unexpected Error: in sbmlutil.add_reaction {rxn} for {model_id}. \n {str(e)}."
raise Exception(error_txt)
mak1 = MassAction(k_forward=kb, k_reverse=ku)
mak2 = MassAction(k_forward=kex)
rxn1_1 = Reaction([G, A, A], [GA], propensity_type=mak1)
rxn1_2 = Reaction([G], [G, X], propensity_type=mak2)
CRN1 = ChemicalReactionNetwork(species1, [rxn1_1, rxn1_2, rxnd])
# Hill positive
species2 = [G, A, X]
hill_pos = HillPositive(k=kex, s1=A, K=float(kb / ku), n=2)
rxn2_1 = Reaction([G], [G, X], propensity_type=hill_pos)
CRN2 = ChemicalReactionNetwork(species2, [rxn2_1, rxnd])
# proportional Hill positive
prop_hill_pos = ProportionalHillPositive(k=kex,
s1=A,
K=float(kb / ku),
n=2,
d=G)
rxn3_1 = Reaction([G], [G, X], propensity_type=prop_hill_pos)
CRN3 = ChemicalReactionNetwork(species2, [rxn3_1, rxnd])
# Hill Negative
hill_negative = HillNegative(k=kex, s1=A, K=float(kb / ku), n=2)
rxn4_1 = Reaction([G], [G, X], propensity_type=hill_negative)
CRN4 = ChemicalReactionNetwork(species2, [rxn4_1, rxnd])
# proportional hill negative
prop_hill_neg = ProportionalHillNegative(k=kex,
s1=A,
K=float(kb / ku),
n=2,