def test_generate_sbml_model():
# Test a non-reversible reaction
s1 = Species("S1")
s2 = Species("S2")
rx0 = Reaction.from_massaction(inputs=[s1], outputs=[s2], k_forward=0.1)
crn = ChemicalReactionNetwork(species=[s1, s2], reactions=[rx0])
# generate an sbml model
document, model = crn.generate_sbml_model()
# all species from the CRN are accounted for
assert len(model.getListOfSpecies()) == len(crn.species)
# all reactions from the CRN are accounted for
assert len(model.getListOfReactions()) == len(crn.reactions)
document2, model2 = crn.generate_sbml_model(check_validity=False)
assert len(model2.getListOfSpecies()) == len(
crn.species) #the same model is made if you dont check validity
assert len(model2.getListOfReactions()) == len(
crn.reactions) #the same model is made if you dont check validity
# reversible needs to be off!
# assert not model.getListOfReactions()[0].isSetReversible()
# test a reversible reaction
rx1 = Reaction.from_massaction(inputs=[s1],
outputs=[s2],
k_forward=0.1,
k_reverse=0.1)
rxn_list = [rx1]
crn = ChemicalReactionNetwork(species=[s1, s2], reactions=rxn_list)
# generate an sbml model
document, model = crn.generate_sbml_model()
# all species from the CRN are accounted for
assert len(model.getListOfSpecies()) == len(crn.species)
# all reactions from the CRN are accounted for
assert len(model.getListOfReactions()) == 2
# although sbml represents a reverisble reaction with reversible flag
# BioCRNpyler always creates two reactions, because this is correct
# for stochastic simulation with SBML.
sbml_rxn = model.getListOfReactions()
# assert not sbml_rxn[0].isSetReversible()
# assert not sbml_rxn[1].isSetReversible()
# Test propagation for for_bioscrape keyword
# generate an sbml model with for_bioscrape = True
document, model = crn.generate_sbml_model(for_bioscrape=True)
for r in model.getListOfReactions():
assert r.getAnnotation() is not None
assert validate_sbml(document) == 0
# generate an sbml model with for_bioscrape = False
document, model = crn.generate_sbml_model(for_bioscrape=False)
for r in model.getListOfReactions():
assert r.getAnnotation() is None
assert validate_sbml(document) == 0
def test_generate_sbml_model_parameter_names():
s1 = Species("S1")
s2 = Species("S2")
# The correct parameter formating should be: "name_partid_mechanism"
key0 = ParameterKey(mechanism="m", part_id="p", name="n")
k0 = ParameterEntry("n", 1.0, parameter_key=key0)
# Tests 3 Parameter entries with seemingly redundant keys.
# In SBML parameter id, None will be kept blank, resulting in
# different numbers of underscores between the V's
k1 = ParameterEntry("v", 1.0, parameter_key=None)
key2 = ParameterKey(mechanism=None, part_id="v", name="v")
k2 = ParameterEntry("v", 2.0, parameter_key=key2)
key3 = ParameterKey(mechanism="v", part_id=None, name="v")
k3 = ParameterEntry("v", 2.0, parameter_key=key3)
key4 = ParameterKey(mechanism="v", part_id="v", name="v")
k4 = ParameterEntry("v", 2.0, parameter_key=key4)
# Throw a duplicate key for good measure!
rx0 = Reaction.from_massaction(inputs=[],
outputs=[s1],
k_forward=k0,
k_reverse=k1)
rx1 = Reaction.from_massaction(inputs=[s1],
outputs=[s2],
k_forward=k1,
k_reverse=k2)
rx2 = Reaction.from_massaction(inputs=2 * [s1],
outputs=2 * [s2],
k_forward=k3,
k_reverse=k4)
rxn_list = [rx0, rx1, rx2]
crn = ChemicalReactionNetwork(species=[s1, s2], reactions=rxn_list)
document, model = crn.generate_sbml_model()
assert validate_sbml(document) == 0
correct_ids = set(["v_v_", "v__v", "v_v_v", "v__", "n_p_m"])
ids = set([p.getId() for p in model.getListOfParameters()])
assert ids == correct_ids
# Names of different supported propensities
for prop in Propensity.get_available_propensities():
print(prop)
kb = 100
ku = 10
kex = 1.
kd = .1
G = Species(name="G", material_type="dna") #DNA
A = Species(name="A", material_type="protein") #Activator
GA = ComplexSpecies([G, A, A]) #Activated Gene
X = Species(name="X", material_type="protein")
rxnd = Reaction.from_massaction(inputs=[X], outputs=[], k_forward=kd)
# Massaction Unregulated
species1 = [G, A, GA, X]
rxn0_1 = Reaction.from_massaction(inputs=[G, A, A],
outputs=[GA],
k_forward=kb,
k_reverse=ku)
rxn0_2 = Reaction.from_massaction(inputs=[GA], outputs=[GA, X], k_forward=kex)
CRN0 = ChemicalReactionNetwork(species1, [rxn0_1, rxn0_2, rxnd])
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)