def test_growth_transcription(self):
# Construct model
env = EnvironmentVarGuard()
env.set('CONFIG__DOT__wc_kb__DOT__io__DOT__strict', '0')
with env:
self.kb = wc_kb.io.Reader().run(
'tests/fixtures/min_model_kb.xlsx',
'tests/fixtures/min_model_kb_seq.fna',
)[wc_kb.KnowledgeBase][0]
self.model = prokaryote.ProkaryoteModelGenerator(
knowledge_base=self.kb,
component_generators=[
prokaryote.InitalizeModel,
prokaryote.TranscriptionSubmodelGenerator,
prokaryote.RnaDegradationSubmodelGenerator,
prokaryote.MetabolismSubmodelGenerator
],
options={
'component': {
'TranscriptionSubmodelGenerator': {
'beta': 1.
},
'RnaDegradationSubmodelGenerator': {
'beta': 1.
}
}
}).run()
# Simulate model
checkpoint_period = 10
end_time = 100
simulation = Simulation(self.model)
results = simulation.run(end_time, self.dir, checkpoint_period)
self.assertIsInstance(results, tuple)
# Check results
num_events = results[0]
run_results_dir = results[1]
run_results = RunResults(run_results_dir)
df = run_results.get('populations')
cytosol = self.model.compartments.get_one(id='c')
rna_ids = []
for rna in self.model.species_types.get(
type=wc_ontology['WC:RNA']): # RNA
rna_ids.append(rna.species.get_one(compartment=cytosol).id)
avg_init_rna_cn = np.mean(df.loc[0.0, rna_ids].values)
avg_final_rna_cn = np.mean(df.loc[100.0, rna_ids].values)
print(simulation.provide_event_counts())
print('\n INIT AVG RNA COPY NUMBERS:', avg_init_rna_cn)
print('\n FINAL AVG RNA COPY NUMBERS:', avg_final_rna_cn)
self.assertGreater(avg_final_rna_cn,
2 * avg_init_rna_cn - avg_init_rna_cn * 0.15)
self.assertLess(avg_final_rna_cn,
2 * avg_init_rna_cn + avg_init_rna_cn * 0.15)
def setUpClass(cls):
env = EnvironmentVarGuard()
env.set('CONFIG__DOT__wc_kb__DOT__io__DOT__strict', '0')
with env:
cls.kb = wc_kb.io.Reader().run('tests/fixtures/min_model_kb.xlsx',
'tests/fixtures/min_model_seq.fna',
)[wc_kb.KnowledgeBase][0]
cls.model = prokaryote.ProkaryoteModelGenerator(knowledge_base = cls.kb,
component_generators=[prokaryote.InitalizeModel]).run()
def test_generate_read_write(self):
env = EnvironmentVarGuard()
env.set('CONFIG__DOT__wc_kb__DOT__io__DOT__strict', '0')
with env:
kb = wc_kb.io.Reader().run(
'tests/fixtures/min_model_kb.xlsx',
'tests/fixtures/min_model_seq.fna',
)[wc_kb.KnowledgeBase][0]
""" Generate and write models from KBs """
model = prokaryote.ProkaryoteModelGenerator(knowledge_base=kb).run()
wc_lang.io.Writer().run(os.path.join(self.dir, 'model.xlsx'),
model,
data_repo_metadata=False)
""" Read back KBs from disk """
model_read = wc_lang.io.Reader().run(
os.path.join(self.dir, 'model.xlsx'))[wc_lang.Model][0]
self.assertTrue(model.is_equal(model_read, tol=1e-4))
def test_submodels(self):
env = EnvironmentVarGuard()
env.set('CONFIG__DOT__wc_kb__DOT__io__DOT__strict', '0')
with env:
cls.kb = wc_kb.io.Reader().run(
'tests/fixtures/min_model_kb.xlsx',
'tests/fixtures/min_model_seq.fna',
)[wc_kb.KnowledgeBase][0]
cls.model = prokaryote.ProkaryoteModelGenerator(
knowledge_base=cls.kb).run()
cytosol = self.model.compartments.get_one(id='c')
extracellular_space = self.model.compartments.get_one(id='e')
mean_doubling_time = self.model.parameters.get_one(
id='mean_doubling_time')
errors = obj_tables.Validator().run(self.model, get_related=True)
self.assertEqual(errors,
None,
msg=wc_utils.util.string.indent_forest(errors))
self.assertEqual(5, len(self.model.submodels))