def test_pfba_batch_iml1515_baseline(self):
fba_model = FBAModel(model_code='ecoli:iML1515')
solution = fba_model.solve(
alg='pfba',
conditions=pd.read_csv(
'../mscproject/simulation/data/perturbations.csv'))
solution.to_csv('baseline/pfba_batch_iml1515.csv')
def test_fba_batch(self):
fba_model = FBAModel()
solution = fba_model.solve(conditions=pd.read_csv(
'../mscproject/simulation/data/perturbations.csv'))
baseline = pd.read_csv('baseline/fba_batch.csv', index_col=0)
solution.columns = baseline.columns
self.assertTrue(solution.equals(baseline))
def test_pfba_batch_baseline(self):
fba_model = FBAModel()
solution = fba_model.solve(
alg='pfba',
conditions=pd.read_csv(
'../mscproject/simulation/data/perturbations.csv'))
solution.to_csv('baseline/pfba_batch.csv')
def test_two_rounds_solve(self):
fba_model = FBAModel()
fba_model.model.reactions.get_by_id(
'BIOMASS_Ec_iJO1366_core_53p95M').bounds = 1, 1
fba_model.solve(
alg='pfba', conditions=pd.read_csv(
'perturbations_small.csv')).to_csv('output/two_round.csv')
def test_pfba_batch_iml1515(self):
fba_model = FBAModel(model_code='ecoli:iML1515')
solution = fba_model.solve(
alg='pfba',
conditions=pd.read_csv(
'../mscproject/simulation/data/perturbations.csv'))
baseline = pd.read_csv('baseline/pfba_batch_iml1515.csv', index_col=0)
solution.columns = baseline.columns
self.assertTrue(solution.equals(baseline))
def run(model_code='ecoli:iJO1366'):
model = FBAModel(model_code).model
conditions = pd.read_csv('perturbations.csv')
df = pd.DataFrame(columns=('met_id', 'e_met_id', 'p_met_id', 'c_met_id'))
for _, condition in conditions.iterrows():
condition = condition.dropna()
for met_id in condition:
row = [met_id]
e_met_id = met_id + '_e'
if model.metabolites.has_id(e_met_id):
row.append(e_met_id)
else:
row.append('-')
p_met_id = met_id + '_p'
if model.metabolites.has_id(p_met_id):
row.append(p_met_id)
else:
row.append('-')
c_met_id = met_id + '_c'
if model.metabolites.has_id(c_met_id):
row.append(c_met_id)
else:
row.append('-')
df.loc[len(df), :] = row
df = df.drop_duplicates()
model_code = model_code.replace(':', '_')
df.to_csv(f'output/{model_code}_check_metabolites.csv', index=False)
def run(model_code='ecoli:iJO1366', database='kegg'):
fba_model = FBAModel(model_code)
if fba_model.species == 'ecoli':
gene_ids = pd.Series(
[f'eco:{gene.id}:{gene.name}' for gene in fba_model.model.genes])
gene_ids = pd.concat([
gene_ids, ecoli_rnap_genes + ':', ecoli_rrna_genes + ':',
ecoli_rpeptide_genes + ':'
])
gene_ids = gene_ids.sort_values()
gene_ids = gene_ids.drop_duplicates()
if database == 'kegg':
nt_sequences = gene_ids.apply(get_from_kegg)
nt_sequences.index = gene_ids.str.split(':').apply(lambda x: x[1])
nt_sequences = nt_sequences.dropna()
nt_sequences.to_csv(
f'{fba_model.species}/{fba_model.model_name}_nt_seq_kegg.csv',
header=False)
def test_align_to_yangs(self):
yangs_data = pd.read_csv('baseline/yangs_data.csv',
index_col=0).round(decimals=2)
pfba_data = FBAModel().solve(
alg='pfba', conditions=pd.read_csv('perturbations_small.csv'))
for _, condition in pd.read_csv('perturbations_small.csv').iterrows():
condition = condition.dropna()
c_name = '-'.join(condition)
x = yangs_data[c_name].to_frame()
x = x[(x.T != 0).any()]
y = pfba_data[c_name].to_frame()
y = y[(y.T != 0).any()]
align_df = pd.merge(x,
y,
left_on=x.index,
right_on=y.index,
how='outer',
suffixes=('_yangs', '_ours')).fillna(0)
align_df = align_df.set_index('key_0').sort_index()
align_df.to_csv(f'output/align_{c_name}.csv')
def test_fba_small_baseline(self):
fba_model = FBAModel()
solution = fba_model.solve(
conditions=pd.read_csv('perturbations_small.csv'))
solution.to_csv('baseline/fba_small.csv')
def test_pfba_small(self):
fba_model = FBAModel()
solution = fba_model.solve(
alg='pfba', conditions=pd.read_csv('perturbations_small.csv'))
baseline = pd.read_csv('baseline/pfba_small.csv', index_col=0)
self.assertTrue(solution.equals(baseline))
def test_small_sampling(self):
sampling_model = FBAModel()
solution = sampling_model.solve(
conditions=pd.read_csv('perturbations_small.csv'),
sampling_n=10000)
solution.to_csv('baseline/sampling_small.csv')