def test_basic_gecko_adjustment():
in_model = {'P00549': 0.1, 'P31373': 0.1, 'P31382': 0.1, 'P39708': 0.1, 'P39714': 0.1, 'P39726': 0.1, 'Q01574': 0.1}
not_in_model = {'P10591': 0.1, 'P31383': 0.1, 'P32471': 0.1}
measurements = pd.concat([pd.Series(in_model), pd.Series(not_in_model)])
model = GeckoModel('multi-pool')
model.limit_proteins(fractions=pd.Series(measurements))
sol = model.optimize()
assert sol.objective_value > 0.05
assert len(model.proteins) - len(model.pool_proteins) - len(in_model) == 0
assert all(rxn.upper_bound > 0 for rxn in model.individual_protein_exchanges)
def simulate_wt():
model = GeckoModel('single-pool')
res = model.optimize()
print(res)
#for p in model.proteins:
p = "P38066"
with model:
r = model.reactions.get_by_id("draw_prot_" + p)
lb = r.lower_bound
ub = r.upper_bound
r.lower_bound = 0
r.upper_bound = 0.000001
res = model.optimize()
#r.knock_out()
#res = model.optimize()
print(p + " wt simulation1 " + str(res.objective_value))
print(str(r.lower_bound) + " --> " + str(r.upper_bound))
def analysis_ko(resFileName):
levels = [0, 1e-10, 1e-8, 1e-6, 1e-4, 1e-2, 0.1]
model = GeckoModel('single-pool')
df = pandas.DataFrame(index=range(100), columns=["ko", "Biomass"])
for i in range(100):
proteins = random.sample(model.proteins, 10)
dic = {p: 0 for p in proteins}
model.limit_proteins(pandas.Series(dic))
res = model.optimize()
df.loc[i] = (dic, res.objective_value)
df.to_csv(resFileName)
def simulate_wt_multi():
model = GeckoModel('multi-pool')
import pandas
some_measurements = pandas.Series({
'P00549': 0.1,
'P31373': 0.1,
'P31382': 0.1
})
model = GeckoModel('multi-pool')
model.limit_proteins(some_measurements)
res = model.optimize()
print(" wt simulation1 ", res.objective_value)
for r in model.reactions:
print(r.id, " --> ", res.fluxes[r.id])
def essential_prot_scale():
model = GeckoModel("single-pool")
model.solver = 'cplex'
print("Essential proteins with cplex, scale model (single-pool)")
for r in model.reactions:
r.lower_bound = r.lower_bound * 100000
r.upper_bound = r.upper_bound * 100000
for p in model.proteins:
with model as m:
r = model.reactions.get_by_id("draw_prot_" + p)
r.lower_bound = 0
r.upper_bound = 0
res = model.optimize()
print(p, ",", res.objective_value)
def simulate_prot():
model = GeckoModel("single-pool")
model.solver = 'cplex'
with model:
# for p in ["P53685","Q01574"]:
for p in ['P33421']:
r = model.reactions.get_by_id("draw_prot_" + p)
r.lower_bound = 0
r.upper_bound = 0
res = model.optimize()
print(" --> growth " + str(res.objective_value))
print(" --> r_2111 " + str(res.fluxes["r_2111"]))
print(" --> r_2056 " + str(res.fluxes["r_2056"]))
print(" --> r_1714 " + str(res.fluxes["r_1714_REV"]))
print(" ------------ ")
def analysis_growth(resFileName):
levels = [0, 1e-10, 1e-8, 1e-6, 1e-4, 1e-2, 0.1]
model = GeckoModel('single-pool')
proteins = model.proteins
df = pandas.DataFrame(index=proteins, columns=levels)
for p in proteins:
print(p)
if p != "P38066":
for level in levels:
r = model.reactions.get_by_id("draw_prot_" + p)
lb = r.lower_bound
ub = r.upper_bound
r.lower_bound = 0
r.upper_bound = level
res = model.optimize()
df.loc[p][level] = res.objective_value
r.lower_bound = lb
r.upper_bound = ub
df.to_csv(resFileName)