def test_relax_thermo_dgr_and_proteins_works(
ec_model_core,
experimental_copy_number,
thermodb,
compartment_data,
slim_solution_core,
):
"""Test that constraining the model works."""
raw_proteomics = pd.read_csv(experimental_copy_number)
ec_model = from_copy_number(
ec_model_core.copy(),
index=raw_proteomics["uniprot"],
cell_copies=raw_proteomics["copies_per_cell"],
stdev=raw_proteomics["stdev"],
vol=2.3,
dens=1.105e-12,
water=0.3,
)
thermodb = load_thermoDB(thermodb)
compartment_data = pytfa.io.read_compartment_data(compartment_data)
tmodel = adapt_gecko_to_thermo(ec_model, thermodb, compartment_data)
sol_pre_relax = tmodel.slim_optimize()
tmodel.reactions.BIOMASS_Ecoli_core_w_GAM.lower_bound = slim_solution_core
# this is an inplace operation so we have to regenerate the model after it
iis_sum_obj, status = relax_thermo_proteins(
tmodel,
[
prot.id for prot in tmodel.proteins
if prot.concentration is not None and not isnan(prot.concentration)
],
Objective_rule.MIN_ELASTIC_SUM_OBJECTIVE,
)
assert status == "optimal"
ec_model = from_copy_number(
ec_model_core.copy(),
index=raw_proteomics["uniprot"],
cell_copies=raw_proteomics["copies_per_cell"],
stdev=raw_proteomics["stdev"],
vol=2.3,
dens=1.105e-12,
water=0.3,
)
tmodel = adapt_gecko_to_thermo(ec_model, thermodb, compartment_data)
for var in iis_sum_obj:
if var in tmodel.proteins:
tmodel.proteins.get_by_id(var).concentration = None
sol_post_relax = tmodel.slim_optimize()
assert sol_pre_relax < 0.1 if not isnan(sol_pre_relax) else True
assert sol_post_relax > 0.1
def test_ec_model_from_copy_number_reduces_grow(ec_model_core,
experimental_copy_number):
"""Test that constraining the model works."""
raw_proteomics = pd.read_csv(experimental_copy_number)
ec_model = from_copy_number(
ec_model_core,
index=raw_proteomics["uniprot"],
cell_copies=raw_proteomics["copies_per_cell"],
stdev=raw_proteomics["stdev"],
vol=2.3,
dens=1.105e-12,
water=0.3,
)
sol = ec_model.slim_optimize()
assert sol < 0.1 if not isnan(sol) else True
def test_relax_concentrations_and_proteins_works(
ec_model_core,
experimental_copy_number,
thermodb,
compartment_data,
slim_solution_core,
):
"""Test that constraining the model works."""
raw_proteomics = pd.read_csv(experimental_copy_number)
ec_model = from_copy_number(
ec_model_core.copy(),
index=raw_proteomics["uniprot"],
cell_copies=raw_proteomics["copies_per_cell"],
stdev=raw_proteomics["stdev"],
vol=2.3,
dens=1.105e-12,
water=0.3,
)
thermodb = load_thermoDB(thermodb)
compartment_data = pytfa.io.read_compartment_data(compartment_data)
tmodel = adapt_gecko_to_thermo(ec_model, thermodb, compartment_data)
sol_pre_relax = tmodel.slim_optimize()
tmodel.variables.LC_atp_c.ub = 2e2
tmodel.variables.LC_atp_c.lb = 1e2
tmodel.reactions.BIOMASS_Ecoli_core_w_GAM.lower_bound = slim_solution_core * 0.4
# this is an inplace operation so we have to regenerate the model after it
iis_sum_obj, status = relax_thermo_concentrations_proteins(
tmodel,
[
prot.id for prot in tmodel.proteins
if prot.concentration is not None and not isnan(prot.concentration)
],
Objective_rule.MIN_ELASTIC_SUM_OBJECTIVE,
)
tmodel.objective = tmodel.reactions.BIOMASS_Ecoli_core_w_GAM
sol_post_relax = tmodel.slim_optimize()
# in the MILP relaxation atp is relaxed, whereas in the SUM LP OBJ relaxation
# adp, gln and pi gets relaxed (in the opposite direction) compensating atp
assert "NegSlackLC_atp_c" in iis_sum_obj or "PosSlackLC_adp_c" in iis_sum_obj
assert status == "optimal"
assert sol_pre_relax < 0.1 if not isnan(sol_pre_relax) else True
assert pytest.approx(sol_post_relax) == slim_solution_core * 0.4
def test_relaxed_ec_model_from_copy_number_can_grow(ec_model_core,
experimental_copy_number):
"""Test that constraining the model works."""
raw_proteomics = pd.read_csv(experimental_copy_number)
ec_model = from_copy_number(
ec_model_core,
index=raw_proteomics["uniprot"],
cell_copies=raw_proteomics["copies_per_cell"],
stdev=raw_proteomics["stdev"],
vol=2.3,
dens=1.105e-12,
water=0.3,
)
sol = ec_model.slim_optimize()
sol = sol if not isnan(sol) and sol else 0.0
ec_model.reactions.BIOMASS_Ecoli_core_w_GAM.lower_bound = 0.8
iis, _ = get_upper_relaxation(ec_model.copy(),
[prot.id for prot in ec_model.proteins])
ec_model.reactions.BIOMASS_Ecoli_core_w_GAM.lower_bound = 0.0
final_gr, prots = relax_proteomics_greedy(ec_model, 0.5, protein_set=iis)
relaxed_sol = ec_model.slim_optimize()
assert relaxed_sol >= 0.5 and relaxed_sol > sol
def test_irreductibly_relaxed_ec_model_from_copy_number_can_grow(
ec_model_core, experimental_copy_number):
"""Test that constraining the model works."""
raw_proteomics = pd.read_csv(experimental_copy_number)
ec_model = from_copy_number(
ec_model_core,
index=raw_proteomics["uniprot"],
cell_copies=raw_proteomics["copies_per_cell"],
stdev=raw_proteomics["stdev"],
vol=2.3,
dens=1.105e-12,
water=0.3,
)
sol = ec_model.slim_optimize()
sol = sol if not isnan(sol) and sol else 0.0
ec_model.reactions.BIOMASS_Ecoli_core_w_GAM.lower_bound = 0.8
iis = elastic_upper_relaxation(ec_model,
[prot.id for prot in ec_model.proteins])
ec_model.reactions.BIOMASS_Ecoli_core_w_GAM.lower_bound = 0.0
for prot_id in iis:
ec_model.proteins.get_by_id(prot_id).concentration = None
relaxed_sol = ec_model.slim_optimize()
assert relaxed_sol >= 0.2 and relaxed_sol > sol