def init_cache(request):
m = request.config.cache.get('small_etfl', None)
m = create_etfl_model(has_thermo=False,
has_neidhardt=False,
n_mu_bins=4,
optimize=False)
request.config.cache.set('small_etfl', json_dumps_model(m))
def write_conf(model, conf):
filename = 'outputs/' + conf['tag'] + '/config.yaml'
conf['objective'] = str(model.objective.expression)
with open(filename, 'w') as fid:
yaml.dump(config, fid)
if __name__ == '__main__':
config = read_config(CONFIG)
if config['model'] != 'debug':
model = load_json_model(config['model'],
solver=config['options']['solver'])
else:
from etfl.tests.small_model import create_etfl_model
model = create_etfl_model(0, 1, solver=config['options']['solver'])
standard_solver_config(model)
model.solver.configuration.verbosity = config['options']['verbose']
copy_number = int(config['simulation']['copy_number'])
vector_generator = vec_dict[config['simulation']['vector']]
has_rnap = config['simulation']['add_rnap']
my_plasmid = vector_generator(model, has_rnap)
#####################
# Model integration #
#####################
# 1. Adding RNAP eq constraints
'EX_lcts_e': lac_fun,
'EX_o2_e': o2_diff,
'EX_ac_e': ac_fun,
}
uptake_funs = get_uptake_funs()
if not has_lcts:
medium_funs.pop('EX_lcts_e')
uptake_funs.pop('EX_lcts_e')
if config['model'] != 'debug':
model = load_json_model(config['model'])
else:
from etfl.tests.small_model import create_etfl_model
model = create_etfl_model(0, 0)
standard_solver_config(model)
model.solver.configuration.verbosity = 0
ini_sol = prepare_model(model,
v0=config['assumptions']['v0'],
S0=config['assumptions']['S0'],
uptake_fun=uptake_funs)
time_data = run_detfl(model=model,
yaml_file=CONFIG,
ini_sol=ini_sol,
uptake_funs=uptake_funs,
medium_funs=medium_funs)
from etfl.tests.small_model import create_etfl_model
from etfl.optim.config import standard_solver_config
from pytfa.optim.debugging import find_maxed_vars
m = create_etfl_model(0, 1)
mv = find_maxed_vars(m)
from etfl.io.dict import model_to_dict, model_from_dict
dm = model_to_dict(m)
md = model_from_dict(dm)
standard_solver_config(md)
md.optimize()
print(abs(m.solution.objective_value - md.solution.objective_value))
print('Objective : {}'.format(md.solution.objective_value))
print(' - Glucose uptake : {}'.format(md.reactions.EX_glc__D_e.flux))
print(' - Growth : {}'.format(md.growth_reaction.flux))
print(' - Ribosomes produced: {}'.format(md.ribosome.X))
print(' - RNAP produced: {}'.format(md.rnap.X))
print(mv)
assert abs(m.solution.objective_value - md.solution.objective_value
) < md.solver.configuration.tolerances.optimality
mus.append(sol)
return ramp, mus
for o in [growth] + objs:
result[o] = pd.DataFrame(index=scales, columns=scales)
for k in scales:
prot_scale = 10**k
for l in range(k, max(scales) + 1):
mrna_scale = 10**l
model[k, l] = create_etfl_model(has_thermo=True,
has_neidhardt=False,
mrna_scaling=mrna_scale,
prot_scaling=prot_scale)
this_model = model[k, l]
mincoeff = model[k, l].solver.problem.MinCoeff
maxcoeff = model[k, l].solver.problem.MaxCoeff
if not hasattr(this_model, 'solution'):
result[growth].loc[k, l] = np.nan
for o in objs:
result[o].loc[k, l] = np.nan
ramp_results[k, l] = glucose_ramp(this_model)
continue
coeffs[k, l] = (mincoeff, maxcoeff, np.log10(maxcoeff / mincoeff))
def test_thermo():
create_etfl_model(has_thermo=False,
has_neidhardt=True,
n_mu_bins=4,
optimize=False)