def run_detfl(yaml_file, uptake_funs, medium_funs=dict(), model=None, ini_sol=None): # pass in
params = read_config(yaml_file)
for key, value in params.items():
print('%s: %s' % (key, value))
if model is None:
model = load_json_model(params['model'])
standard_solver_config(model)
model.solver.configuration.verbosity = params['options']['verbose']
time_data = run_dynamic_etfl(model,
timestep=params['simulation']['timestep'],
tfinal=params['simulation']['tfinal'],
uptake_fun=uptake_funs,
medium_fun=medium_funs,
uptake_enz=params['assumptions']['uptake_enz'],
S0=params['assumptions']['S0'],
X0=params['assumptions']['X0'],
inplace=params['options']['inplace'],
initial_solution = ini_sol,
chebyshev_include = params['options']['chebyshev_include'],
dynamic_constraints = params['options']['constraints']
)
# plot_dynamics(model, time_data)
out_path = join('outputs',params['tag']+get_timestr())
makedirs(out_path)
time_data.to_csv(join(out_path,'solution.csv'))
write_yaml(params,join(out_path,'config.yaml'))
return time_data
times = [x*timestep for x in range(1,10)]
print("Test step fun:")
print(times)
for rxn, fun in glyc_glc_switch.items():
values = [S0_glc]
_ = [values.append(fun(t,values[-1])) for t in times]
print(rxn, values)
standard_solver_config(model)
min_glycolysis_enz = prepare_model(model, S0_glyc=S0_gly, S0_glc=S0_glc)
time_data = run_dynamic_etfl(model,
timestep=timestep,
tfinal=2.5,
uptake_fun=get_uptake_funs(),
medium_fun = glyc_glc_switch,
S0={'EX_glyc_e':S0_gly, 'EX_glc__D_e':S0_glc},
X0=X0,
inplace=True,
initial_solution = min_glycolysis_enz,
chebyshev_include = [ForwardCatalyticConstraint,
BackwardCatalyticConstraint,
ExpressionCoupling]
)
plot_dynamics(model, time_data)
time_data.to_csv('outputs/detfl_cheby_glyc.csv')
print(times)
for rxn, fun in conc_funs.items():
values = [S0[rxn]]
_ = [values.append(fun(t, values[-1])) for t in times]
print(rxn, values)
standard_solver_config(model)
model.solver.configuration.verbosity = 0
min_glycolysis_enz = prepare_model(model,
S0=S0,
uptake_fun=get_uptake_funs())
time_data = run_dynamic_etfl(model,
timestep=timestep,
tfinal=3,
uptake_fun=get_uptake_funs(),
medium_fun=conc_funs,
S0=S0,
X0=X0,
inplace=True,
initial_solution=min_glycolysis_enz,
chebyshev_include=[
ForwardCatalyticConstraint,
BackwardCatalyticConstraint,
ExpressionCoupling
])
plot_dynamics(model, time_data)
time_data.to_csv('outputs/detfl_cheby_monod_{}.csv'.format(model_tag))
# Initial solution on lcts/glc mix
set_mix(10,5)
ini_sol = smol_model.optimize()
uptake_fun = {'EX_glc' :lambda x:10, # No point in putting kinetics, we just put fixed LBs
'EX_lcts':lambda x:5}
medium_fun = {'EX_glc' :lambda t,x:max(0,x), # no negative concentrations
'EX_lcts':lambda t,x:max(0,x)}
S0 = {'EX_glc' : 1, # No point in putting kinetics, we just put fixed LBs
'EX_lcts': 1}
X0 = 1
# Dyno:
tsol = run_dynamic_etfl(smol_model, timestep=0.01, tfinal=1, uptake_fun=uptake_fun, medium_fun=medium_fun,
uptake_enz=[],
S0=S0, X0=X0,
inplace=True,
initial_solution = ini_sol,
chebyshev_include=False,
dynamic_constraints={
'mRNA_degradation':False,
'mRNA_synthesis':False,
'enzyme_synthesis':True,
'enzyme_degradation':True,
},
mode='forward')
tsol.to_csv('outputs/tsol_small_model.csv')