def add_min_log_displacement(tmodel,
min_log_displacement,
tva_fluxes=None,
inplace=True):
EPSILON = tmodel.solver.configuration.tolerances.feasibility
if inplace:
temp_model = tmodel
else:
temp_model = tmodel.copy()
if tva_fluxes is None:
tva_fluxes = variability_analysis(temp_model, kind='reactions')
for ln_gamma in temp_model.thermo_displacement:
if tva_fluxes["minimum"][ln_gamma.id] >= -EPSILON and \
tva_fluxes["maximum"][ln_gamma.id] > -EPSILON :
ln_gamma.variable.ub = -min_log_displacement
elif tva_fluxes["minimum"][ln_gamma.id] < EPSILON and \
tva_fluxes["maximum"][ln_gamma.id] <= EPSILON :
ln_gamma.variable.lb = min_log_displacement
else:
raise ValueError("Not a model with fixed directionality!")
temp_model.repair()
return temp_model
continuous_model.growth_reaction.upper_bound = 10
continuous_model.reactions.EX_glc__D_e.lower_bound = glc_uptake - 0.1
continuous_model.reactions.EX_glc__D_e.upper_bound = glc_uptake + 0.1
continuous_model.optimize()
mu = continuous_model.growth_reaction.flux
mu_lo, mu_hi = get_mu_bin(continuous_model, mu)
continuous_model.growth_reaction.upper_bound = mu_hi
continuous_model.growth_reaction.lower_bound = mu_lo
print_sol(continuous_model)
variables = EnzymeVariable
eva = variability_analysis(continuous_model, variables)
peptides_conc_min = pd.Series(enzymes_to_peptides_conc(continuous_model, eva['minimum']))
peptides_conc_max = pd.Series(enzymes_to_peptides_conc(continuous_model, eva['maximum']))
peptides_conc = pd.concat([peptides_conc_min,peptides_conc_max], axis=1)
peptides_conc.to_csv('outputs/iJO_T1E1N1_low_hi_{}_pep.csv'.format(glc_uptake))
rescale = lambda row: ecoli.mrnas.get_by_id(row.name[3:]).scaling_factor * row
eva_real = eva.apply(rescale, axis=1)
eva_real.to_csv('outputs/iJO_T1E1N1_low_hi_{}_enz.csv'.format(glc_uptake))
# mva = variability_analysis(continuous_model, mRNAVariable)
# rescale = lambda row: ecoli.mrnas.get_by_id(row.name[3:]).scaling_factor * row
# mva_real = mva.apply(rescale, axis=1)
# mva_real.to_csv('outputs/iJO_T1E1N1_low_hi_{}_mrna.csv'.format(glc_uptake))
# Set the solver
tmodel.solver = GLPK
## TFA conversion
tmodel.prepare()
tmodel.convert(add_displacement=True)
## Info on the cobra_model
tmodel.print_info()
## Optimality
solution = tmodel.optimize()
# Calculate variability analysis on all continuous variables
fva_fluxes = flux_variability_analysis(cobra_model)
tva_fluxes = variability_analysis(tmodel, kind='reactions')
# Add more specific concentration data
def apply_concentration_bound(met, lb, ub):
the_conc_var = tmodel.log_concentration.get_by_id(met)
# Do not forget the variables in the model are logs !
the_conc_var.ub = log(ub)
the_conc_var.lb = log(lb)
apply_concentration_bound('atp_c', lb=1e-3, ub=1e-2)
apply_concentration_bound('adp_c', lb=4e-4, ub=7e-4)
apply_concentration_bound('amp_c', lb=2e-4, ub=3e-4)
tmodel.optimize()
## TFA conversion
tmodel.prepare()
tmodel.convert(add_displacement=True)
## Info on the cobra_model
tmodel.print_info()
## Optimality
solution = tmodel.optimize()
# Apply the directionality of the solution to the cobra_model
fixed_directionality_model = apply_directionality(tmodel, solution)
# Calculate variability analysis on all continuous variables
tva_fluxes = variability_analysis(fixed_directionality_model, kind='reactions')
thermo_vars = [DeltaG, DeltaGstd, ThermoDisplacement]
tva_thermo = variability_analysis(fixed_directionality_model, kind=thermo_vars)
tight_model = apply_reaction_variability(fixed_directionality_model,
tva_fluxes)
tight_model = apply_generic_variability(tight_model, tva_thermo)
## Sample space
from pytfa.optim import strip_from_integer_variables
from pytfa.analysis import sample
continuous_model = strip_from_integer_variables(tight_model)
sampling = sample(continuous_model, 10, processes=10)
directory = 'outputs/'