5.1.3.1.2. etfba.optim.variability

Define classes for variability analysis

5.1.3.1.2.1. Classes

FVAOptimizer	FVA calculates the variability of net fluxes under the constraints of mass
TFVAOptimizer	TFVA calculates the variability of net fluxes under the constraints of mass
EFVAOptimizer	EFVA calculates the variability of net fluxes under the constraints of mass
ETFVAOptimizer	ETFVA calculates the variability of net fluxes under the constraints of mass
TVAOptimizer	TVA calculates the variability of Gibbs energy for reactions under the
ETVAOptimizer	ETVA calculates the variability of Gibbs energy for reactions under the
EVAOptimizer	EVA estimates the variability of enzyme protein costs under the constraints of
TEVAOptimizer	TEVA estimates the variability of enzyme protein costs under the constraints of

5.1.3.1.2.2. Module Contents

class etfba.optim.variability.FVAOptimizer(model, objective, direction, obj_value, gamma, flux_bound, spec_flux_bound, preset_flux, irr_reactions, ex_mass_bal_cons, **kwargs)

Bases: etfba.optim.optim.FBAOptimizer

FVA calculates the variability of net fluxes under the constraints of mass balance.

It’s advisable to run FBA initially to obtain the optimal objective. Improper objective values or gamma settings may result in the failure of estimating flux ranges.

obj_value

gamma

_build_objective(rxnid, direction)

_build_objective_constraint()

_individual_solve(solver, rxnids)

solve(solver='glpk', n_jobs=1)

Parameters:

• solver ({"glpk", "gurobi"}) – “gurobi” is highly recommended for large models.

• n_jobs (int) – Number of jobs to run in parallel.

class etfba.optim.variability.TFVAOptimizer(model, objective, direction, obj_value, gamma, flux_bound, conc_bound, spec_flux_bound, spec_conc_bound, preset_flux, preset_conc, preset_conc_ratio, irr_reactions, ex_conc, ex_mass_bal_cons, ex_thermo_cons, dgpm_conf_level, **kwargs)

Bases: FVAOptimizer, etfba.optim.optim.TFBAOptimizer

TFVA calculates the variability of net fluxes under the constraints of mass balance and thermodynamic feasibility. It’s important to note that not all reactions are subject to thermodynamic constraints.

It’s advisable to run FBA initially to obtain the optimal objective. Improper objective values or gamma settings may result in the failure of estimating flux ranges.

_individual_solve(solver, rxnids)

class etfba.optim.variability.EFVAOptimizer(model, objective, direction, obj_value, gamma, flux_bound, spec_flux_bound, preset_flux, irr_reactions, ex_mass_bal_cons, inc_enz_cons, enz_prot_lb, **kwargs)

Bases: FVAOptimizer, etfba.optim.optim.EFBAOptimizer

EFVA calculates the variability of net fluxes under the constraints of mass balance and enzyme protein allocation. It’s important to note that not all reactions are subject to enzyme protein constraints.

It’s advisable to run FBA initially to obtain the optimal objective. Improper objective values or gamma settings may result in the failure of estimating flux ranges.

_individual_solve(solver, rxnids)

class etfba.optim.variability.ETFVAOptimizer(model, objective, direction, obj_value, gamma, flux_bound, conc_bound, spec_flux_bound, spec_conc_bound, preset_flux, preset_conc, preset_conc_ratio, irr_reactions, ex_conc, ex_mass_bal_cons, ex_thermo_cons, inc_enz_cons, enz_prot_lb, dgpm_conf_level)

Bases: TFVAOptimizer, EFVAOptimizer

ETFVA calculates the variability of net fluxes under the constraints of mass balance, thermodynamic feasibility, and enzyme protein allocation. It’s important to note that not all reactions are subject to enzyme protein constraints or thermodynamic constraints.

It’s advisable to run FBA initially to obtain the optimal objective. Improper objective values or gamma settings may result in the failure of estimating flux ranges.

_individual_solve(solver, rxnids)

class etfba.optim.variability.TVAOptimizer(model, objective, direction, obj_value, gamma, flux_bound, conc_bound, spec_flux_bound, spec_conc_bound, preset_flux, preset_conc, preset_conc_ratio, irr_reactions, ex_conc, ex_mass_bal_cons, ex_thermo_cons, dgpm_conf_level, **kwargs)

Bases: TFVAOptimizer

TVA calculates the variability of Gibbs energy for reactions under the constraints of thermodynamic feasibility and mass balance.

It’s advisable to run FBA initially to obtain the optimal objective. Improper objective values or gamma settings may result in the failure of estimating Gibbs energy ranges.

_build_objective(fluxid, direction)

solve(solver='glpk', n_jobs=1)

Parameters:

• solver ({"glpk", "gurobi"}) – “gurobi” is highly recommended for large models.

• n_jobs (int) – Number of jobs to run in parallel.

class etfba.optim.variability.ETVAOptimizer(model, objective, direction, obj_value, gamma, flux_bound, conc_bound, spec_flux_bound, spec_conc_bound, preset_flux, preset_conc, preset_conc_ratio, irr_reactions, ex_conc, ex_mass_bal_cons, ex_thermo_cons, inc_enz_cons, enz_prot_lb, dgpm_conf_level)

Bases: TVAOptimizer, etfba.optim.optim.EFBAOptimizer

ETVA calculates the variability of Gibbs energy for reactions under the constraints of thermodynamic feasibility, mass balance, and enzyme protein allocation.

It’s advisable to run FBA initially to obtain the optimal objective. Improper objective values or gamma settings may result in the failure of estimating Gibbs energy ranges.

_individual_solve(solver, fluxids)

class etfba.optim.variability.EVAOptimizer(model, objective, direction, obj_value, gamma, flux_bound, spec_flux_bound, preset_flux, irr_reactions, ex_mass_bal_cons, inc_enz_cons, enz_prot_lb, **kwargs)

Bases: EFVAOptimizer

EVA estimates the variability of enzyme protein costs under the constraints of mass balance and total enzyme protein allocation.

It’s advisable to run FBA initially to obtain the optimal objective. Improper objective values or gamma settings may result in the failure of estimating enzyme protein cost ranges.

_build_objective(rxnid, direction)

solve(solver='glpk', n_jobs=1)

Parameters:

• solver ({"glpk", "gurobi"}) – “gurobi” is highly recommended for large models.

• n_jobs (int) – Number of jobs to run in parallel.

class etfba.optim.variability.TEVAOptimizer(model, objective, direction, obj_value, gamma, flux_bound, conc_bound, spec_flux_bound, spec_conc_bound, preset_flux, preset_conc, preset_conc_ratio, irr_reactions, ex_conc, ex_mass_bal_cons, ex_thermo_cons, inc_enz_cons, enz_prot_lb, dgpm_conf_level)

Bases: EVAOptimizer, etfba.optim.optim.TFBAOptimizer

TEVA estimates the variability of enzyme protein costs under the constraints of thermodynamic feasibility, mass balance, and enzyme protein allocation.

It’s advisable to run FBA initially to obtain the optimal objective. Improper objective values or gamma settings may result in the failure of estimating enzyme protein cost ranges.

_individual_solve(solver, rxnids)