etfba.optim.variability ======================= .. py:module:: etfba.optim.variability .. autoapi-nested-parse:: Define classes for variability analysis Classes ------- .. autoapisummary:: etfba.optim.variability.FVAOptimizer etfba.optim.variability.TFVAOptimizer etfba.optim.variability.EFVAOptimizer etfba.optim.variability.ETFVAOptimizer etfba.optim.variability.TVAOptimizer etfba.optim.variability.ETVAOptimizer etfba.optim.variability.EVAOptimizer etfba.optim.variability.TEVAOptimizer Module Contents --------------- .. py:class:: FVAOptimizer(model, objective, direction, obj_value, gamma, flux_bound, spec_flux_bound, preset_flux, irr_reactions, ex_mass_bal_cons, **kwargs) Bases: :py:obj:`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. .. py:attribute:: obj_value .. py:attribute:: gamma .. py:method:: _build_objective(rxnid, direction) .. py:method:: _build_objective_constraint() .. py:method:: _individual_solve(solver, rxnids) .. py:method:: solve(solver='glpk', n_jobs=1) :param solver: "gurobi" is highly recommended for large models. :type solver: {"glpk", "gurobi"} :param n_jobs: Number of jobs to run in parallel. :type n_jobs: int .. py:class:: 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: :py:obj:`FVAOptimizer`, :py:obj:`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. .. py:method:: _individual_solve(solver, rxnids) .. py:class:: 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: :py:obj:`FVAOptimizer`, :py:obj:`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. .. py:method:: _individual_solve(solver, rxnids) .. py:class:: 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: :py:obj:`TFVAOptimizer`, :py:obj:`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. .. py:method:: _individual_solve(solver, rxnids) .. py:class:: 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: :py:obj:`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. .. py:method:: _build_objective(fluxid, direction) .. py:method:: solve(solver='glpk', n_jobs=1) :param solver: "gurobi" is highly recommended for large models. :type solver: {"glpk", "gurobi"} :param n_jobs: Number of jobs to run in parallel. :type n_jobs: int .. py:class:: 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: :py:obj:`TVAOptimizer`, :py:obj:`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. .. py:method:: _individual_solve(solver, fluxids) .. py:class:: 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: :py:obj:`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. .. py:method:: _build_objective(rxnid, direction) .. py:method:: solve(solver='glpk', n_jobs=1) :param solver: "gurobi" is highly recommended for large models. :type solver: {"glpk", "gurobi"} :param n_jobs: Number of jobs to run in parallel. :type n_jobs: int .. py:class:: 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: :py:obj:`EVAOptimizer`, :py:obj:`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. .. py:method:: _individual_solve(solver, rxnids)