3.1.1.4. etfl.debugging

3.1.1.4.1. Submodules

3.1.1.4.2. Package Contents

3.1.1.4.2.1. Classes

CatalyticConstraint

Class to represent a enzymatic constraint

CatalyticActivator

Class to represent a binary variable that activates a catalytic constraint

ForwardCatalyticConstraint

Class to represent a enzymatic constraint

BackwardCatalyticConstraint

Class to represent a enzymatic constraint

3.1.1.4.2.2. Functions

localize_exp(exp)

Takes an optlang expression, and replaces symbols (tied to variables) by

compare_expressions(exp1, exp2)

Check is the two given expressions are equal

find_different_constraints(model1, model2)

Given two models, find which expressions are different

find_translation_gaps(model)

For each translation constraint in the model, finds the value of each

find_essentials_from(model, met_dict)

Given a dictionnary of {met_id:uptake_reaction}, checks the value of the

get_model_argument(args, kwargs, arg_index=0)

Utility function to get the model object from the arguments of a function

save_objective_function(fun)

Decorator to restore the objective function after the execution of the

save_growth_bounds(fun)

Decorator to save the growth bound and restore them after the execution of

perform_iMM(model, uptake_dict, min_growth_coef=0.5, bigM=1000)

An implementation of the in silico Minimal Media methods, which uses MILP

check_production_of_mets(model, met_ids)

for each metabolite ID given, create a sink and maximize the production of

relax_catalytic_constraints(model, min_growth)

Find a minimal set of catalytic constraints to relax to meet a minimum

relax_catalytic_constraints_bkwd(model, min_growth)

Find a minimal set of catalytic constraints to relax to meet a minimum
class etfl.debugging.CatalyticConstraint

Bases: pytfa.optim.ReactionConstraint

Class to represent a enzymatic constraint

prefix = CC_
class etfl.debugging.CatalyticActivator(reaction, **kwargs)

Bases: pytfa.optim.variables.ReactionVariable, pytfa.optim.variables.BinaryVariable

Class to represent a binary variable that activates a catalytic constraint or relaxes it

prefix = CA_
class etfl.debugging.ForwardCatalyticConstraint

Bases: pytfa.optim.ReactionConstraint

Class to represent a enzymatic constraint

prefix = FC_
class etfl.debugging.BackwardCatalyticConstraint

Bases: pytfa.optim.ReactionConstraint

Class to represent a enzymatic constraint

prefix = BC_
etfl.debugging.localize_exp(exp)[source]

Takes an optlang expression, and replaces symbols (tied to variables) by their string names, to compare expressions of two different models

Parameters

exp (optlang.symbolics.Expr) –

Returns

etfl.debugging.compare_expressions(exp1, exp2)[source]

Check is the two given expressions are equal

Parameters

  • exp1 (optlang.symbolics.Expr) –

  • exp2 (optlang.symbolics.Expr) –

Returns

etfl.debugging.find_different_constraints(model1, model2)[source]

Given two models, find which expressions are different

Parameters

  • model1 –

  • model2 –

Returns

etfl.debugging.find_translation_gaps(model)[source]

For each translation constraint in the model, finds the value of each variable, and then evaluates the LHS of the constraint

Constraints look like v_tsl - ktrans/L [RNAP_i] <= 0

Parameters

model –

Returns

etfl.debugging.find_essentials_from(model, met_dict)[source]

Given a dictionnary of {met_id:uptake_reaction}, checks the value of the objective function at optimality when the given uptake is closed.

Uptake reactions are expected to be aligned according to the consensus directionality for systems : met_e <=> []

Parameters

  • model –

  • met_dict –

Returns

etfl.debugging.get_model_argument(args, kwargs, arg_index=0)[source]

Utility function to get the model object from the arguments of a function

Parameters

  • args –

  • kwargs –

  • arg_index –

Returns

etfl.debugging.save_objective_function(fun)[source]

Decorator to restore the objective function after the execution of the decorated function.

Parameters

fun –

Returns

etfl.debugging.save_growth_bounds(fun)[source]

Decorator to save the growth bound and restore them after the execution of the decorated function.

Parameters

fun –

Returns

etfl.debugging.perform_iMM(model, uptake_dict, min_growth_coef=0.5, bigM=1000)[source]

An implementation of the in silico Minimal Media methods, which uses MILP to find a minimum set of uptakes necessary to meet growth requirements

See:

Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks Chiappino-Pepe A, Tymoshenko S, Ataman M, Soldati-Favre D, Hatzimanikatis V (2017) PLOS Computational Biology 13(3): e1005397. https://doi.org/10.1371/journal.pcbi.1005397

Parameters

  • model (etfl.core.memodel.MEModel) –

  • uptake_dict – {met_id : <reaction object>}

  • min_growth_coef – minimum fraction of optimal growth to be met

  • bigM – a big-M value for the optimization problem

Returns

etfl.debugging.check_production_of_mets(model, met_ids)[source]

for each metabolite ID given, create a sink and maximize the production of the metabolite

Parameters

  • model (etfl.core.memodel.MEModel) –

  • met_ids –

Returns

etfl.debugging.relax_catalytic_constraints(model, min_growth)[source]

Find a minimal set of catalytic constraints to relax to meet a minimum growth criterion

Parameters

  • model (etfl.core.memodel.MEModel) –

  • min_growth –

Returns

etfl.debugging.relax_catalytic_constraints_bkwd(model, min_growth)[source]

Find a minimal set of catalytic constraints to relax to meet a minimum growth criterion

Parameters

  • model (etfl.core.memodel.MEModel) –

  • min_growth –

Returns