cobra.flux_analysis.loopless

Provide functions to remove thermodynamically infeasible loops.

Functions

add_loopless(→ None)	Modify a model so all feasible flux distributions are loopless.
_add_cycle_free(→ None)	Add constraints for CycleFreeFlux.
loopless_solution(→ cobra.Solution)	Convert an existing solution to a loopless one.
loopless_fva_iter(→ Union[float, Dict[str, float]])	Plugin to get a loopless FVA solution from single FVA iteration.

Module Contents

cobra.flux_analysis.loopless.add_loopless(model: cobra.Model, zero_cutoff: float | None = None, method: str = 'fastSNP', reactions: List[str] | None = None) → None

Modify a model so all feasible flux distributions are loopless.

It adds variables and constraints to a model which will disallow flux distributions with loops. This function will modify your model.

The used formulation is described in [1]_. If method is set to “fastSNP”, it uses a faster implementation based on the Fast-SNP algorithm [2].

In most cases you probably want to use the much faster loopless_solution. May be used in cases where you want to add complex constraints and objecives (for instance quadratic objectives) to the model afterwards or use an approximation of Gibbs free energy directions in your model.

Parameters:

• model (cobra.Model) – The model to which to add the constraints.

• zero_cutoff (positive float, optional) – Cutoff used for null space. Coefficients with an absolute value smaller than zero_cutoff are considered to be zero. The default uses the model.tolerance (default None).

• method (str, "original" or "fastSNP", optional) – The method to use for finding the null space. The “original” method uses the original method from [1]_, while “fastSNP” uses a faster implementation based on the FastSNP algorithm. The “fastSNP” method is much faster and should be used in most cases.

• reactions (list of str, optional) – The list of reaction IDs to constrain. All cycles within these reactions will be removed. If None, all reactions will be constrained.

References

[1]

Elimination of thermodynamically infeasible loops in steady-state metabolic models. Schellenberger J, Lewis NE, Palsson BO. Biophys J. 2011 Feb 2;100(3):544-53. doi: 10.1016/j.bpj.2010.12.3707. Erratum in: Biophys J. 2011 Mar 2;100(5):1381.

[2]

Fast-SNP: a fast matrix pre-processing algorithm for efficient loopless flux optimization of metabolic models. Saa PA, Nielsen LK. Bioinformatics. 2016 Dec;32(24):3807–3814. doi: 10.1093/bioinformatics/btw555.

cobra.flux_analysis.loopless._add_cycle_free(model: cobra.Model, fluxes: Dict[str, float]) → None

Add constraints for CycleFreeFlux.

Parameters:

• model (cobra.Model) – The model to operate on.

• fluxes (dict of {str: float}) – A dictionary having keys as reaction IDs and values as their flux values.

cobra.flux_analysis.loopless.loopless_solution(model: cobra.Model, fluxes: Dict[str, float] | None = None) → cobra.Solution

Convert an existing solution to a loopless one.

Removes as many loops as possible (see Notes).

Uses the method from CycleFreeFlux [1]_ and is much faster than add_loopless and should therefore be the preferred option to get loopless flux distributions.

Parameters:

• model (cobra.Model) – The model to which to add the constraints.

• fluxes (dict of {str, float}, optional) – A dictionary having keys as reaction IDs and values as their flux values. If not None will use the provided flux values to obtain a close loopless solution (default None).

Returns:

A solution object containing the fluxes with the least amount of loops possible or None if the optimization failed (usually happening if the flux distribution in fluxes is infeasible).

Return type:

cobra.Solution

Notes

The returned flux solution has the following properties:

• It contains the minimal number of loops possible and no loops at all if all flux bounds include zero and the objective is not in a cycle.

• It has the same objective value as the original flux solution and assumes that the objective does not participate in a cycle (which is usually true since it consumes metabolites).

• It has the same exact exchange fluxes as the previous solution.

• All fluxes have the same sign (flow in the same direction) as the previous solution.

When providing fluxes to the method, please note that those have to come from the exact same model that you provided, meaning that no bounds or coefficients have been changed, and the optimum has remained the same.

References

[1]

CycleFreeFlux: efficient removal of thermodynamically infeasible loops from flux distributions. Desouki AA, Jarre F, Gelius-Dietrich G, Lercher MJ. Bioinformatics. 2015 Jul 1;31(13):2159-65. doi: 10.1093/bioinformatics/btv096.

cobra.flux_analysis.loopless.loopless_fva_iter(model: cobra.Model, reaction: cobra.Reaction, solution: bool = False, zero_cutoff: float | None = None) → float | Dict[str, float]

Plugin to get a loopless FVA solution from single FVA iteration.

Assumes the following about model and reaction: 1. The model objective is set to be reaction. 2. The model has been optimized and contains the minimum/maximum flux

for reaction.

3. The model contains an auxiliary variable called “fva_old_objective” denoting the previous objective.

Parameters:

• model (cobra.Model) – The model to be used.

• reaction (cobra.Reaction) – The reaction currently minimized/maximized.

• solution (bool, optional) – Whether to return the entire solution or only the minimum/maximum for reaction (default False).

• zero_cutoff (positive float, optional) – Cutoff used for loop removal. Fluxes with an absolute value smaller than zero_cutoff are considered to be zero. The default is to use model.tolerance (default None).

Returns:

single float or dict of {str – Returns the minimized/maximized flux through reaction if solution is False. Otherwise, returns a loopless flux solution object containing the minimum/maximum flux for reaction.

Return type:

float}