geckopy.flux_analysis

COBRA flux Methods to work with/around EC model nuisances.

Module Contents

Functions

flux_variability_analysis(ec_model: geckopy.model.Model, fixed_reactions: Optional[List[str]] = None, ignored_reactions: Optional[List[str]] = None, n_proc: Optional[int] = config.processes, inplace: bool = False) → pandas.DataFrame	Flux variability analysis for EC models.
get_protein_bottlenecks(model: geckopy.model.Model, top: int = 10) → pandas.DataFrame	Return top protein bottlenecks based on the shadow prices.
protein_variability_analysis(ec_model: geckopy.model.Model, protein_list: Optional[List[str]] = None, n_proc: Optional[int] = config.processes) → pandas.DataFrame	Return the top used proteins for each reaction rate.
pfba_protein(model: geckopy.model.Model, objective: Optional[str] = None, fraction_of_optimum: float = 1.0) → cobra.core.Solution	Add pFBA objective.
get_protein_usage_by_reaction_rate(model: geckopy.model.Model, reaction: str, fix_fluxes: List[float], reaction_plot_name: str = 'Reaction rate', top: int = 10) → pandas.DataFrame	Return the top used proteins for each reaction rate.
rate_kcat_concentration(model: geckopy.model.Model, protein: str, reaction: str, kcat_range: List[float])	Calculate the flux value for each kcat in kcat_range.

geckopy.flux_analysis.flux_variability_analysis(ec_model: geckopy.model.Model, fixed_reactions: Optional[List[str]] = None, ignored_reactions: Optional[List[str]] = None, n_proc: Optional[int] = config.processes, inplace: bool = False) → pandas.DataFrame

Flux variability analysis for EC models.

This method is detailed in the Supplementary material of [Domenzain et al., 2021](https://www.biorxiv.org/content/10.1101/2021.03.05.433259v1.full.pdf).

For a fair comparison of flux distributions, EC models require some adjustments:

1. Since there are duplicated reactions (for each direction), the complementary reaction must be blocked to remove artificial flux variation.

2. Some reactions might be fixed (the glucose exchange reaction in the paper) to compare them with non-EC models.

3. The final reported flux is \(v_{max}, v_{max}^{rev} \forall v \in \text{reactions}\)

Additionally, just the combined reaction arm_ is reported for isozymes.

See also

cobra.flux_analysis.flux_variability_analysis()

Parameters

• model (geckopy.Model) –

• fixed_reactions (Optional[List[str]]) – List of reactions to be fixed (as in the second point in the description)

• ignored_reactions (Optional[List[str]]) – List of reactions to be ignored

• n_proc (int) – Number of processes to use. Default: the number of logical CPUs.

• inplace (bool) – Whether to copy the model to then apply the changes

Returns

fva_result – A data frame with reaction identifiers as the index and two columns: - maximum: indicating the highest possible flux - minimum: indicating the lowest possible flux

Return type

pandas.DataFrame

geckopy.flux_analysis.get_protein_bottlenecks(model: geckopy.model.Model, top: int = 10) → pandas.DataFrame

Return top protein bottlenecks based on the shadow prices.

geckopy.flux_analysis.protein_variability_analysis(ec_model: geckopy.model.Model, protein_list: Optional[List[str]] = None, n_proc: Optional[int] = config.processes) → pandas.DataFrame

Return the top used proteins for each reaction rate.

Parameters

• model (geckopy.Model) –

• protein_list (list[float]) –

• n_proc (Optional[int]) –

Returns

with columns ‘minimum’, ‘maximum’ and index as Protein.id

Return type

pandas.DataFrame

geckopy.flux_analysis.pfba_protein(model: geckopy.model.Model, objective: Optional[str] = None, fraction_of_optimum: float = 1.0) → cobra.core.Solution

Add pFBA objective.

Add objective to minimize the summed flux of all proteins to the current objective.

See also

cobra.flux_analysis.pfba()

Parameters

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

• objective – An objective to set in combination with the pFBA objective.

• fraction_of_optimum (float) – Fraction of optimum which must be maintained. The original objective reaction is constrained to be greater than maximal_value * fraction_of_optimum.

• Result –

• ------ –

• solution (cobra.core.Solution) –

geckopy.flux_analysis.get_protein_usage_by_reaction_rate(model: geckopy.model.Model, reaction: str, fix_fluxes: List[float], reaction_plot_name: str = 'Reaction rate', top: int = 10) → pandas.DataFrame

Return the top used proteins for each reaction rate.

Parameters

• model (geckopy.Model) –

• reaction (str) – id of the reaction whose flux will be fixed to each fix_fluxes.

• fix_fluxes (list[float]) – the model will be optimized by sequentally fixing the reaction bounds to these fluxes.

• top (int) – number of top proteins to return for each condition

Returns

with colums ‘protein’, ‘fluxes’, ‘reduced_costs’, reaction_plot_name, ‘gene’

Return type

pandas.DataFrame

Example

We can make a bar plot with a slider for each uptake rate using plotly express:

>>> import plotly.express as px
>>> model.constrain_pool(0.448, 0.65, 0.8)
>>> fluxes = geckopy.flux_analysis.get_protein_usage_by_reaction_rate(
        model, "EX_glc__D_e", [-0.1, -0.2, -0.5, -1.0, -5.0, -10.0], "Glc uptake"
    )
>>> # return percentages of the total protein pool
>>> fluxes.fluxes *= 100 / 0.448
>>> px.bar(
        fluxes, x="protein", y="fluxes", hover_data=["gene"], color="fluxes",
        animation_frame="Glc uptake",
        color_continuous_scale="TealGrn", template="simple_white"
    )

geckopy.flux_analysis.rate_kcat_concentration(model: geckopy.model.Model, protein: str, reaction: str, kcat_range: List[float])

Calculate the flux value for each kcat in kcat_range.