loopless_utils

Functions

`loops_enumeration_from_fva`(→ List)	Function that aims to identify loopy reaction from Flux Variability Analysis.
`loopy_reactions_turned_off_in_pfba`(→ List)	Function that identifies which reactions from the loopy reactions set calculated with the loops_enumeration_from_fva function
`get_loopless_solutions_from_samples`(...)	Function that calculates the loopless_solution from each sample and saves results into a new numpy 2D array
`calculate_affected_samples`(→ Tuple[List, int])	Function that given the default and the get_loopless_solutions_from_samples samples identifies how many samples have significant differences.
`calculate_distances_from_samples`(...)	Function that calculates the euclidean distance between a sampling dataset before and after applying the get_loopless_solutions_from_samples.
`calculate_distances_from_reactions`(...)	Function that calculates the euclidean distance between a sampling dataset before and after applying the get_loopless_solutions_from_samples.
`violin_plot_samples_distances`(distances_array[, ...])	Function that creates a violin plot from a given distances_array

Module Contents

loopless_utils.loops_enumeration_from_fva(ec_cobra_model: cobra.Model, fraction_of_optimum: float = 1.0) → List

Function that aims to identify loopy reaction from Flux Variability Analysis. This method was introduced here: https://doi.org/10.1016/j.bpj.2010.12.3707

Keyword arguments: cobra_model (Model) – cobra model object fraction_of_optimum (float) – Float variable that defines the fraction_of_optimum parameter used in flux_variability_analysis

Returns: loopy_reactions (List) – List with reactions classified as loopy based on the algorithm

loopless_utils.loopy_reactions_turned_off_in_pfba(loopy_cobra_model: cobra.Model, loopy_reactions: List, tol: float = 1e-06) → List

Function that identifies which reactions from the loopy reactions set calculated with the loops_enumeration_from_fva function can be turned off when performing a pFBA or a loopless_solution to pFBA results

Keyword arguments: loopy_cobra_model (Model) – cobra model object possibly containing loops (default model without any preproccess) loopy_reactions (List) – loopy_reactions (List) – List with reactions classified as loopy tol (float) – Tolerance value used to classify a numeric change as important or not

Returns: turned_off_reactions (List) – List with reactions turned of in pFBA with loopless_solution applied

loopless_utils.get_loopless_solutions_from_samples(samples: numpy.typing.NDArray[numpy.float64], cobra_model: cobra.Model) → numpy.typing.NDArray[numpy.float64]

Function that calculates the loopless_solution from each sample and saves results into a new numpy 2D array

Keyword arguments: samples (NDArray[np.float64]) – Numpy 2D array of the samples cobra_model (model) – cobra model object

Returns: samples_loopless_solutions (NDArray[np.float64]) – Numpy 2D array of the samples after application of loopless_solution

loopless_utils.calculate_affected_samples(initial_samples: numpy.typing.NDArray[numpy.float64], loopless_samples: numpy.typing.NDArray[numpy.float64], cobra_model: cobra.Model, tol_reaction_difference: float = 0.01, tol_reactions_count: int = 1) → Tuple[List, int]

Function that given the default and the get_loopless_solutions_from_samples samples identifies how many samples have significant differences. This is done by calculating (for each sample) the distance (change) of the reactions from the given arrays. Reactions that have a difference higher than a given threshold are counted and the corresponding sample can potentially be classified as a loopy sample. However, if user does not consider importance when only 1 reaction overcomes this tolerance, he can specify the least number of reactions needed for this classification (e.g 2, 5, 10 ..).

Keyword arguments: initial_samples (NDArray[np.float64]) – Numpy 2D array of the default samples loopless_samples (NDArray[np.float64]) – Numpy 2D array of the default samples after application of the get_loopless_solutions_from_samples cobra_model (Model) – ocbra model object tol_reaction_difference (float) – Tolerance value to classify the difference in reactions before and after get_loopless_solutions_from_samples as important tol_reactions_count (int) – Tolerance value to classify a sample as loopy based on the least amount of significantly altered reactions

Returns: Tuple[List, int]

affected_reactions_count (List) – List containing the number of affected (significantly altered) reactions across all samples total_affected_samples (int) – The total number of affected (significantly altered) samples

loopless_utils.calculate_distances_from_samples(initial_samples: numpy.typing.NDArray[numpy.float64], loopless_samples: numpy.typing.NDArray[numpy.float64], cobra_model: cobra.Model) → numpy.typing.NDArray[numpy.float64]

Function that calculates the euclidean distance between a sampling dataset before and after applying the get_loopless_solutions_from_samples. Distances are calculated between different samples, so if user has 3000 samples, he will end up with 3000 distances.

Keyword arguments: initial_samples (NDArray[np.float64]) – Numpy 2D array of the default samples loopless_samples (NDArray[np.float64]) – Numpy 2D array of the default samples after application of the get_loopless_solutions_from_samples cobra_model (Model) – cobra model object

Returns: distances_array (NDArray[np.float64]) – Numpy 1D array of the samples’ euclidean distances before and after the get_loopless_solutions_from_samples

loopless_utils.calculate_distances_from_reactions(initial_samples: numpy.typing.NDArray[numpy.float64], loopless_samples: numpy.typing.NDArray[numpy.float64], cobra_model: cobra.Model) → numpy.typing.NDArray[numpy.float64]

Function that calculates the euclidean distance between a sampling dataset before and after applying the get_loopless_solutions_from_samples. Distances are calculated between different reactions, so if user provides samples of 100 reactions, he will end up with 100 distances.

Keyword arguments: initial_samples (NDArray[np.float64]) – Numpy 2D array of the default samples loopless_samples (NDArray[np.float64]) – Numpy 2D array of the default samples after application of the get_loopless_solutions_from_samples cobra_model (Model) – cobra model object

Returns: distances_array (NDArray[np.float64]) – Numpy 1D array of the reactions’ euclidean distances before and after the get_loopless_solutions_from_samples

loopless_utils.violin_plot_samples_distances(distances_array: numpy.typing.NDArray[numpy.float64], title: str = '', exponentformat: str = 'none')

Function that creates a violin plot from a given distances_array

Keyword arguments:

distances_array (NDArray[np.float64]) – Numpy 1D array of either the reactions’ or samples’ euclidean distances before and after: the get_loopless_solutions_from_samples

title (str) – Title for the plot exponentformat (str) – Parameter used to determine the numeric scientific notation in the plot (determines a formatting rule for the tick exponents).

Available options: “e”, “E”, “power”, “SI”, “B”