:py:mod:`cobra.flux_analysis.variability` ========================================= .. py:module:: cobra.flux_analysis.variability .. autoapi-nested-parse:: Provide variability based methods such as flux variability or gene essentiality. Module Contents --------------- Functions ~~~~~~~~~ .. autoapisummary:: cobra.flux_analysis.variability._init_worker cobra.flux_analysis.variability._fva_step cobra.flux_analysis.variability.flux_variability_analysis cobra.flux_analysis.variability.find_blocked_reactions cobra.flux_analysis.variability.find_essential_genes cobra.flux_analysis.variability.find_essential_reactions Attributes ~~~~~~~~~~ .. autoapisummary:: cobra.flux_analysis.variability.logger cobra.flux_analysis.variability.configuration .. py:data:: logger .. py:data:: configuration .. py:function:: _init_worker(model: cobra.Model, loopless: bool, sense: str) -> None Initialize a global model object for multiprocessing. :param model: The model to operate on. :type model: cobra.Model :param loopless: Whether to use loopless version. :type loopless: bool :param sense: Whether to maximise or minimise objective. :type sense: {"max", "min"} .. py:function:: _fva_step(reaction_id: str) -> Tuple[str, float] Take a step for calculating FVA. :param reaction_id: The ID of the reaction. :type reaction_id: str :returns: The reaction ID with the flux value. :rtype: tuple of (str, float) .. py:function:: flux_variability_analysis(model: cobra.Model, reaction_list: Optional[List[Union[cobra.Reaction, str]]] = None, loopless: bool = False, fraction_of_optimum: float = 1.0, pfba_factor: Optional[float] = None, processes: Optional[int] = None) -> pandas.DataFrame Determine the minimum and maximum flux value for each reaction. :param model: The model for which to run the analysis. It will *not* be modified. :type model: cobra.Model :param reaction_list: The reactions for which to obtain min/max fluxes. If None will use all reactions in the model (default None). :type reaction_list: list of cobra.Reaction or str, optional :param loopless: Whether to return only loopless solutions. This is significantly slower. Please also refer to the notes (default False). :type loopless: bool, optional :param fraction_of_optimum: Must be <= 1.0. Requires that the objective value is at least the fraction times maximum objective value. A value of 0.85 for instance means that the objective has to be at least at 85% percent of its maximum (default 1.0). :type fraction_of_optimum: float, optional :param pfba_factor: Add an additional constraint to the model that requires the total sum of absolute fluxes must not be larger than this value times the smallest possible sum of absolute fluxes, i.e., by setting the value to 1.1 the total sum of absolute fluxes must not be more than 10% larger than the pFBA solution. Since the pFBA solution is the one that optimally minimizes the total flux sum, the `pfba_factor` should, if set, be larger than one. Setting this value may lead to more realistic predictions of the effective flux bounds (default None). :type pfba_factor: float, optional :param processes: The number of parallel processes to run. If not explicitly passed, will be set from the global configuration singleton (default None). :type processes: int, optional :returns: A data frame with reaction identifiers as the index and two columns: - maximum: indicating the highest possible flux - minimum: indicating the lowest possible flux :rtype: pandas.DataFrame .. rubric:: Notes This implements the fast version as described in [1]_. Please note that the flux distribution containing all minimal/maximal fluxes does not have to be a feasible solution for the model. Fluxes are minimized/maximized individually and a single minimal flux might require all others to be sub-optimal. Using the loopless option will lead to a significant increase in computation time (about a factor of 100 for large models). However, the algorithm used here (see [2]_) is still more than 1000x faster than the "naive" version using `add_loopless(model)`. Also note that if you have included constraints that force a loop (for instance by setting all fluxes in a loop to be non-zero) this loop will be included in the solution. .. rubric:: References .. [1] Computationally efficient flux variability analysis. Gudmundsson S, Thiele I. BMC Bioinformatics. 2010 Sep 29;11:489. doi: 10.1186/1471-2105-11-489, PMID: 20920235 .. [2] 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. .. py:function:: find_blocked_reactions(model: cobra.Model, reaction_list: Optional[List[Union[cobra.Reaction, str]]] = None, zero_cutoff: Optional[float] = None, open_exchanges: bool = False, processes: Optional[int] = None) -> List[cobra.Reaction] Find reactions that cannot carry any flux. The question whether or not a reaction is blocked is highly dependent on the current exchange reaction settings for a COBRA model. Hence an argument is provided to open all exchange reactions. :param model: The model to analyze. :type model: cobra.Model :param reaction_list: List of reactions to consider, the default includes all model reactions (default None). :type reaction_list: list of cobra.Reaction or str, optional :param zero_cutoff: Flux value which is considered to effectively be zero. The default is set to use `model.tolerance` (default None). :type zero_cutoff: float, optional :param open_exchanges: Whether or not to open all exchange reactions to very high flux ranges (default False). :type open_exchanges: bool, optional :param processes: The number of parallel processes to run. Can speed up the computations if the number of reactions is large. If not explicitly passed, it will be set from the global configuration singleton (default None). :type processes: int, optional :returns: List with the identifiers of blocked reactions. :rtype: list of cobra.Reaction .. rubric:: Notes Sink and demand reactions are left untouched. Please modify them manually. .. py:function:: find_essential_genes(model: cobra.Model, threshold: Optional[float] = None, processes: Optional[int] = None) -> Set[cobra.Gene] Return a set of essential genes. A gene is considered essential if restricting the flux of all reactions that depend on it to zero causes the objective, e.g., the growth rate, to also be zero, below the threshold, or infeasible. :param model: The model to find the essential genes for. :type model: cobra.Model :param threshold: Minimal objective flux to be considered viable. By default this is 1% of the maximal objective (default None). :type threshold: float, optional :param processes: The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not explicitly passed, it will be set from the global configuration singleton (default None). :type processes: int, optional :returns: Set of essential genes. :rtype: set of cobra.Gene .. py:function:: find_essential_reactions(model: cobra.Model, threshold: Optional[float] = None, processes: Optional[int] = None) -> Set[cobra.Reaction] Return a set of essential reactions. A reaction is considered essential if restricting its flux to zero causes the objective, e.g., the growth rate, to also be zero, below the threshold, or infeasible. :param model: The model to find the essential reactions for. :type model: cobra.Model :param threshold: Minimal objective flux to be considered viable. By default this is 1% of the maximal objective (default None). :type threshold: float, optional :param processes: The number of parallel processes to run. Can speed up the computations if the number of knockouts to perform is large. If not explicitly passed, it will be set from the global configuration singleton (default None). :type processes: int, optional :returns: Set of essential reactions. :rtype: set of cobra.Reaction