MultiProcess
- pycomo.helper.multiprocess.fva(pycomo_model, reactions, fraction_of_optimum=None, verbose=False, processes=None, time_out=30, max_time_out=300)
Performs flux variability analysis.
- Parameters:
pycomo_model – A pycomo community metabolic model
reactions – A list of reactions that should be analysed
fraction_of_optimum – The fraction of the optimal objective flux that needs to be reached
use_loop_reactions_for_ko – Find loops in the model and use these reactions as ko_candidates. Overwrites value in ko_candidates
ko_candidate_ids – Reactions to be constrained and used in the objective (as set of reaction ids)
verbose – Prints progress messages
processes – The number of processes to use for the calculation
- Returns:
A dataframe of reaction flux solution ranges. Contains the columns minimum and maximum with index of reaction IDs
- pycomo.helper.multiprocess.loopless_fva(pycomo_model, reactions, fraction_of_optimum=None, use_loop_reactions_for_ko=True, ko_candidate_ids=None, verbose=False, processes=None, time_out=30, max_time_out=300)
Performs flux variability analysis and removes futile cycles from the solutions. This is achieved by fixing the direction of reactions as found in the solution, fixing the fluxes of exchange reactions and minimizing the remaining flux values. This approach is adapted from CycleFreeFLux and its implementation in COBRApy.
- Parameters:
pycomo_model – A pycomo community metabolic model
reactions – A list of reactions that should be analysed
fraction_of_optimum – The fraction of the optimal objective flux that needs to be reached
use_loop_reactions_for_ko – Find loops in the model and use these reactions as ko_candidates. Overwrites value in ko_candidates
ko_candidate_ids – Reactions to be constrained and used in the objective (as set of reaction ids)
verbose – Prints progress messages
processes – The number of processes to use for the calculation
- Returns:
A dataframe of reaction flux solution ranges. Contains the columns minimum and maximum with index of reaction IDs