cameo.flux_analysis package

Submodules

cameo.flux_analysis.analysis module

cameo.flux_analysis.analysis.find_blocked_reactions(model)[source]

Determine reactions that cannot carry steady-state flux.

Parameters:model (SolverBasedModel) –
Returns:A list of reactions.
Return type:list
cameo.flux_analysis.analysis.flux_variability_analysis(model, reactions=None, fraction_of_optimum=0.0, pfba_factor=None, remove_cycles=False, view=None)[source]

Flux variability analysis.

Parameters:
  • model (cameo.core.SolverBasedModel) –
  • reactions (None or iterable) – The list of reaction whose lower and upper bounds should be determined. If None, all reactions in model will be assessed.
  • fraction_of_optimum (float) – Fix the objective of the model to a fraction of it’s max. Expected to be within [0;1]. Lower values increase variability.
  • pfba_factor (float) – If not None, fix the total sum of reaction fluxes to its minimum times a factor. Expected to be within [ 1;inf]. Higher factors increase flux variability to a certain point since the bound for the objective is still fixed.
  • remove_cycles (bool) – If true, apply the CycleFreeFlux algorithm to remove loops from each simulated flux distribution.
  • view (cameo.parallel.SequentialView or cameo.parallel.MultiprocessingView or ipython.cluster.DirectView) – A parallelization view.
Returns:

Pandas DataFrame containing the results of the flux variability analysis.
Return type:

pandas.DataFrame
cameo.flux_analysis.analysis.phenotypic_phase_plane(model, variables=[], objective=None, source=None, points=20, view=None)[source]

Phenotypic phase plane analysis [1].

Implements a phenotypic phase plan analysis with interpretation same as that presented in [1] but calculated by optimizing the model for all steps of the indicated variables (instead of using shadow prices).

Parameters:
  • model (SolverBasedModel) –
  • variables (str or reaction or iterable) – A reaction ID, reaction, or list of reactions to be varied.
  • objective (str or reaction or optlang.Objective or Metabolite, optional) – An objective, a reaction’s flux, or a metabolite’s production to be minimized/maximized (defaults to the current model objective).
  • source (Reaction or reaction identifier) – The reaction to use as the source when calculating mass and carbon yield. Set to the medium reaction with the highest input carbon flux if left none.
  • points (int or iterable) – Number of points to be interspersed between the variable bounds. A list of same same dimensions as variables can be used to specify variable specific numbers of points.
  • view (SequentialView or MultiprocessingView or ipython.cluster.DirectView) – A parallelization view.
Returns:

The phenotypic phase plane with flux, mol carbon input / mol carbon output, gram input / gram output
Return type:

PhenotypicPhasePlaneResult

References

[1] Edwards, J. S., Ramakrishna, R. and Palsson, B. O. (2002). Characterizing the metabolic phenotype: a phenotype
phase plane analysis. Biotechnology and Bioengineering, 77(1), 27–36. doi:10.1002/bit.10047
cameo.flux_analysis.analysis.flux_balance_impact_degree(model, knockouts, view=<cameo.parallel.SequentialView object>, method=’fva’)[source]

Flux balance impact degree by Zhao et al 2013

Parameters:
  • model (SolverBasedModel) – Wild-type model
  • knockouts (list) – Reactions to knockout
  • method (str) – The method to compute the perturbation. default is “fva” - Flux Variability Analysis. It can also be computed with “em” - Elementary modes (not implemented)
Returns:

FluxBalanceImpactDegreeResult – The changes in reachable reactions (reactions that can carry flux)
Return type:

perturbation

cameo.flux_analysis.simulation module

Methods for simulating flux distributions. Currently implements: * fba - Flux Balance Analysis * pfba - Parsimonious Flux Balance Analysis * lmoma - (Linear) Minimization of Metabolic Adjustment * room - Regulatory On/Off Minimization

cameo.flux_analysis.simulation.fba(model, objective=None, reactions=None, *args, **kwargs)[source]

Flux Balance Analysis.

Parameters:
  • model (SolverBasedModel) –
  • objective (a valid objective - see SolverBaseModel.objective (optional)) –
Returns:

Contains the result of the linear solver.
Return type:

FluxDistributionResult
cameo.flux_analysis.simulation.pfba(model, objective=None, reactions=None, fraction_of_optimum=1, *args, **kwargs)[source]

Parsimonious Enzyme Usage Flux Balance Analysis [1].

Parameters:
  • model (cameo.core.SolverBasedModel) – The model to perform pFBA with
  • objective (str or reaction or optlang.Objective) – An objective to be minimized/maximized for
  • reactions (list) – list of reactions to get results for. Getting fluxes from solution can be time consuming so if not all are needed it may be faster to request specific reactions.
  • fraction_of_optimum (float) – Fix the value of the current objective to a fraction of is maximum.
Returns:

Contains the result of the linear solver.
Return type:

FluxDistributionResult

References

[R3]Lewis, N. E., Hixson, K. K., Conrad, T. M., Lerman, J. A., Charusanti, P., Polpitiya, A. D., … Palsson, B. Ø. (2010). Omic data from evolved E. coli are consistent with computed optimal growth from genome-scale models. Molecular Systems Biology, 6, 390. doi:10.1038/msb.2010.47
cameo.flux_analysis.simulation.moma(model, reference=None, cache=None, reactions=None, *args, **kwargs)[source]

Minimization of Metabolic Adjustment[1]

Parameters:
Returns:

Contains the result of the solver.
Return type:

FluxDistributionResult

References

[R4]Segrè, D., Vitkup, D., & Church, G. M. (2002). Analysis of optimality in natural and perturbed metabolic networks. Proceedings of the National Academy of Sciences of the United States of America, 99(23), 15112–7. doi:10.1073/pnas.232349399
cameo.flux_analysis.simulation.lmoma(model, reference=None, cache=None, reactions=None, *args, **kwargs)[source]

Linear Minimization Of Metabolic Adjustment [1].

Parameters:
Returns:

Contains the result of the solver.
Return type:

FluxDistributionResult

References

[R5]Becker, S. A., Feist, A. M., Mo, M. L., Hannum, G., Palsson, B. Ø., & Herrgard, M. J. (2007). Quantitative prediction of cellular metabolism with constraint-based models: the COBRA Toolbox. Nature Protocols, 2(3), 727–38. doi:10.1038/nprot.2007.99
cameo.flux_analysis.simulation.room(model, reference=None, cache=None, delta=0.03, epsilon=0.001, reactions=None, *args, **kwargs)[source]

Regulatory On/Off Minimization [1].

Parameters:
Returns:

Contains the result of the linear solver.
Return type:

FluxDistributionResult

References

[R6]Tomer Shlomi, Omer Berkman and Eytan Ruppin, “Regulatory on/off minimization of metabolic flux changes after genetic perturbations”, PNAS 2005 102 (21) 7695-7700; doi:10.1073/pnas.0406346102

cameo.flux_analysis.structural module

Methods for stoichiometric analaysis

cameo.flux_analysis.structural.find_dead_end_reactions(model)[source]

Identify reactions that are structurally prevented from carrying flux (dead ends).

cameo.flux_analysis.structural.find_coupled_reactions(model, return_dead_ends=False)[source]

Find reaction sets that are structurally forced to carry equal flux

class cameo.flux_analysis.structural.ShortestElementaryFluxModes(model, reactions=None, c=1e-05, copy=True, change_bounds=True)[source]

Bases: object

Attributes

model
indicator_variables

cameo.flux_analysis.util module

cameo.flux_analysis.util.remove_infeasible_cycles(model, fluxes, fix=())[source]

Remove thermodynamically infeasible cycles from a flux distribution.

Parameters:
  • model (SolverBasedModel) – The model that generated the flux distribution.
  • fluxes (dict) – The flux distribution containing infeasible loops.
Returns:

A cycle free flux distribution.
Return type:

dict

References

[R7]A. A. Desouki, F. Jarre, G. Gelius-Dietrich, and M. J. Lercher, “CycleFreeFlux: efficient removal of thermodynamically infeasible loops from flux distributions.”
cameo.flux_analysis.util.fix_pfba_as_constraint(model, multiplier=1, fraction_of_optimum=1, time_machine=None)[source]

Fix the pFBA optimum as a constraint

Useful when setting other objectives, like the maximum flux through given reaction may be more realistic if all other fluxes are not allowed to reach their full upper bounds, but collectively constrained to max sum.

Parameters:
  • model (cameo.core.SolverBasedModel) – The model to add the pfba constraint to
  • multiplier (float) – The multiplier of the minimal sum of all reaction fluxes to use as the constraint.
  • fraction_of_optimum (float) – The fraction of the objective value’s optimum to use as constraint when getting the pFBA objective’s minimum
  • time_machine (TimeMachine, optional) – A TimeMachine instance can be provided, making it easy to undo this modification.

Module contents

This package provides methods for simulating and analyzing models.