Basic model operations#

Operations on bounds, the objective and flux distributions that the other guide pages assume. They work on conventional GEMs and ecModels in both languages: RAVEN toolbox functions in MATLAB, cobrapy idioms in Python. At the end of the page a bound is changed, a flux distribution is computed and inspected, and the ecModel is exported.

Functions#

MATLAB

Python

Note

setParam

Reaction.upper_bound, Reaction.lower_bound, Model.objective

sets bounds and the objective

solveLP

Model.optimize

runs FBA, or parsimonious FBA with the argument 1 in MATLAB

getPfbaEnzymes

pfba_enzymes

minimises the total enzyme usage

printFluxes

Solution.fluxes

prints the nonzero fluxes

exportToExcelFormat

Series.to_excel

writes the fluxes to a spreadsheet

exportModel

cobra.io.write_sbml_model

writes an SBML file

Setup#

A loaded model (ecModel in MATLAB, ec_model in Python) and, for the objective, the model adapter parameters params.

Set bounds and the objective#

Set the upper bound of a reaction to ten:

ecModel = setParam(ecModel, 'ub', 'r_0003', 10);

ec_model.reactions.get_by_id("r_0003").upper_bound = 10

Set the lower bound of a reaction to zero:

ecModel = setParam(ecModel, 'lb', 'r_0003', 0);

ec_model.reactions.get_by_id("r_0003").lower_bound = 0

Set the objective to maximize flux through the biomass reaction:

ecModel = setParam(ecModel, 'obj', params.bioRxn, 1);

ec_model.objective = params.bio_rxn

Run flux balance analysis#

Perform FBA, optimising the objective:

sol = solveLP(ecModel);

sol = ec_model.optimize()

Perform parsimonious FBA, optimising the objective and minimizing the total sum of flux:

sol = solveLP(ecModel, 1);

from cobra.flux_analysis import pfba

sol = pfba(ec_model)

GECKO 4: enzyme-aware pFBA

Both languages also offer a variant that minimizes total enzyme usage, the L1 norm of the usage_prot_* fluxes, in place of the total flux of all reactions. It is not part of the original Nature Protocols procedure, and it serves the enzyme-usage inspection done during simulation and analysis. It is not available for light ecModels, which have no usage_prot_* reactions to minimize.

sol = getPfbaEnzymes(ecModel);

from geckopy import pfba_enzymes

sol = pfba_enzymes(ec_model)

Inspect fluxes#

Inspect the nonzero fluxes:

Through exchange reactions (reactions without substrates or without products, as getExchangeRxns defines them; the usage_prot_<id> reactions have the protein pool as substrate and the enzyme pseudometabolite as product, so the list contains prot_pool_exchange and no usage reaction):

printFluxes(ecModel, sol.x)

Through all reactions:

printFluxes(ecModel, sol.x, false)

sol.fluxes is a pandas Series indexed by reaction id, so this is ordinary pandas filtering. Through all reactions:

print(sol.fluxes[sol.fluxes != 0])

Restricted to exchange reactions:

exchange_ids = [r.id for r in ec_model.exchanges]
print(sol.fluxes[exchange_ids][sol.fluxes[exchange_ids] != 0])

Export results and models#

Export the results to a spreadsheet. The file does not carry content from the ec structure and helps to find reaction identifiers quickly:

exportToExcelFormat(ecModel, 'filename.xlsx');

geckopy has no dedicated spreadsheet exporter; write the solution directly with pandas:

sol.fluxes.to_excel("filename.xlsx")

Export the ecModel to an SBML file for use in other constraint-based modelling software:

exportModel(ecModel, 'filename.xml');

import cobra

cobra.io.write_sbml_model(ec_model, "filename.xml")

As on Building an empty ecModel, this plain-cobrapy SBML export does not retain the ec_model.ec fields, the same caveat as MATLAB's exportModel.

See also#