Loopless flux balance analysis (ll-FBA)

Here we wil add loopless constraints to a flux balance model to ensure that the resultant solution is thermodynamically consistent. As before, we will use the core E. coli model, which we can download using download_model:

using COBREXA

download_model(
    "http://bigg.ucsd.edu/static/models/e_coli_core.json",
    "e_coli_core.json",
    "7bedec10576cfe935b19218dc881f3fb14f890a1871448fc19a9b4ee15b448d8",
)

"e_coli_core.json"

Additionally to COBREXA and the JSON model format package. We will also need a solver which can solve mixed-interger linear programs, such as HiGHS.

import JSONFBCModels
import HiGHS

model = load_model("e_coli_core.json")

JSONFBCModels.JSONFBCModel(#= 95 reactions, 72 metabolites =#)

Running a loopless FBA (ll-FBA)

One can directly use loopless_flux_balance_analysis to solve an FBA problem based on model where loopless constraints are added to all fluxes. This is the direct approach.

solution = loopless_flux_balance_analysis(model; optimizer = HiGHS.Optimizer)

ConstraintTrees.Tree{Float64} with 7 elements:
  :coupling                => ConstraintTrees.Tree{Float64}(#= 0 elements =#)
  :flux_stoichiometry      => ConstraintTrees.Tree{Float64}(#= 72 elements =#)
  :fluxes                  => ConstraintTrees.Tree{Float64}(#= 95 elements =#)
  :loopless_constraints    => ConstraintTrees.Tree{Float64}(#= 5 elements =#)
  :loopless_directions     => ConstraintTrees.Tree{Float64}(#= 75 elements =#)
  :loopless_driving_forces => ConstraintTrees.Tree{Float64}(#= 75 elements =#)
  :objective               => 0.873922

Loopless constraints can also be added to any model (e.g. enzyme constrained models). Refer to the source code of loopless_flux_balance_constraints for guidance.

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