CoreModel usage

In this tutorial we will introduce COBREXA's CoreModel and CoreModelCoupled. We will use E. coli's toy model to start with.

!isfile("e_coli_core.xml") &&
    download("http://bigg.ucsd.edu/static/models/e_coli_core.xml", "e_coli_core.xml")

using COBREXA

Loading a CoreModel

model = load_model(CoreModel, "e_coli_core.xml") # we specifically want to load a CoreModel from the model file

Metabolic model of type CoreModel
sparse([8, 10, 21, 43, 50, 51, 8, 9, 6, 12  …  33, 66, 68, 72, 23, 26, 33, 72, 22, 33], [1, 1, 1, 1, 1, 1, 2, 2, 3, 3  …  93, 93, 93, 93, 94, 94, 94, 94, 95, 95], [-1.0, 1.0, -1.0, 1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 1.0  …  1.0, -1.0, 1.0, -1.0, -1.0, 1.0, 1.0, -1.0, -1.0, 1.0], 72, 95)
Number of reactions: 95
Number of metabolites: 72

Basic analysis on CoreModel

As before, for optimization based analysis we need to load an optimizer. Here we will use Tulip.jl to optimize the linear programs of this tutorial. Refer to the examples of analysis and analysis modifications for details and explanations.

using Tulip

dict_sol = flux_balance_analysis_dict(
    model,
    Tulip.Optimizer;
    modifications = [
        change_objective("R_BIOMASS_Ecoli_core_w_GAM"),
        change_constraint("R_EX_glc__D_e"; lb = -12, ub = -12),
        change_constraint("R_EX_o2_e"; lb = 0, ub = 0),
    ],
)

Dict{String, Float64} with 95 entries:
  "R_EX_fum_e"    => 0.0
  "R_ACONTb"      => 0.294088
  "R_GLNS"        => 0.069699
  "R_SUCOAS"      => -5.31471e-11
  "R_TPI"         => 11.7289
  "R_EX_pi_e"     => -1.00274
  "R_PPC"         => 0.781108
  "R_O2t"         => -2.65248e-17
  "R_G6PDH2r"     => 4.23316e-9
  "R_TALA"        => -0.0487648
  "R_PPCK"        => 5.66414e-11
  "R_EX_lac__D_e" => 1.86429e-10
  "R_PGL"         => 4.23316e-9
  "R_H2Ot"        => 8.2857
  "R_GLNabc"      => 0.0
  "R_EX_co2_e"    => -0.487021
  "R_EX_gln__L_e" => 0.0
  "R_EX_nh4_e"    => -1.48633
  "R_MALt2_2"     => 0.0
  ⋮               => ⋮

Structure of CoreModel

CoreModel is optimized for analysis of models that utilizes the matrix, linearly-algebraic "view" of the models. It stores data in a sparse format wherever possible.

The structure contains fields that contain the expectable model elements:

fieldnames(CoreModel)

(:S, :b, :c, :xl, :xu, :rxns, :mets, :grrs)

model.S

72×95 SparseArrays.SparseMatrixCSC{Float64, Int64} with 360 stored entries:
⠀⠀⠀⠀⠀⠄⠀⠂⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠁⠀⠀⠠⠀⠀⠀⠀⠀⠀⠀⢥⠆⠀⠀⠀⠀⠀⠀⠀
⣤⠁⠃⠀⠄⡀⡀⠀⢂⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠀⠀⢀⢀⠀⠀⠀⡀⠀⠀⠀⠀⠀
⠀⠎⠠⣀⠤⡄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠤⣀⠀⢀⠀⠀⠀⠀⠀⠄⠄⠀⠄⠢⠀⠀⠠⠀⠀
⠀⠢⠘⡈⠒⢂⠄⠀⠀⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠒⠀⢀⢀⠀⠀⣀⠀⢀⠂⠂⠀⡚⠐⠀⠀⠐⠀⠀
⠂⠀⠀⠂⡀⠎⠂⢀⠀⠁⠀⠀⠀⠀⠀⠀⠐⠀⠀⠀⠀⠀⠀⠀⠀⠐⠀⠀⠀⠐⠐⠀⠀⠀⠂⠀⠀⠐⠄⠔
⠀⠀⠀⠀⠀⠂⠀⠈⠀⠈⡀⠀⠀⠀⠀⠀⠠⣦⠐⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠶⠀⠀⠀⠀⠀⠀⠀⠂⠂
⠀⠀⠀⠀⠀⡀⠀⠀⠀⠀⠈⠄⠀⠀⠀⠀⢀⠀⠊⢲⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠀⠀⠀⠀⠀⠀⠂⣀⣀
⠀⠀⠀⠀⠀⡂⠀⠀⠀⠀⠀⠈⠄⠀⠀⠀⠀⠀⠀⠀⠈⡲⣐⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀
⡀⠐⡀⢀⣒⡂⣒⢂⠀⠀⠀⠀⠡⠀⠀⠀⠀⢂⠀⢂⡀⣐⣒⡁⠆⣙⣀⢀⠀⠀⡀⢐⢀⣒⢀⡀⣀⠀⡀⠀
⠀⠐⠁⠈⠈⠀⠸⠈⠀⠀⠀⠀⠀⠡⠀⠀⠀⠈⠀⡈⠀⠀⠀⠁⠐⢂⣁⣈⠀⠀⠀⠀⠈⠀⠀⠁⠉⠀⠁⠀
⠆⠀⠀⠆⠆⡆⠀⠀⠀⠀⠀⠀⠀⠀⠁⠀⠀⠀⠀⢠⠆⠀⣤⢠⢠⠆⠱⢶⡆⠰⠀⠀⠀⠀⠀⠀⠀⠀⡆⠀
⠀⠀⠀⠀⠀⡀⡠⠀⠀⠀⠀⠀⠀⠀⢐⠀⠀⠀⠀⠀⠀⠁⠉⠀⠀⠀⢀⠀⠘⡄⠀⠀⠀⡀⠀⠀⠀⠀⠀⠀
⠀⠀⠀⠀⠒⡃⠀⠁⠀⠀⠀⠀⠀⠀⠀⠄⠀⠂⢈⠀⢊⠒⠀⠀⠀⡀⠀⣀⠀⢀⢀⠀⠰⢛⢊⡀⠀⠐⠀⠀
⠀⠀⠀⠀⠀⠄⠘⠀⠀⠀⠀⠀⠀⠀⠀⠈⠀⠀⠃⠀⠀⠀⠀⢀⠀⠀⠀⠘⠀⠀⠀⠀⠀⠀⠀⢡⠀⠃⠠⠀
⠀⠀⠀⠄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠂⠀⠂⠀⠀⠀⠀⠀⠀⠂⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⠒⠲⢉⡀

Contrary to the usual implementations, the model representation does not contain reaction coupling boudns; these can be added to any model by wrapping it with CoreCoupling. You may also use the prepared CoreModelCoupled to get a version of CoreModel with this coupling.

This page was generated using Literate.jl.