Tutorial - distributedFBA.jl

This tutorial serves as a reference to get started with distributedFBA.jl. Download the live notebook from here.

If you are not familiar with COBRA.jl, or how COBRA.jl should be installed, please refer to the tutorial on COBRA.jl.



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using COBRA

Adding local workers

The connection functions are given in connect.jl, which, if a parallel version is desired, must be included separately:



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using Distributed #leejm516: This is needed even though COBRA imports that package 
include(joinpath(dirname(pathof(COBRA)), "connect.jl"))

You may add local workers as follows:



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# specify the total number of parallel workers
nWorkers = 4 

# create a parallel pool
workersPool, nWorkers = createPool(nWorkers)

The IDs of the respective workers are given in workersPool, and the number of local workers is stored in nWorkers.

In order to be able to use the COBRA module on all connected workers, you must invoke:



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@everywhere using COBRA;

Define and change the COBRA solver

Before the COBRA solver can be defined, the solver parameters and configuration must be loaded after having set the solverName (solver must be installed):

:GLPKMathProgInterface
:CPLEX
:Gurobi



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# specify the solver name
solverName = :GLPKMathProgInterface

# include the solver configuration file
include(joinpath(dirname(pathof(COBRA)), "../config/solverCfg.jl"))

The name of the solver can be changed as follows:



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# change the COBRA solver
solver = changeCobraSolver(solverName, solParams)

where solParams is an array with the definition of the solver parameters.

Load a COBRA model

As a test and as an example, the E.coli core model may be loaded as:



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# download the test model
using HTTP
include(joinpath(dirname(pathof(COBRA)), "../test/getTestmodel.jl"))
getTestModel()

Load the stoichiometric matrix S from a MATLAB structure named model in the specified .mat file



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model = loadModel("ecoli_core_model.mat", "S", "model");

where S is the name of the field of the stoichiometric matrix and model is the name of the model. Note the semicolon that suppresses the ouput of model.

Flux Balance Analysis (FBA)

In order to run a flux balance analysis (FBA), distributedFBA can be invoked with only 1 reaction and without an extra condition:



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# set the reaction list (only one reaction)
rxnsList = 13

# select the reaction optimization mode
#  0: only minimization
#  1: only maximization
#  2: maximization and minimization
rxnsOptMode = 1

# launch the distributedFBA process with only 1 reaction on 1 worker
minFlux, maxFlux  = distributedFBA(model, solver, nWorkers=1, rxnsList=rxnsList, rxnsOptMode=rxnsOptMode);

where the reaction number 13 is solved. Note that the no extra conditions are added to the model (last function argument is false). The minimum flux and maximum flux can hence be listed as:



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maxFlux[rxnsList]

Flux Variability Analysis (FVA)

In order to run a common flux variability analysis (FVA), distributedFBA can be invoked with all reactions as follows:



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# launch the distributedFBA process with all reactions
minFlux, maxFlux, optSol, fbaSol, fvamin, fvamax = distributedFBA(model, solver, nWorkers=4, optPercentage=90.0, preFBA=true);

The optimal solution of the original FBA problem can be retrieved with:



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optSol

The corresponding solution vector maxFlux of the original FBA that is solved with:



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fbaSol

The minimum and maximum fluxes of each reaction are in:



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maxFlux

The flux vectors of all the reactions are stored in fvamin and fvamax.



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fvamin



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fvamax

DistributedFBA of distinct reactions

You may now input several reactions with various rxnsOptMode values to run specific optimization problems.



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rxnsList = [1; 18; 10; 20:30; 90; 93; 95]
rxnsOptMode = [0; 1; 2; 2 .+ zeros(Int, length(20:30)); 2; 1; 0]

# run only a few reactions with rxnsOptMode and rxnsList
# distributedFBA(model, solver, nWorkers, optPercentage, objective, rxnsList, strategy, preFBA, rxnsOptMode)
minFlux, maxFlux, optSol, fbaSol, fvamin, fvamax, statussolmin, statussolmax = distributedFBA(model, solver);

Note that the reactions can be input as an unordered list.

Saving the variables

You can save the output of distributedFBA by using:



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saveDistributedFBA("results.mat")

Note that the results are saved in a .mat file that can be opened in MATLAB for further processing.

Cleanup

In order to cleanup the files generated during this tutorial, you can remove the files using:



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rm("ecoli_core_model.mat")
rm("results.mat")