Improving Performance

config/ConfigFile.txt

@@ -1,2 +1,2 @@
 1000000
-1e-8
+1e-6

src/Consistency Checking/SwiftCC.jl

@@ -72,10 +72,6 @@ See also: `MyModel`, myModel_Constructor(), 'getTolerance()', `reversibility()`,
     n_irr = length(irreversible_reactions_id)
     n_rev = length(reversible_reactions_id)
 
-    # Homogenizing the upper_bound and lower_bound of reactions:
-
-    lb, ub = homogenization(lb, ub)
-
     # Calculating the dimensions of the S matrix:
 
     row_num, col_num = size(S)
@@ -120,12 +116,12 @@ See also: `MyModel`, myModel_Constructor(), 'getTolerance()', `reversibility()`,
 
     # Constructing the I_reversible Matrix:
 
-    unit_vector(i,n) = [zeros(i-1); 1 ; zeros(n-i)]
-    reversible_reactions_id = sort(reversible_reactions_id)
-    I_reversible = unit_vector(reversible_reactions_id[1], n)
-    for i in range(2, n_rev; step=1)
-        a = unit_vector(reversible_reactions_id[i], n)
-        I_reversible = hcat(I_reversible, a)
+    I_reversible = zeros(n, n_rev)
+
+    rev_id = 1
+    for col in eachcol(I_reversible)
+        col[reversible_reactions_id[rev_id]] = 1.0
+        rev_id = rev_id + 1
     end
 
     # Removing irrevesible blocked from I_reversible Matrix:
@@ -169,16 +165,10 @@ See also: `MyModel`, myModel_Constructor(), 'getTolerance()', `reversibility()`,
     blocked_index = []
     blocked_index = union(rev_blocked_reactions, irr_blocked_reactions)
 
-    blocked_names = []
-    for i in blocked_index
-        r_name = reactions(myModel)[i]
-        push!(blocked_names, r_name)
-    end
-
-    # Returning a list consist of the names of the blocked reactions:
+    # Returning a list consist of the Ids of the blocked reactions:
 
-    blocked_names = sort(blocked_names)
-    return blocked_names
+    blocked_index = sort(blocked_index)
+    return blocked_index
 end
 
 end

src/Consistency Checking/TheNaiveApproach.jl

@@ -57,8 +57,6 @@ function find_blocked_reactions(myModel::StandardModel)
 
     # Determining the reversibility of a reaction:
 
-    irreversible_reactions_id = []
-    reversible_reactions_id = []
     irreversible_reactions_id, reversible_reactions_id = reversibility(lb)
 
     # Homogenizing the upper_bound and lower_bound of reactions:
@@ -140,17 +138,10 @@ function find_blocked_reactions(myModel::StandardModel)
         end
     end
 
-    blocked_reactions = union(reversible_blocked_reactions_id, irreversible_blocked_reactions_id)
-    blocked_names = []
+    blocked_index = union(reversible_blocked_reactions_id, irreversible_blocked_reactions_id)
+    blocked_index = sort(blocked_index)
 
-    for i in blocked_reactions
-        r_name = reactions(myModel)[i]
-        push!(blocked_names, r_name)
-    end
-
-    blocked_names = sort(blocked_names)
-
-    return blocked_names
+    return blocked_index
 
 end
 

src/Data Processing/pre_processing.jl

@@ -1,7 +1,7 @@
 #-------------------------------------------------------------------------------------------
 
 #=
-    Purpose:    Preprocessing functions of metabolic networks
+    Purpose:    Preprocessing functions of metabolic networks analysis
     Author:     Iman Ghadimi, Mojtaba Tefagh - Sharif University of Technology - Iran
     Date:       April 2022
 =#
@@ -107,6 +107,8 @@ julia> S, Metabolites, Reactions, Genes, m, n, lb, ub = dataOfModel(myModel)
 """
 
 function dataOfModel(myModel::StandardModel)
+
+    # Extracting Data:
     S = stoichiometry(myModel)
     Metabolites = metabolites(myModel)
     Reactions = reactions(myModel)
@@ -115,6 +117,13 @@ function dataOfModel(myModel::StandardModel)
     n = length(reactions(myModel))
     lb = lower_bounds(myModel)
     ub = upper_bounds(myModel)
+
+    # Sorting Reactions:
+    p = sortperm(Reactions)
+    Reactions = Reactions[p]
+    lb = lb[p]
+    ub = ub[p]
+    S = S[:,p]
     return S, Metabolites, Reactions, Genes, m, n, lb, ub
 end
 

src/QFCA/distributedQFCA.jl

@@ -145,6 +145,10 @@ function distributedQFCA(myModel::StandardModel, removing::Bool=false)
 
     lb, ub = homogenization(lb, ub)
 
+    # assigning a small value to Tolerance representing the level of error tolerance:
+
+    Tolerance = getTolerance()
+
     # Creating a newly object of MyModel:
 
     ModelObject = MyModel(S, Metabolites, Reactions, Genes, m, n, lb, ub)
@@ -165,8 +169,6 @@ function distributedQFCA(myModel::StandardModel, removing::Bool=false)
 
     # Defining a SharedMatrix:
 
-    @time begin
-
     DC_Matrix = SharedArray{Int,2}((col_noBlocked, col_noBlocked), init = false)
 
     # Finding Coupling between reactions by using swiftCC functions in Parallel:
@@ -233,11 +235,9 @@ function distributedQFCA(myModel::StandardModel, removing::Bool=false)
         end
     end
 
-    end
-
-    # Defining a matrix to save coupling relations:
+    # Defining a matrix to show coupling relations
 
-    fctable = zeros(col_noBlocked, col_noBlocked)
+    fctable = SharedArray{Int,2}((col_noBlocked, col_noBlocked), init = false)
 
     # Directional Coupling:
 
@@ -282,7 +282,7 @@ function distributedQFCA(myModel::StandardModel, removing::Bool=false)
 
     # Determining Partial Couples:
 
-    PC = Dict()
+    @everywhere PC = Dict()
 
     s = 1
     Partially_Couples = []
@@ -299,7 +299,7 @@ function distributedQFCA(myModel::StandardModel, removing::Bool=false)
 
     # Solving a LU for each pair to determine Fully Coupling:
 
-    for key in sort(collect(keys(PC)))
+    @sync @distributed for key in sort(collect(keys(PC)))
 
         # Transposing S:
 
@@ -327,7 +327,7 @@ function distributedQFCA(myModel::StandardModel, removing::Bool=false)
 
         # Determining Fully Coupling:
 
-        if isapprox(norm(Sol), 0.0, atol = 1e-8)
+        if isapprox(norm(Sol), 0.0, atol = Tolerance)
             fctable[PC[key][1],PC[key][2]] = 1.0
         end
     end