TrimGdel folder is added to ~/src/design/

src/design/TrimGdel/GRPRchecker.m

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+function [GR ,PR] = GRPRchecker(model, targetMet, givenGvalue)
+% GRPRchecker calculates the maximum GR and the minimu PR 
+% under the GR maximization when a constraint-based model, a target
+% metabolite, and a gene deletion stratety are given.
+%
+% function [GR, PR]  
+%      = GRPRchecker(model, targetMet, givenGvalue)
+%
+% INPUTS
+%  model     COBRA model structure containing the following required fields to perform gDel_minRN.
+%    rxns                    Rxns in the model
+%    mets                    Metabolites in the model
+%    genes               Genes in the model
+%    grRules            Gene-protein-reaction relations in the model
+%    S                       Stoichiometric matrix (sparse)
+%    b                       RHS of Sv = b (usually zeros)
+%    c                       Objective coefficients
+%    lb                      Lower bounds for fluxes
+%    ub                      Upper bounds for fluxes
+%    rev                     Reversibility of fluxes
+%
+%  targetMet   target metabolites
+%              (e.g.,  'btn_c')
+%  givenGvalue    The first column is the list of genes in the original model.
+%                 The second column contains a 0/1 vector indicating which genes should be deleted.
+%                 0 indicates genes to be deleted.
+%                 1 indecates genes to be remained.
+%
+% OUTPUTS
+%  GR        the growth rate obained when the gene deletion strategy is
+%            applied and the growth rate is maximized.
+%  PR        the minimum target metabolite production rate obained 
+%            when the gene deletion strategy is applied and the growth rate is maximized.
+%
+%   Feb. 10, 2025  Takeyuki TAMURA
+%
+
+
+[model, targetRID, extype] = modelSetting(model, targetMet);
+
+m = size(model.mets, 1);
+n = size(model.rxns, 1);
+g = size(model.genes, 1);
+gid = find(model.c);
+pid = targetRID;
+
+
+model2 = model;
+[grRules0] = calculateGR(model, givenGvalue);
+lb2 = model.lb;
+ub2 = model.ub;
+
+for i=1:n
+    if grRules0{i, 4} == 0
+        lb2(i) = 0;
+        ub2(i) = 0;
+    end
+end
+
+gm.A = sparse(model.S);
+gm.obj = -model.c;
+gm.modelsense = 'Min';
+gm.sense = repmat('=', 1, size(model.S, 1));
+gm.lb = lb2;
+gm.ub = ub2;
+opt0 = gurobi(gm);
+
+[opt0.x(gid) opt0.x(pid)]
+
+GR0 = -opt0.objval;
+lb2(gid) = GR0;
+ub2(gid) = GR0;
+model2.c(gid) = 0;
+model2.c(pid) = 1;
+
+gm2.A = sparse(model.S);
+gm2.obj = model2.c;
+gm2.modelsense = 'Min';
+gm2.sense = repmat('=', 1, size(model.S, 1));
+gm2.lb = lb2;
+gm2.ub = ub2;
+opt1 = gurobi(gm2);
+
+GR = GR0
+PR = opt1.x(pid)
+[GR PR]
+
+return;
+end
+

src/design/TrimGdel/TrimGdel.m

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+function [gvalue, GR, PR, size1, size2, size3, success] = TrimGdel(model, targetMet, maxLoop, PRLB, GRLB)
+%
+% TrimGdel appropriately considers GPR rules and determines 
+% a minimal gene deletion strategies to achieve growth-coupled production 
+% for a given target metabolite and a genome-scale model.
+% even in the worst-case analysis (ensures the weak-growth-coupled production).
+%
+% Gurobi is required for this version. 
+% The CPLEX version is available on https://github.com/MetNetComp/TrimGdel
+%
+% function [gvalue, GR, PR, size1, size2, size3, success] 
+%                  = TrimGdel(model, targetMet, maxLoop, PRLB, GRLB)
+%
+% INPUTS
+%  model     COBRA model structure containing the following required fields to perform gDel_minRN.
+%    rxns                    Rxns in the model
+%    mets                    Metabolites in the model
+%    genes               Genes in the model
+%    grRules            Gene-protein-reaction relations in the model
+%    S                       Stoichiometric matrix (sparse)
+%    b                       RHS of Sv = b (usually zeros)
+%    c                       Objective coefficients
+%    lb                      Lower bounds for fluxes
+%    ub                      Upper bounds for fluxes
+%    rev                     Reversibility of fluxes
+%
+%  targetMet   target metabolites
+%             (e.g.,  'btn_c')
+%  maxLoop   the maximum number of iterations in gDel_minRN
+%  PRLB           the minimum required production rates of the target metabolites
+%                     when gDel-minRN searches the gene deletion
+%                     strategy candidates. 
+%                     (But it is not ensured to achieve this minimum required value
+%                      when GR is maximized withoug PRLB.)
+%  GRLB         the minimum required growth rate 
+%                     when gDel-minRN searches the gene deletion
+%                     strategy candidates. 
+%
+% OUTPUTS
+%  gvalue       a small gene deletion strategy (obtained by TrimGdel).
+%              The first column is the list of genes.
+%              The second column is a 0/1 vector indicating which genes should be deleted.
+%              0 indicates genes to be deleted.
+%              1 indecates genes to be remained.
+%  GR           the maximum growth rate when the obtained gene deletion
+%              strategy represented by gvalue is applied.
+%  PR           the minimum production rate of the target metabolite under 
+%              the maximization of the growth rate when the obtained gene deletion
+%              strategy represented by gvalue is applied.
+%  size1        the number of gene deletions after Step1.
+%  size2        the number of gene deletions after Step2.
+%  size3        the number of gene deletions after Step3.
+%  success      indicates whether TrimGdel obained an appropriate gene
+%               deletion strategy. (1:success, 0:failure)
+%
+%   T. Tamura, "Trimming Gene Deletion Strategies for Growth-Coupled 
+%   Production in Constraint-Based Metabolic Networks: TrimGdel," 
+%   in IEEE/ACM Transactions on Computational Biology and Bioinformatics,
+%   vol. 20, no. 2, pp. 1540-1549, 2023.
+%
+%   Comprehensive computational results are accumulated in MetNetComp
+%   database.
+%   https://metnetcomp.github.io/database1/indexFiles/index.html
+%
+%   T. Tamura, "MetNetComp: Database for Minimal and Maximal Gene-Deletion Strategies 
+%   for Growth-Coupled Production of Genome-Scale Metabolic Networks," 
+%   in IEEE/ACM Transactions on Computational Biology and Bioinformatics, 
+%   vol. 20, no. 6, pp. 3748-3758, 2023, 
+%
+%   Feb. 6, 2025  Takeyuki TAMURA
+%
+
+[gvalue gr pr it success] = gDel_minRN(model, targetMet, maxLoop, PRLB, GRLB) % Step 1
+if success
+    [gvalue, GR, PR, size1, size2, size3] = step2and3(model, targetMet, gvalue) % Step 2 and 3
+else
+    gvalue = [];
+    GR = 0;
+    PR = 0;
+    size1 = 0;
+    size2 = 0;
+    size3 = 0;
+end
+
+end
+

src/design/TrimGdel/calculateGR.m

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+function [grRules] = calculateGR(model, xname)
+
+grRules = cell(size(model.rxns));
+for i=1:size(model.grRules, 1)
+    grRules{i, 1} = model.grRules{i,1};
+end
+for i = 1:size(model.rxns, 1)
+    if isempty(grRules{i, 1})==1
+        grRules{i,1} = '1';
+    end
+end
+grRules(:, 2) = strrep(grRules, 'or', '+');
+grRules(:,2) = strrep(grRules(:,2), 'and', '*');
+
+[xname2, index] = sortrows(xname(:,1), 'descend');
+for i=1:size(index, 1)
+   sorted_gvalue(i, 1) = xname{index(i, 1), 2}; 
+end
+for i = 1:size(model.genes, 1)
+    grRules(:, 2) = strrep(grRules(:, 2), xname2{i, 1},num2str(sorted_gvalue(i, 1)));
+end
+for i = 1:size(grRules, 1)
+    %i
+    if isempty(grRules{i, 2}) == 0
+        grRules{i, 3} = eval(grRules{i, 2});
+        if grRules{i, 3} > 0.9
+            grRules{i, 4} = 1;
+        else
+            grRules{i, 4} = 0;
+        end
+    else
+       grRules{i, 4} = -1; 
+    end
+end
+end
+

src/design/TrimGdel/example1.m

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+function [outputArg1, outputArg2] = example1()
+% example1 calculates the gene deletion strategy for growth coupling
+% for succinate in e_coli_core.
+%
+% Feb. 6, 2025  Takeyuki TAMURA
+%
+
+load('e_coli_core.mat');
+model = e_coli_core;
+
+[gvalue, GR, PR, size1, size2, size3, success] = TrimGdel(model, 'succ_e', 10, 0.1, 0.1)
+
+end
+

src/design/TrimGdel/example2.m

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+function [outputArg1, outputArg2] = example2()
+% example2 calculates the gene deletion strategy for growth coupling
+% for biotin in iML1515.
+%
+% Feb. 6, 2025  Takeyuki TAMURA
+%
+
+load('iML1515.mat');
+model = iML1515;
+[gvalue, GR, PR, size1, size2, size3, success] = TrimGdel(model, 'btn_c', 10, 0.1, 0.1)
+
+end
+

src/design/TrimGdel/example3.m

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+function [outputArg1, outputArg2] = example3()
+% example3 calculates the gene deletion strategy for growth coupling
+% for riboflavin in iML1515.
+%
+% Feb. 6, 2025  Takeyuki TAMURA
+%
+
+load('iML1515.mat');
+model = iML1515;
+
+[gvalue, GR, PR, size1, size2, size3, success] = TrimGdel(model, 'ribflv_c', 10, 0.1, 0.1)
+
+end
+

src/design/TrimGdel/example4.m

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+function [outputArg1, outputArg2] = exampl4()
+% example4 calculates the gene deletion strategy for growth coupling
+% for pantothenate in iML1515.
+%
+% Feb. 6, 2025  Takeyuki TAMURA
+%
+
+load('iML1515.mat');
+model = iML1515;
+
+[gvalue, GR, PR, size1, size2, size3, success] = TrimGdel(model, 'pnto__R_c', 10, 0.1, 0.1)
+
+end
+

src/design/TrimGdel/example5.m

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+function [outputArg1, outputArg2] = example5()
+% example5 calculates the gene deletion strategy for growth coupling
+% for succinate in iMM904.
+%
+% Feb. 6, 2025  Takeyuki TAMURA
+%
+
+load('iMM904.mat');
+model = iMM904;
+
+[gvalue, GR, PR, size1, size2, size3, success] = TrimGdel(model, 'succ_e', 10, 0.1, 0.1)
+
+end
+