TrimGdel, tested under Gurobi12.0 and MATLAB2024b, is added.

src/design/TrimGdel/GRPRchecker.m

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+function [GR ,PR] = GRPRchecker(model,targetMet,givenGvalue)
+
+[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

@@ -0,0 +1,14 @@
+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
+