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

‎src/design/TrimGdel/GRPRchecker.m

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function [GR ,PR] = GRPRchecker(model,targetMet,givenGvalue)
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[model,targetRID,extype] = modelSetting(model,targetMet)
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m=size(model.mets,1);
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n=size(model.rxns,1);
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g=size(model.genes,1);
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gid=find(model.c);
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pid=targetRID;
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model2=model;
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[grRules0] = calculateGR(model,givenGvalue);
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lb2=model.lb;
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ub2=model.ub;
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for i=1:n
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if grRules0{i,4}==0
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lb2(i)=0;
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ub2(i)=0;
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end
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end
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gm.A=sparse(model.S);
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gm.obj=-model.c;
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gm.modelsense='Min';
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gm.sense=repmat('=',1,size(model.S,1));
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gm.lb=lb2;
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gm.ub=ub2;
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opt0=gurobi(gm);
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[opt0.x(gid) opt0.x(pid)]
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GR0=-opt0.objval;
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lb2(gid)=GR0;
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ub2(gid)=GR0;
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model2.c(gid)=0;
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model2.c(pid)=1;
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gm2.A=sparse(model.S);
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gm2.obj=model2.c;
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gm2.modelsense='Min';
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gm2.sense=repmat('=',1,size(model.S,1));
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gm2.lb=lb2;
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gm2.ub=ub2;
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opt1=gurobi(gm2);
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GR=GR0
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PR=opt1.x(pid)
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[GR PR]
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return;
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end
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‎src/design/TrimGdel/README.pdf

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‎src/design/TrimGdel/TrimGdel.m

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

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

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‎src/design/TrimGdel/example1.m

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function [outputArg1,outputArg2] = example1()
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% example1 calculates the gene deletion strategy for growth coupling
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% for succinate in e_coli_core.
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%
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% Feb. 6, 2025 Takeyuki TAMURA
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%
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load('e_coli_core.mat');
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model=e_coli_core;
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[gvalue, GR, PR, size1, size2, size3, success] = TrimGdel(model,'succ_e',10,0.1,0.1)
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end
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‎src/design/TrimGdel/example2.m

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function [outputArg1,outputArg2] = example2()
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% example2 calculates the gene deletion strategy for growth coupling
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% for biotin in iML1515.
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%
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% Feb. 6, 2025 Takeyuki TAMURA
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%
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load('iML1515.mat');
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model=iML1515;
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[gvalue, GR, PR, size1, size2, size3, success] = TrimGdel(model,'btn_c',10,0.1,0.1)
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end
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‎src/design/TrimGdel/example3.m

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function [outputArg1,outputArg2] = example3()
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% example3 calculates the gene deletion strategy for growth coupling
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% for riboflavin in iML1515.
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%
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% Feb. 6, 2025 Takeyuki TAMURA
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%
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load('iML1515.mat');
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model=iML1515;
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[gvalue, GR, PR, size1, size2, size3, success] = TrimGdel(model,'ribflv_c',10,0.1,0.1)
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end
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‎src/design/TrimGdel/example4.m

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