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TrimGdel folder is added to ~/src/design/
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‎src/design/TrimGdel/GRPRchecker.m

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function [GR ,PR] = GRPRchecker(model, targetMet, givenGvalue)
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% GRPRchecker calculates the maximum GR and the minimu PR
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% under the GR maximization when a constraint-based model, a target
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% metabolite, and a gene deletion stratety are given.
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%
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% function [GR, PR]
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% = GRPRchecker(model, targetMet, givenGvalue)
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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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% givenGvalue The first column is the list of genes in the original model.
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% The second column contains 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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%
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% OUTPUTS
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% GR the growth rate obained when the gene deletion strategy is
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% applied and the growth rate is maximized.
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% PR the minimum target metabolite production rate obained
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% when the gene deletion strategy is applied and the growth rate is maximized.
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%
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% Feb. 10, 2025 Takeyuki TAMURA
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%
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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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‎src/design/TrimGdel/example5.m

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function [outputArg1, outputArg2] = example5()
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% example5 calculates the gene deletion strategy for growth coupling
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% for succinate in iMM904.
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%
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% Feb. 6, 2025 Takeyuki TAMURA
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%
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load('iMM904.mat');
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model = iMM904;
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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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