CBA_DEFAULT_OPTIONS - Default settings for directives in 'cba_options' and 'cba_constraints'
[cba_options, cba_constraints] = cba_default_cba_options(network)
Set default values for structures 'cba_options' and 'cba_constraints'
cba_constraints.v_fix: vector predetermined fluxes
cba_constraints.v_min: vector of lower bounds
cba_constraints.v_max: vector of upper bounds
cba_constraints.v_sign: predetermined flux signs
cba_constraints.v_mean: vector of data values (mean)
cba_constraints.v_std: vector of data values (std dev)
cba_constraints.ext_sign: sign vector for external metabolite production
cba_constraints.mu_fix: given mu values
cba_constraints.mu_min: lower bounds for mu values
cba_constraints.mu_max: upper bounds for mu values
cba_constraints.dmu_fix: given delta mu values
cba_constraints.dmu_min: lower bounds for delta mu values
cba_constraints.dmu_max: upper bounds for delta mu values
cba_constraints.dmu_sign: upper bounds for delta mu values
cba_constraints.ind_controllable: which enzymes are controllable?
cba_constraints.fx cost function (positive) expansion at u=0 (scalar)
cba_constraints.fxx cost function (positive) expansion at u=0 (scalar)
cba_constraints.hu cost function (positive) gradient at reference state
cba_options.seed random seed
cba_options.compute_mu 'mu' or 'delta_mu'
cba_options.verbose (Boolean)
cba_options.test_eba 1
cba_options.test_cba 1
cba_options.kinetic_law 'cs'
cba_options.cba_conditions 'y'
cba_options.objective 'fba', 'fit'
cba_options.check_curvatures default:10001 function [cba_options,cba_constraints] = cba_default_options(network) 0002 0003 % CBA_DEFAULT_OPTIONS - Default settings for directives in 'cba_options' and 'cba_constraints' 0004 % 0005 % [cba_options, cba_constraints] = cba_default_cba_options(network) 0006 % 0007 % Set default values for structures 'cba_options' and 'cba_constraints' 0008 % 0009 % cba_constraints.v_fix: vector predetermined fluxes 0010 % cba_constraints.v_min: vector of lower bounds 0011 % cba_constraints.v_max: vector of upper bounds 0012 % cba_constraints.v_sign: predetermined flux signs 0013 % cba_constraints.v_mean: vector of data values (mean) 0014 % cba_constraints.v_std: vector of data values (std dev) 0015 % cba_constraints.ext_sign: sign vector for external metabolite production 0016 % 0017 % cba_constraints.mu_fix: given mu values 0018 % cba_constraints.mu_min: lower bounds for mu values 0019 % cba_constraints.mu_max: upper bounds for mu values 0020 % cba_constraints.dmu_fix: given delta mu values 0021 % cba_constraints.dmu_min: lower bounds for delta mu values 0022 % cba_constraints.dmu_max: upper bounds for delta mu values 0023 % cba_constraints.dmu_sign: upper bounds for delta mu values 0024 % cba_constraints.ind_controllable: which enzymes are controllable? 0025 % 0026 % cba_constraints.fx cost function (positive) expansion at u=0 (scalar) 0027 % cba_constraints.fxx cost function (positive) expansion at u=0 (scalar) 0028 % cba_constraints.hu cost function (positive) gradient at reference state 0029 % 0030 % cba_options.seed random seed 0031 % cba_options.compute_mu 'mu' or 'delta_mu' 0032 % cba_options.verbose (Boolean) 0033 % cba_options.test_eba 1 0034 % cba_options.test_cba 1 0035 % cba_options.kinetic_law 'cs' 0036 % cba_options.cba_conditions 'y' 0037 % cba_options.objective 'fba', 'fit' 0038 % cba_options.check_curvatures default:1 0039 0040 [nm,nr] = size(network.N); 0041 0042 cba_constraints.v_fix = nan * ones(nr,1); 0043 cba_constraints.v_min = - ones(nr,1); 0044 cba_constraints.v_max = ones(nr,1); 0045 cba_constraints.v_sign = nan * ones(nr,1); 0046 cba_constraints.v_mean = nan * ones(nr,1); 0047 cba_constraints.v_std = nan * ones(nr,1); 0048 cba_constraints.ext_sign = nan * ones(nm,1); 0049 0050 cba_constraints.mu_fix = nan * ones(nm,1); 0051 cba_constraints.mu_min = - ones(nm,1); 0052 cba_constraints.mu_max = ones(nm,1); 0053 cba_constraints.dmu_fix = nan * ones(nr,1); 0054 cba_constraints.dmu_min = - ones(nr,1); 0055 cba_constraints.dmu_max = ones(nr,1); 0056 cba_constraints.dmu_sign = nan* ones(nr,1); 0057 0058 cba_constraints.y_min = []; % rate value 0059 cba_constraints.y_max = []; % rate value 0060 0061 cba_constraints.w_min = []; % metabolite value 0062 cba_constraints.w_max = []; % metabolite value 0063 0064 cba_constraints.z_ext = []; % benefit for production of external metabolites 0065 cba_constraints.z_int = zeros(nr,1); % direct benefit for fluxes 0066 cba_constraints.zc = []; % benefit for concentration of internal metabolites 0067 0068 cba_constraints.zx_scaled_min = 0.001; 0069 cba_constraints.u = nan * ones(nr,1); 0070 0071 cba_constraints.N_tot = network.N; 0072 0073 % enzyme cost, expansion at u = 0, costs are counted positive 0074 % h(u) = 0 + fx * u + 1/2 * fxx * u^2 0075 cba_constraints.fx = .1; % first-order enzyme cost 0076 cba_constraints.fxx = .1; % second-order enzyme cost 0077 0078 % enzyme cost (costs is counted positive), gradient at reference state 0079 cba_constraints.hu = []; 0080 0081 cba_constraints.ind_controllable = 1:nr; % controllable enzymes 0082 0083 cba_options.seed = nan; 0084 cba_options.compute_mu = 'delta_mu'; 0085 cba_options.verbose = 0; 0086 cba_options.test_eba = 1; 0087 cba_options.test_cba = 1; 0088 cba_options.kinetic_law = 'cs'; 0089 cba_options.cba_conditions = 'y'; 0090 cba_options.objective = 'fba'; % 'fit' 0091 cba_options.check_curvatures = 1;