Settings file¶

A place for the user to define all the relevant properties of model simulation that are not stored in SBtab. This are usually things that need to change during optimizations or model development.

These settings files can be found can be found on the respective model repository in the directory “Matlab/Settings”, in the example model from our main repository in the directory “Matlab/model/Model_Example/Matlab/Settings”, or by following these links:

Example model settings files

Fujita_2010 model settings files

Nair_2016 model settings files

Viswan_2018 model settings files

Default settings code

function [stg] = default_settings()

%% Import

% True or false to decide whether to run import functions (Import)
stg.import = true;


% Name of the excel file with the sbtab (SBtab excel name)
stg.sbtab_excel_name = "SBTAB.xlsx";

% Name of the model (Name)
stg.name = "model_name";

% Name of the default model compartment (Compartment name)
stg.cname = "Compartment";

% Name of the sbtab saved in .mat format (SBtab name)
stg.sbtab_name = "SBtab_" + stg.name;

%% Analysis

% Experiments to run (example experiment 1 to 3 and experimet 6)
stg.exprun = [1:3,6];

% Choice between 0,1,2 and 3,4 to choose the scoring method (check
% documentation) (Use logarithm)
stg.useLog = 4;

% True or false to decide whether to use multicore everywhere it is
% available (Optimization Multicore)
stg.optmc = true;

% Choice of ramdom seed (Ramdom seed)
stg.rseed = 1;

% True or false to decide whether to use display simulation diagnostics in
% the console (Simulation Console)
stg.simcsl = false;

% True or false to decide whether to display optimization results on
% console (Optimization console)
stg.optcsl = false;

% True or false to decide whether to save results (Save results)
stg.save_results = true;

% True or false to decide whether to run detailed simulation for plots
stg.simdetail = true;

%% Simulation

% Maximum time for each individual function to run in seconds (Maximum
% time)
stg.maxt = 10;

% Equilibration time (Equilibration time)
stg.eqt  = 50000;

% True or false to decide whether to do Dimensional Analysis (Dimensional
% Analysis)
stg.dimenanal = false;

% True or false to decide whether to do Unit conversion (Unit conversion)
stg.UnitConversion = false;

% True or false to decide whether to do Absolute Tolerance Scaling
% (Absolute Tolerance Scaling)
stg.abstolscale = true;

% Value of Relative tolerance (Relative tolerance)
stg.reltol = 1.0E-4;

% Value of Absolute tolerance (Absolute tolerance)
stg.abstol = 1.0E-4;

% Time units for simulation (Simulation time)
stg.simtime = "second";

% True or false to decide whether to run sbioaccelerate (after changing
% this value you need to run "clear functions" to see an effect)
% (sbioaccelerate)
stg.sbioacc = true;

% (Absolute tolerance step size for equilibration)
stg.abstolstepsize_eq = [];

% Max step size in the simulation (if empty matlab decides whats best)
% (Maximum step)
stg.maxstep = [10];

% Max step size in the equilibration (if empty matlab decides whats best)
% (Maximum step)
stg.maxstepeq = [2];

% Max step size in the detailed plots (if empty matlab decides whats best)
% (Maximum step)
stg.maxstepdetail = [2];

% Default score when there is a simulation error, this is needed to keep
% the optimizations working.
% (error score)
stg.errorscore = 10^5;
%% Model

% Number of parameters to optimize (Parameter number)
stg.parnum = 5;

original_parameter_set = zeros(1,10);

% Array with the lower bound of all parameters (Lower bound)
stg.lb = original_parameter_set-5;

% Array with the upper bound of all parameters (Upper bound)
stg.ub = original_parameter_set+5;

%% Diagnostics

% Choice of what parameters in the array to test, the indices correspond to
% the parameters in the model and the numbers correspond to the parameters
% in the optimization array, usually not all parameters are optimized so
% there needs to be a match between one and the other. (Parameters to test)
% In this example there are ten parameters in this imaginary model and we
% are only interested in parameter 2,4,8,9, and 10. Note that stg.parnum is
% five because of this and not ten
stg.partest(:,1) = [0,1,0,2,0,0,0,3,4,5];

% (Parameter array to test)
stg.pat = [1:2];

% All the parameter arrays, in this case there is only one (parameters here
% are in log10 space)(Parameter arrays)
stg.pa(1,:) = [1,1,1,1,1];
stg.pa(1,:) = [1,0,1,2,1];

% Best parameter array found so far for the model (Best parameter array)
stg.bestpa = stg.pa(1,:);

%% Plots

% True or false to decide whether to plot results (Plots)
stg.plot = true;

% True or false to decide whether to use long names in the title of the
% outputs plots in f_plot_outputs.m (Plot outputs long names)
stg.plotoln = true;

%% Sensitivity analysis

% Number of samples to use in SA (Sensitivity analysis number of samples)
stg.sansamples = 100;

% True or false to decide whether to subtract the mean before calculating
% SI and SIT (Sensitivity analysis subtract mean)
stg.sasubmean = true;

% Choose the way you want to obtain the samples of the parameters for
% performing the SA; 0 Log uniform distribution truncated at the parameter
% bounds 1 Log normal distribution with mu as the best value for a
% parameter and sigma as stg.sasamplesigma truncated at the parameter
% bounds 2 same as 1 without truncation 3 Log normal distribution centered
% at the mean of the parameter bounds and sigma as stg.sasamplesigma
% truncated at the parameter bounds 4 same as 3 without truncation.
% (Sensitivity analysis sampling mode)
stg.sasamplemode = 2;

% Sigma for creating the normal distribution of parameters to perform
% sensitivity analysis (Sensitivity analysis sampling sigma)
stg.sasamplesigma = 0.1;

%% Optimization

%  Time for the optimization in seconds (fmincon does not respect this
% time!!) (Optimization time)
stg.optt = 60*5;

% Population size for the algorithms that use populations (Population size)
stg.popsize = 144;

% optimization start method, choose between: 1 Random starting point or
% group of starting points inside the bounds 2 Random starting point or
% group of starting points near the best point (Optimization start method)
stg.osm = 1;

% Distance from best parameter array to be used in stg.osm method 2
% (Distance from best parameter array)
stg.dbs = 0.1;

% True or false to decide whether to use Multistart (Multistart)
stg.mst = false;

% Multistart size
stg.msts = 1;

% True or false to decide whether to display Plots (Plots doesn't work if
% using multicore) (Optimization plots)
stg.optplots = true;

% True or false to decide whether to run fmincon (no gradient so this
% doesn't work very well, no max time!!)
stg.fmincon = false;

% Options for fmincon (fmincon options)
stg.fm_options = optimoptions('fmincon',...
    'Algorithm','interior-point',...
    'MaxIterations',2,'OptimalityTolerance',0,...
    'StepTolerance',1e-6,'FiniteDifferenceType','central',...
    'MaxFunctionEvaluations',10000);

% True or false to decide whether to run simulated annealing (Simulated
% annealing)
stg.sa = false;

% Options for simulated annealing (Simulated annealing options)
stg.sa_options = optimoptions(@simulannealbnd, ...
    'MaxTime',stg.optt,...
    'ReannealInterval',40);

% True or false to decide whether to run Pattern search (Pattern search)
stg.psearch = false;

% Options for Pattern search (Pattern search options)
stg.psearch_options = optimoptions(@patternsearch,...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'UseCompletePoll',true,'UseCompleteSearch',true,...
    'MaxMeshSize',2,'MaxFunctionEvaluations',2000);

% True or false to decide whether to run Genetic algorithm (Genetic
% algorithm)
stg.ga = false;

% Options for Genetic algorithm (Genetic algorithm options)
stg.ga_options = optimoptions(@ga,'MaxGenerations',200,...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'PopulationSize',stg.popsize,...
    'MutationFcn','mutationadaptfeasible');

% True or false to decide whether to run Particle swarm (Particle swarm)
stg.pswarm = false;

% Options for Particle swarm (Particle swarm options)
stg.pswarm_options = optimoptions('particleswarm',...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,'MaxIterations',200,...
    'SwarmSize',stg.popsize);

% True or false to decide whether to run Surrogate optimization (Surrogate
% optimization)
stg.sopt = false;

% Options for Surrogate optimization (Surrogate optimization options)
stg.sopt_options = optimoptions('surrogateopt',...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'MaxFunctionEvaluations',5000,...
    'MinSampleDistance',0.2,'MinSurrogatePoints',32*2+1);
end

Example settings code

function [stg] = Example_model()

%% Import

% True or false to decide whether to run import functions (Import)
stg.import = true;

% Name of the excel file with the sbtab (SBtab excel name)
stg.sbtab_excel_name = "SBTAB example.xlsx";

% Name of the model (Name)
stg.name = "Example";

% Name of the default model compartment (Compartment name)
stg.cname = "Compartment";

%% Analysis

% Experiments to run
stg.exprun = [1,3];
% stg.exprun = [1,2,3];

% Choice between 0,1,2 and 3 to change either and how to apply log10 to the
% scores (check documentation) (Use logarithm)
stg.useLog = 0;

% True or false to decide whether to use multicore everywhere it is
% available (Optimization Multicore)
stg.optmc = false;

% Choice of ramdom seed (Ramdom seed)
stg.rseed = 1;

% True or false to decide whether to use display simulation diagnostics in
% the console (Simulation Console)
stg.simcsl = false;

% True or false to decide whether to display optimization results on
% console (Optimization console)
stg.optcsl = true;

% True or false to decide whether to display PLA results on console (PLA
% console)
stg.placsl = true;

% True or false to decide whether to save results (Save results)
stg.save_results = true;

% True or false to decide whether to run detailed simulation for plots
stg.simdetail = false;

%% Simulation

% Maximum time for each individual function to run in seconds (Maximum
% time)
stg.maxt = 2;

% Equilibration time (Equilibration time)
stg.eqt  = 50000;

% True or false to decide whether to do Dimensional Analysis (Dimensional
% Analysis)
stg.dimenanal = true;

% True or false to decide whether to do Unit conversion (Unit conversion)
stg.UnitConversion = true;

% True or false to decide whether to do Absolute Tolerance Scaling
% (Absolute Tolerance Scaling)
stg.abstolscale = true;

% Value of Relative tolerance (Relative tolerance)
stg.reltol = 1.0E-4;

% Value of Absolute tolerance (Absolute tolerance)
stg.abstol = 1.0E-7;

% Time units for simulation (Simulation time)
stg.simtime = "second";

% True or false to decide whether to run sbioaccelerate (after changing
% this value you need to run "clear functions" to see an effect)
% (sbioaccelerate)
stg.sbioacc = false;

% Max step size in the simulation (if empty matlab decides whats best)
% (Maximum step)
stg.maxstep = [];

% Max step size in the equilibration (if empty matlab decides whats best)
% (Maximum step)
stg.maxstepeq = [];

% Max step size in the detailed plots (if empty matlab decides whats best)
% (Maximum step)
stg.maxstepdetail = [0.001];

% Default score when there is a simulation error, this is needed to keep
% the optimizations working. (error score)
stg.errorscore = 10^5;

%% Model

% Number of parameters to optimize (Parameter number)
stg.parnum = 12;

% Index for the parameters that have thermodynamic constrains (Termodiamic
% Constrains Index)
stg.tci = [8];

% Parameters to multiply to the first parameter (in Stg.partest to get to
% the correct thermodynamic constrain formula) (Termodiamic Constrains
% multipliers)
stg.tcm([8],1) = [4];
stg.tcm([8],2) = [5];
stg.tcm([8],3) = [7];

% Parameters to divide to the first parameter (in Stg.partest to get to the
% correct thermodynamic constrain formula) (Termodiamic Constrains
% divisors)
stg.tcd([8],1) = [1];
stg.tcd([8],2) = [3];
stg.tcd([8],3) = [6];

% Array with the lower bound of all parameters (Lower bound)
stg.lb = zeros(1,stg.parnum)-4;

% Array with the upper bound of all parameters (Upper bound)
stg.ub = zeros(1,stg.parnum)+4;

%% Diagnostics

% Choice of what parameters in the array to test, the indices correspond to
% the parameters in the model and the numbers correspond to the parameters
% in the optimization array, usually not all parameters are optimized so
% there needs to be a match between one and the other. (Parameters to test)
stg.partest(:,1) = [1  ,2  ,3  ,4  ,5  ,6  ,7 ,2 ,8 ,9,...
    10 ,11 ,12];

% (Parameter array to test)
stg.pat = 1:3;

% All the parameter arrays, in this case there is only one (Parameter
% arrays)
stg.pa(1,:) = [3.999,0.696,1.072,3.429,-0.751,-3.741,-0.569,0.831,3.068,0.921,-2.156,-1.970];
stg.pa(2,:) = stg.pa(1,:)-1;
stg.pa(3,:) = stg.pa(1,:)+1;

% Best parameter array found so far for the model (Best parameter array)
stg.bestpa = stg.pa(1,:);

%% Plots

% True or false to decide whether to plot results (Plots)
stg.plot = true;

% True or false to decide whether to use long names in the title of the
% outputs plots in f_plot_outputs.m (Plot outputs long names)
stg.plotnames = true;

%% Sensitivity analysis

% Number of samples to use in SA (Sensitivity analysis number of samples)
stg.sansamples = 1000;

% True or false to decide whether to subtract the mean before calculating
% SI and SIT (Sensitivity analysis subtract mean)
stg.sasubmean = true;

% Choose the way you want to obtain the samples of the parameters for
% performing the SA; 0 Log uniform distribution truncated at the parameter
% bounds 1 Log normal distribution with mu as the best value for a
% parameter and sigma as stg.sasamplesigma truncated at the parameter
% bounds 2 same as 1 without truncation 3 Log normal distribution centered
% at the mean of the parameter bounds and sigma as stg.sasamplesigma
% truncated at the parameter bounds 4 same as 3 without truncation.
% (Sensitivity analysis sampling mode)
stg.sasamplemode = 2;

% Sigma for creating the normal distribution of parameters to perform
% sensitivity analysis (Sensitivity analysis sampling sigma)
stg.sasamplesigma = 0.1;

stg.gsabootstrapsize = ceil(sqrt(stg.sansamples));

%% Optimization

%  Time for the optimization in seconds (fmincon does not respect this
% time!!) (Optimization time)
stg.optt = 60*1;

% Population size for the algorithms that use populations (Population size)
stg.popsize = 10;

% optimization start method, choose between: 1 Random starting point or
% group of starting points inside the bounds 2 Random starting point or
% group of starting points near the best point (Optimization start method)
stg.osm = 1;

% Distance from best parameter array to be used in stg.osm method 2
% (Distance from best parameter array)
stg.dbs = 0.1;

% True or false to decide whether to use Multistart (Multistart)
stg.mst = false;

% Multistart size
stg.msts = 1;

% True or false to decide whether to display Plots (Plots doesn't work if
% using multicore) (Optimization plots)
stg.optplots = true;

% True or false to decide whether to run fmincon (no gradient so this
% doesn't work very well, no max time!!)
stg.fmincon = false;

% Options for fmincon (fmincon options)
stg.fm_options = optimoptions('fmincon',...
    'UseParallel',stg.optmc,...
    'Algorithm','interior-point',...
    'MaxIterations',2,'OptimalityTolerance',0,...
    'StepTolerance',1e-6,'FiniteDifferenceType','central',...
    'MaxFunctionEvaluations',10000);

% True or false to decide whether to run simulated annealing (Simulated
% annealing)
stg.sa = false;

% Options for simulated annealing (Simulated annealing options)
stg.sa_options = optimoptions(@simulannealbnd, ...
    'MaxTime',stg.optt,...
    'InitialTemperature',...
    ones(1,stg.parnum)*2,'ReannealInterval',40);

% True or false to decide whether to run Pattern search (Pattern search)
stg.psearch = false;

% Options for Pattern search (Pattern search options)
stg.psearch_options = optimoptions(@patternsearch,...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'UseCompletePoll',true,'UseCompleteSearch',true,...
    'MaxMeshSize',2,'MaxFunctionEvaluations',2000);

% True or false to decide whether to run Genetic algorithm (Genetic
% algorithm)
stg.ga = true;

% Options for Genetic algorithm (Genetic algorithm options)
stg.ga_options = optimoptions(@ga,'MaxGenerations',200,...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'PopulationSize',stg.popsize,...
    'MutationFcn','mutationadaptfeasible','Display','diagnose');

% True or false to decide whether to run Particle swarm (Particle swarm)
stg.pswarm = false;

% Options for Particle swarm (Particle swarm options)
stg.pswarm_options = optimoptions('particleswarm',...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'SwarmSize',stg.popsize);

% True or false to decide whether to run Surrogate optimization (Surrogate
% optimization)
stg.sopt = false;

% Options for Surrogate optimization (Surrogate optimization options)
stg.sopt_options = optimoptions('surrogateopt',...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'MaxFunctionEvaluations',5000,...
    'MinSampleDistance',0.2,'MinSurrogatePoints',32*2+1);
end

Import¶

Default settings code

% True or false to decide whether to run import functions (Import)
stg.import = true;


% Name of the excel file with the sbtab (SBtab excel name)
stg.sbtab_excel_name = "SBTAB.xlsx";

% Name of the model (Name)
stg.name = "model_name";

% Name of the default model compartment (Compartment name)
stg.cname = "Compartment";

% Name of the sbtab saved in .mat format (SBtab name)
stg.sbtab_name = "SBtab_" + stg.name;

Example settings code

% True or false to decide whether to run import functions (Import)
stg.import = true;

% Name of the excel file with the sbtab (SBtab excel name)
stg.sbtab_excel_name = "SBTAB example.xlsx";

% Name of the model (Name)
stg.name = "Example";

% Name of the default model compartment (Compartment name)
stg.cname = "Compartment";

stg.import - (logical) Decide whether to run import functions
stg.sbtab_excel_name - (string) Name of the Excel file with the SBtab
stg.name - (string) Name of the model
stg.cname - (string) Name of the default model compartment
stg.sbtab_name - (string) Name of the SBtab saved in .mat format

Analysis¶

Default settings code

% Experiments to run (example experiment 1 to 3 and experimet 6)
stg.exprun = [1:3,6];

% Choice between 0,1,2 and 3,4 to choose the scoring method (check
% documentation) (Use logarithm)
stg.useLog = 4;

% True or false to decide whether to use multicore everywhere it is
% available (Optimization Multicore)
stg.optmc = true;

% Choice of ramdom seed (Ramdom seed)
stg.rseed = 1;

% True or false to decide whether to use display simulation diagnostics in
% the console (Simulation Console)
stg.simcsl = false;

% True or false to decide whether to display optimization results on
% console (Optimization console)
stg.optcsl = false;

% True or false to decide whether to save results (Save results)
stg.save_results = true;

% True or false to decide whether to run detailed simulation for plots
stg.simdetail = true;

Example settings code

% Experiments to run
stg.exprun = [1,3];
% stg.exprun = [1,2,3];

% Choice between 0,1,2 and 3 to change either and how to apply log10 to the
% scores (check documentation) (Use logarithm)
stg.useLog = 0;

% True or false to decide whether to use multicore everywhere it is
% available (Optimization Multicore)
stg.optmc = false;

% Choice of ramdom seed (Ramdom seed)
stg.rseed = 1;

% True or false to decide whether to use display simulation diagnostics in
% the console (Simulation Console)
stg.simcsl = false;

% True or false to decide whether to display optimization results on
% console (Optimization console)
stg.optcsl = true;

% True or false to decide whether to display PLA results on console (PLA
% console)
stg.placsl = true;

% True or false to decide whether to save results (Save results)
stg.save_results = true;

% True or false to decide whether to run detailed simulation for plots
stg.simdetail = false;

stg.exprun - (double) Experiments to run
stg.useLog - (double) Choice between 0,1,2 and 3 to change either and how to apply log10 to the scores, check results:
stg.optmc - (logical) Decide whether to use multicore everywhere it is available
stg.rseed - (double) Choice of random seed
stg.simcsl - (logical) Decide whether to display simulation diagnostics in the console
stg.optcsl - (logical) Decide whether to display optimization results on console
stg.save_results - (logical) Decide whether to save results
stg.simdetail - (logical) Decide whether to run detailed simulation for plots

Simulation¶

Default settings code

% Maximum time for each individual function to run in seconds (Maximum
% time)
stg.maxt = 10;

% Equilibration time (Equilibration time)
stg.eqt  = 50000;

% True or false to decide whether to do Dimensional Analysis (Dimensional
% Analysis)
stg.dimenanal = false;

% True or false to decide whether to do Unit conversion (Unit conversion)
stg.UnitConversion = false;

% True or false to decide whether to do Absolute Tolerance Scaling
% (Absolute Tolerance Scaling)
stg.abstolscale = true;

% Value of Relative tolerance (Relative tolerance)
stg.reltol = 1.0E-4;

% Value of Absolute tolerance (Absolute tolerance)
stg.abstol = 1.0E-4;

% Time units for simulation (Simulation time)
stg.simtime = "second";

% True or false to decide whether to run sbioaccelerate (after changing
% this value you need to run "clear functions" to see an effect)
% (sbioaccelerate)
stg.sbioacc = true;

% (Absolute tolerance step size for equilibration)
stg.abstolstepsize_eq = [];

% Max step size in the simulation (if empty matlab decides whats best)
% (Maximum step)
stg.maxstep = [10];

% Max step size in the equilibration (if empty matlab decides whats best)
% (Maximum step)
stg.maxstepeq = [2];

% Max step size in the detailed plots (if empty matlab decides whats best)
% (Maximum step)
stg.maxstepdetail = [2];

% Default score when there is a simulation error, this is needed to keep
% the optimizations working.
% (error score)
stg.errorscore = 10^5;

Example settings code

% Maximum time for each individual function to run in seconds (Maximum
% time)
stg.maxt = 2;

% Equilibration time (Equilibration time)
stg.eqt  = 50000;

% True or false to decide whether to do Dimensional Analysis (Dimensional
% Analysis)
stg.dimenanal = true;

% True or false to decide whether to do Unit conversion (Unit conversion)
stg.UnitConversion = true;

% True or false to decide whether to do Absolute Tolerance Scaling
% (Absolute Tolerance Scaling)
stg.abstolscale = true;

% Value of Relative tolerance (Relative tolerance)
stg.reltol = 1.0E-4;

% Value of Absolute tolerance (Absolute tolerance)
stg.abstol = 1.0E-7;

% Time units for simulation (Simulation time)
stg.simtime = "second";

% True or false to decide whether to run sbioaccelerate (after changing
% this value you need to run "clear functions" to see an effect)
% (sbioaccelerate)
stg.sbioacc = false;

% Max step size in the simulation (if empty matlab decides whats best)
% (Maximum step)
stg.maxstep = [];

% Max step size in the equilibration (if empty matlab decides whats best)
% (Maximum step)
stg.maxstepeq = [];

% Max step size in the detailed plots (if empty matlab decides whats best)
% (Maximum step)
stg.maxstepdetail = [0.001];

% Default score when there is a simulation error, this is needed to keep
% the optimizations working. (error score)
stg.errorscore = 10^5;

stg.maxt - (double) Maximum time for each individual function has to run in seconds
stg.eqt - (double) Equilibration time in seconds
stg.dimenanal - (logical) Decide whether to do Dimensional Analysis
stg.UnitConversion - (logical) Decide whether to do Unit conversion
stg.abstolscale - (logical) Decide whether to do Absolute Tolerance Scaling
stg.reltol - (double) Value of Relative tolerance
stg.abstol - (double) Value of Absolute tolerance
stg.simtime - (string) Time units for simulation
stg.sbioacc - (logical) Decide whether to run sbioaccelerate (after changing this value you need to run “clear functions” to see an effect)
stg.abstolstepsize_eq - (double) Absolute tolerance step size for equilibration (if empty MATLAB® decides whats best)
stg.maxstep - (double) Max step size in the simulation (if empty MATLAB® decides what’s best)
stg.maxstepeq - (double) Max step size in the equilibration (if empty MATLAB® decides whats best)
stg.maxstepdetail - (double) Max step size in the detailed plots (if empty MATLAB® decides whats best)
stg.errorscore - (double) Default score when there is a simulation error, this is needed to keep the optimizations working.

Model¶

Default settings code

% Number of parameters to optimize (Parameter number)
stg.parnum = 5;

original_parameter_set = zeros(1,10);

% Array with the lower bound of all parameters (Lower bound)
stg.lb = original_parameter_set-5;

% Array with the upper bound of all parameters (Upper bound)
stg.ub = original_parameter_set+5;

Example settings code

% Number of parameters to optimize (Parameter number)
stg.parnum = 12;

% Index for the parameters that have thermodynamic constrains (Termodiamic
% Constrains Index)
stg.tci = [8];

% Parameters to multiply to the first parameter (in Stg.partest to get to
% the correct thermodynamic constrain formula) (Termodiamic Constrains
% multipliers)
stg.tcm([8],1) = [4];
stg.tcm([8],2) = [5];
stg.tcm([8],3) = [7];

% Parameters to divide to the first parameter (in Stg.partest to get to the
% correct thermodynamic constrain formula) (Termodiamic Constrains
% divisors)
stg.tcd([8],1) = [1];
stg.tcd([8],2) = [3];
stg.tcd([8],3) = [6];

% Array with the lower bound of all parameters (Lower bound)
stg.lb = zeros(1,stg.parnum)-4;

% Array with the upper bound of all parameters (Upper bound)
stg.ub = zeros(1,stg.parnum)+4;

stg.parnum - (double) Number of parameters to optimize
stg.tci - (double) Index for the parameters that have thermodynamic constraints
stg.tcm - (double) Parameters to multiply to the first parameter (in stg.partest to get to the correct thermodynamic constraint formula)
stg.tcd - (double) Parameters to divide to the first parameter (in stg.partest to get to the correct thermodynamic constraint formula)
stg.lb - (double) Lower bound of all parameters

\[stg.lb = \begin{bmatrix} lb_{1} & lb_{2} & ... & lb_{i} \end{bmatrix}\]
- \(i =\) Parameter index
stg.ub - (double) Upper bound of all parameters

\[stg.up = \begin{bmatrix} ub_{1} & ub_{2} & ... & ub_{i} \end{bmatrix}\]
- \(i =\) Parameter index

Diagnostics¶

Default settings code

% Choice of what parameters in the array to test, the indices correspond to
% the parameters in the model and the numbers correspond to the parameters
% in the optimization array, usually not all parameters are optimized so
% there needs to be a match between one and the other. (Parameters to test)
% In this example there are ten parameters in this imaginary model and we
% are only interested in parameter 2,4,8,9, and 10. Note that stg.parnum is
% five because of this and not ten
stg.partest(:,1) = [0,1,0,2,0,0,0,3,4,5];

% (Parameter array to test)
stg.pat = [1:2];

% All the parameter arrays, in this case there is only one (parameters here
% are in log10 space)(Parameter arrays)
stg.pa(1,:) = [1,1,1,1,1];
stg.pa(1,:) = [1,0,1,2,1];

% Best parameter array found so far for the model (Best parameter array)
stg.bestpa = stg.pa(1,:);

Example settings code

% Choice of what parameters in the array to test, the indices correspond to
% the parameters in the model and the numbers correspond to the parameters
% in the optimization array, usually not all parameters are optimized so
% there needs to be a match between one and the other. (Parameters to test)
stg.partest(:,1) = [1  ,2  ,3  ,4  ,5  ,6  ,7 ,2 ,8 ,9,...
    10 ,11 ,12];

% (Parameter array to test)
stg.pat = 1:3;

% All the parameter arrays, in this case there is only one (Parameter
% arrays)
stg.pa(1,:) = [3.999,0.696,1.072,3.429,-0.751,-3.741,-0.569,0.831,3.068,0.921,-2.156,-1.970];
stg.pa(2,:) = stg.pa(1,:)-1;
stg.pa(3,:) = stg.pa(1,:)+1;

% Best parameter array found so far for the model (Best parameter array)
stg.bestpa = stg.pa(1,:);

stg.partest - (double) Choice of which parameters to work on, since depending on the task, not all SBtab parameters are worked on. k indices correspond to the parameters in the SBtab and numbers up to i correspond to the parameters in the work set. This is the set that actually gets used for diagnostics, optimization, and sensitivity analyis.

\[stg.partest_k = \begin{bmatrix} 1_{k_1} & 2_{k_2} & ... & i_{k_{end}} \end{bmatrix}\]

In our example model parameter 216 from the SBtab is parameter number 1 of the work set, parameter 217 from the SBtab is parameter number 2 of the work set, and successively.

\[stg.partest_{[216:227]} = \begin{bmatrix} 1_{216} & 2_{217} & ... & 6_{221} & 1_{222} & 2_{223} & ... & 6_{227} \end{bmatrix}\]
stg.pat - (double) Index(\(j\)) of the parameter set to work on
stg.pa - (double) All the parameter sets

\[\begin{split}stg.pa = \begin{bmatrix} x_{1,1} & x_{2,1} & ... & x_{i,1} \\ x_{1,2} & x_{2,2} & ... & x_{i,2} \\ ... & ... & ... & ... \\ x_{1,j} & x_{2,j} & ... & x_{i,j} \end{bmatrix}\end{split}\]
stg.bestpa - (double) Best parameter set found so far during optimization

\[stg.bestx = \begin{bmatrix} bestx_{1} & bestx_{2} & ... & bestx_{i} \end{bmatrix}\]
- \(x =\) Parameters being worked on
- \(i =\) Index of Parameters being worked on
- \(k =\) Index of the parameters in SBtab
- \(j =\) Index of the Parameter set to work on

Plots¶

Default settings code

% True or false to decide whether to plot results (Plots)
stg.plot = true;

% True or false to decide whether to use long names in the title of the
% outputs plots in f_plot_outputs.m (Plot outputs long names)
stg.plotoln = true;

Example settings code

% True or false to decide whether to plot results (Plots)
stg.plot = true;

% True or false to decide whether to use long names in the title of the
% outputs plots in f_plot_outputs.m (Plot outputs long names)
stg.plotnames = true;

stg.plot - (logical) Decide whether to plot results
stg.plotoln - (logical) Decide whether to use long names in the title of the output plots in f_plot_outputs.m

Global Sensitivity Analysis (GSA)¶

Default settings code

% Number of samples to use in SA (Sensitivity analysis number of samples)
stg.sansamples = 100;

% True or false to decide whether to subtract the mean before calculating
% SI and SIT (Sensitivity analysis subtract mean)
stg.sasubmean = true;

% Choose the way you want to obtain the samples of the parameters for
% performing the SA; 0 Log uniform distribution truncated at the parameter
% bounds 1 Log normal distribution with mu as the best value for a
% parameter and sigma as stg.sasamplesigma truncated at the parameter
% bounds 2 same as 1 without truncation 3 Log normal distribution centered
% at the mean of the parameter bounds and sigma as stg.sasamplesigma
% truncated at the parameter bounds 4 same as 3 without truncation.
% (Sensitivity analysis sampling mode)
stg.sasamplemode = 2;

% Sigma for creating the normal distribution of parameters to perform
% sensitivity analysis (Sensitivity analysis sampling sigma)
stg.sasamplesigma = 0.1;

Example settings code

% Number of samples to use in SA (Sensitivity analysis number of samples)
stg.sansamples = 1000;

% True or false to decide whether to subtract the mean before calculating
% SI and SIT (Sensitivity analysis subtract mean)
stg.sasubmean = true;

% Choose the way you want to obtain the samples of the parameters for
% performing the SA; 0 Log uniform distribution truncated at the parameter
% bounds 1 Log normal distribution with mu as the best value for a
% parameter and sigma as stg.sasamplesigma truncated at the parameter
% bounds 2 same as 1 without truncation 3 Log normal distribution centered
% at the mean of the parameter bounds and sigma as stg.sasamplesigma
% truncated at the parameter bounds 4 same as 3 without truncation.
% (Sensitivity analysis sampling mode)
stg.sasamplemode = 2;

% Sigma for creating the normal distribution of parameters to perform
% sensitivity analysis (Sensitivity analysis sampling sigma)
stg.sasamplesigma = 0.1;

stg.gsabootstrapsize = ceil(sqrt(stg.sansamples));

stg.sansamples - (double) Number of samples to use in GSA (in total (2+npars)*sansamples simulations will be performed, where npars are the number of parameters).
stg.sasubmean - (logical) Decide whether to subtract mean before calculating SI and STI, see Halnes et al 2009.
stg.sasamplemode - (double) Choose the way you want to obtain the samples of the parameters for performing the GSA;

Reciprocal (log uniform) distribution

\(X_{i} \sim Reciprocal(a_{i},b_{i})\)

\(i =\) Parameter index

\(a_{i} = stg.lb_{i}\)

\(b_{i} = stg.ub_{i}\)

Example distribution with \(a = -1, b = 1\)

Log normal distribution with μ corresponding to the best value for a parameter, as recieved from the optimization, and σ as stg.sasamplesigma truncated at the parameter bounds

\(X_{i} \sim TruncatedLogNormal(μ_{i}, σ, a_{i}, b_{i})\)

\(i =\) Parameter index

\(μ_{i} = bestx_{i}\)

\(σ = stg.sasamplesigma\)

\(a_{i} = stg.lb_{i}\)

\(b_{i} = stg.ub_{i}\)

Example distribution with \(μ = 0.5, σ = 1, a = -1, b = 1\)

same as 1 without truncation

\(X_{i} \sim LogNormal(μ, σ)\)

\(i =\) Parameter index

\(μ_{i} = bestx_{i}\)

\(σ = stg.sasamplesigma\)

Example distribution with \(μ = 0.5, σ = 1\)

Log normal distribution with μ corresponding to the mean of the parameter bounds and σ as stg.sasamplesigma but truncated at the parameter bounds

\(X_{i} \sim TruncatedLogNormal(μ_{i}, σ, a_{i}, b_{i})\)

\(i =\) Parameter index

\(μ_{i} = \frac{stg.lb_{i} + (stg.ub_{i} - stg.lb_{i})}{2}\)

\(σ = stg.sasamplesigma\)

\(a_{i} = stg.lb_{i}\)

\(b_{i} = stg.ub_{i}\)

Example distribution with \(μ = \frac{a+(b-a)}{2}, σ = 1, a = -1, b = 1\)

same as 3 without truncation.

\(X_{i} \sim LogNormal(mu_{i}, σ)\)

\(i =\) Parameter index

\(μ_{i} = \frac{stg.lb_{i} + (stg.ub_{i} - stg.lb_{i})}{2}\)

\(σ = stg.sasamplesigma\)

Example distribution with \(μ = \frac{a+(b-a)}{2}, σ = 1, a = -1, b = 1\)

stg.sasamplesigma - (double) σ for creating the normal distribution of parameters to perform sensitivity analysis

Optimization¶

Default settings code

%  Time for the optimization in seconds (fmincon does not respect this
% time!!) (Optimization time)
stg.optt = 60*5;

% Population size for the algorithms that use populations (Population size)
stg.popsize = 144;

% optimization start method, choose between: 1 Random starting point or
% group of starting points inside the bounds 2 Random starting point or
% group of starting points near the best point (Optimization start method)
stg.osm = 1;

% Distance from best parameter array to be used in stg.osm method 2
% (Distance from best parameter array)
stg.dbs = 0.1;

% True or false to decide whether to use Multistart (Multistart)
stg.mst = false;

% Multistart size
stg.msts = 1;

% True or false to decide whether to display Plots (Plots doesn't work if
% using multicore) (Optimization plots)
stg.optplots = true;

% True or false to decide whether to run fmincon (no gradient so this
% doesn't work very well, no max time!!)
stg.fmincon = false;

% Options for fmincon (fmincon options)
stg.fm_options = optimoptions('fmincon',...
    'Algorithm','interior-point',...
    'MaxIterations',2,'OptimalityTolerance',0,...
    'StepTolerance',1e-6,'FiniteDifferenceType','central',...
    'MaxFunctionEvaluations',10000);

% True or false to decide whether to run simulated annealing (Simulated
% annealing)
stg.sa = false;

% Options for simulated annealing (Simulated annealing options)
stg.sa_options = optimoptions(@simulannealbnd, ...
    'MaxTime',stg.optt,...
    'ReannealInterval',40);

% True or false to decide whether to run Pattern search (Pattern search)
stg.psearch = false;

% Options for Pattern search (Pattern search options)
stg.psearch_options = optimoptions(@patternsearch,...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'UseCompletePoll',true,'UseCompleteSearch',true,...
    'MaxMeshSize',2,'MaxFunctionEvaluations',2000);

% True or false to decide whether to run Genetic algorithm (Genetic
% algorithm)
stg.ga = false;

% Options for Genetic algorithm (Genetic algorithm options)
stg.ga_options = optimoptions(@ga,'MaxGenerations',200,...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'PopulationSize',stg.popsize,...
    'MutationFcn','mutationadaptfeasible');

% True or false to decide whether to run Particle swarm (Particle swarm)
stg.pswarm = false;

% Options for Particle swarm (Particle swarm options)
stg.pswarm_options = optimoptions('particleswarm',...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,'MaxIterations',200,...
    'SwarmSize',stg.popsize);

% True or false to decide whether to run Surrogate optimization (Surrogate
% optimization)
stg.sopt = false;

% Options for Surrogate optimization (Surrogate optimization options)
stg.sopt_options = optimoptions('surrogateopt',...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'MaxFunctionEvaluations',5000,...
    'MinSampleDistance',0.2,'MinSurrogatePoints',32*2+1);
end

Example settings code

%  Time for the optimization in seconds (fmincon does not respect this
% time!!) (Optimization time)
stg.optt = 60*1;

% Population size for the algorithms that use populations (Population size)
stg.popsize = 10;

% optimization start method, choose between: 1 Random starting point or
% group of starting points inside the bounds 2 Random starting point or
% group of starting points near the best point (Optimization start method)
stg.osm = 1;

% Distance from best parameter array to be used in stg.osm method 2
% (Distance from best parameter array)
stg.dbs = 0.1;

% True or false to decide whether to use Multistart (Multistart)
stg.mst = false;

% Multistart size
stg.msts = 1;

% True or false to decide whether to display Plots (Plots doesn't work if
% using multicore) (Optimization plots)
stg.optplots = true;

% True or false to decide whether to run fmincon (no gradient so this
% doesn't work very well, no max time!!)
stg.fmincon = false;

% Options for fmincon (fmincon options)
stg.fm_options = optimoptions('fmincon',...
    'UseParallel',stg.optmc,...
    'Algorithm','interior-point',...
    'MaxIterations',2,'OptimalityTolerance',0,...
    'StepTolerance',1e-6,'FiniteDifferenceType','central',...
    'MaxFunctionEvaluations',10000);

% True or false to decide whether to run simulated annealing (Simulated
% annealing)
stg.sa = false;

% Options for simulated annealing (Simulated annealing options)
stg.sa_options = optimoptions(@simulannealbnd, ...
    'MaxTime',stg.optt,...
    'InitialTemperature',...
    ones(1,stg.parnum)*2,'ReannealInterval',40);

% True or false to decide whether to run Pattern search (Pattern search)
stg.psearch = false;

% Options for Pattern search (Pattern search options)
stg.psearch_options = optimoptions(@patternsearch,...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'UseCompletePoll',true,'UseCompleteSearch',true,...
    'MaxMeshSize',2,'MaxFunctionEvaluations',2000);

% True or false to decide whether to run Genetic algorithm (Genetic
% algorithm)
stg.ga = true;

% Options for Genetic algorithm (Genetic algorithm options)
stg.ga_options = optimoptions(@ga,'MaxGenerations',200,...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'PopulationSize',stg.popsize,...
    'MutationFcn','mutationadaptfeasible','Display','diagnose');

% True or false to decide whether to run Particle swarm (Particle swarm)
stg.pswarm = false;

% Options for Particle swarm (Particle swarm options)
stg.pswarm_options = optimoptions('particleswarm',...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'SwarmSize',stg.popsize);

% True or false to decide whether to run Surrogate optimization (Surrogate
% optimization)
stg.sopt = false;

% Options for Surrogate optimization (Surrogate optimization options)
stg.sopt_options = optimoptions('surrogateopt',...
    'MaxTime',stg.optt,'UseParallel',stg.optmc,...
    'MaxFunctionEvaluations',5000,...
    'MinSampleDistance',0.2,'MinSurrogatePoints',32*2+1);
end

stg.optt - (double) Time for the optimization in seconds (fmincon does not respect this time!!)
stg.popsize - (double) Population size (for the algorithms that use populations)
stg.osm - (double) optimization start method, choose between
1. Get a random starting parameter set or group of starting parameter sets inside the bounds
2. Get a random starting parameter set or group of starting parameter sets near the best parameter set
stg.dbpa - (double) Distance from best parameter set to be used in stg.osm method 2
stg.mst - (logical) Decide whether to use one or multiple starting parameter sets for the optimization
stg.msts - (double) Number of starting parameter sets for the optimizations
stg.optplots - (logical) Decide whether to display optimiazation plots (They aren’t ploted if running the code in multicore)
stg.fmincon - (logical) Decide whether to run fmincon (no gradient in our models so this doesn’t work very well, does not respect time set for the optimization!!)
stg.fm_options - (optim.options.Fmincon) Options for fmincon
stg.sa - (logical) Decide whether to run simulated annealing
stg.sa_options - (optim.options.SimulannealbndOptions) Options for simulated annealing
stg.psearch - (logical) Decide whether to run Pattern search
stg.psearch_options - (optim.options.PatternsearchOptions) Options for Pattern search
stg.ga - (logical) Decide whether to run Genetic algorithm
stg.ga_options - (optim.options.GaOptions) Options for Genetic algorithm
stg.pswarm - (logical) Decide whether to run Particle swarm
stg.pswarm_options - (optim.options.Particleswarm) Options for Particle swarm
stg.sopt - (logical) Decide whether to run Surrogate optimization
stg.sopt_options - (optim.options.Surrogateopt) Options for Surrogate optimization

Automatically generated at Import¶

stg.expn - (double) Total number of experiments stored in the SBtab
stg.outn - (double) Total number of experimental outputs specified in the SBtab

References¶

Halnes, G., Ulfhielm, E., Ljunggren, E.E., Kotaleski, J.H. and Rospars, J.P., 2009. Modelling and sensitivity analysis of the reactions involving receptor, G-protein and effector in vertebrate olfactory receptor neurons. Journal of Computational Neuroscience, 27(3), p.471.