example_01_CreateSSITModels.m

%% SSIT/Examples/example_01_CreateSSITModels

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%% Section 3.1: Create, Save, and Load Models in the SSIT
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%% Preliminaries:
% Clear from memory and close windows:
% clear
% close all
% Add SSIT source codes to Matlab search path:
addpath(genpath('../src'));

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%% Ex(1): Create a simple Bursting Gene model in the SSIT
%%  This model consists of 3 species: 
%   an inactive gene ('offGene'), an activated gene ('onGene'), and mRNA. 
%%  There are four reactions, each with a unique rate parameter: 
%   activation ('kon'), inactivation ('koff'), transcription ('kr'), 
%   and mRNA degradation ('dr')
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% Create an SSIT instance and call it 'Model':
Model = SSIT;    

% Set species names for bursting gene expression model:
Model.species = {'offGene';'onGene';'mRNA'}; 

% Set initial condition:
Model.initialCondition = [1;0;0];           

% Set stoichiometry of reactions:
Model.stoichiometry = [-1,1,0,0;...
                        1,-1,0,0;...
                        0,0,1,-1]; 

% Set propensity functions:
Model.propensityFunctions = {'kon * offGene';'koff * onGene';...
                             'kr * onGene';'dr * mRNA'}; 

% Set initial guesses for parameters:
Model.parameters = ({'kon',0.2; 'koff',0.2; 'kr',10; 'dr',5});

% Print a summary of STL1 Model:
Model.summarizeModel

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%% Ex(2): Copy the Bursting Gene Model above to a new model, 'STL1'.  
% The species, reactions, and stochiometries remain the same; 
% however, a change is made to the propensity for the first reaction 
% (gene activation) so that its rate is controlled by a time-varying 
% MAPK input signal. As a result, new parameters must also be added.
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% Create a copy of the simple Bursting Gene model from above:
STL1 = Model;

% Update propensity function for the gene activation reaction:
STL1.propensityFunctions{2} = 'onGene*koff/(1+Hog1)';

% Define a simplified time-varying TF/MAPK input signal:
STL1.inputExpressions = {'Hog1','(a0+a1*exp(-r1*t)*(1-exp(-r2*t))*(t>0))'};

% Add the new parameters from the TF/MAPK input signal:
STL1.parameters = ({'kon',0.2; 'koff',0.2; 'kr',10; 'dr',5;...
                    'a0',5; 'a1',10; 'r1',0.1; 'r2',.2}); 

% Print a summary of STL1 Model:
STL1.summarizeModel

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%% Ex(3): Copy the 'STL1' model for 4-(gene)-state dynamics.  
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% Create a copy of the STL1 model from above:
STL1_4state = STL1;

% Set species names for STL1_4state:
STL1_4state.species = {'g1';'g2';'g3';'g4';'mRNA'};

% Set initial condition:
STL1_4state.initialCondition = [1;0;0;0;0];  

% Set stoichiometry of reactions:
STL1_4state.stoichiometry = [-1,1,0,0,0,0,0,0,0,0,0;...   % gene state 1
                              1,-1,-1,1,0,0,0,0,0,0,0;... % gene state 2
                              0,0,1,-1,-1,1,0,0,0,0,0;... % gene state 3        
                              0,0,0,0,1,-1,0,0,0,0,0;...  % gene state 4
                              0,0,0,0,0,0,1,1,1,1,-1]     % mRNA
                 % Reactions: 1,2,3,4,5,6,7,8,9,10,11

% Add a lag to the time-varying TF/MAPK input signal:
STL1_4state.inputExpressions = ...
    {'Hog1',['A*(((1-(exp(1)^(-r1*(t-t0))))*',...
     'exp(1)^(-r2*(t-t0)))/(1+((1-(exp(1)^(-r1*(t-t0))))*',...
     'exp(1)^(-r2*(t-t0)))/M))^n*(t>t0)']};

% Set propensity functions:
STL1_4state.propensityFunctions = {...
          'k12*g1';'(max(0,k21o*(1-k21i*Hog1)))*g2';...
          'k23*g2';'k32*g3'; 'k34*g3';'k43*g4';...
          'kr1*g1';'kr2*g2';'kr3*g3';'kr4*g4';'dr*mRNA'}; 

% Add the new parameters for the 4 state model:
STL1_4state.parameters = ({'t0',3.17; 'k12',78; 'k21o',1.92e+05;...
    'k21i',3200; 'k23',0.402; 'k34',7.8; 'k32',1.62;...
    'k43',2.28; 'dr',0.294; 'kr1',4.68e-02; 'kr2',0.72;...
    'kr3', 59.4; 'kr4', 3.24; 'r1',4.14e-03; 'r2',0.426;...
    'A',9.3e+09; 'M',6.4e-04; 'n',3.1});

% Print a summary of STL1 Model:
STL1_4state.summarizeModel

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%% Ex.(4) Load and modify a pre-set SSIT model

% Several simple models are already coded in SSIT and can be quickly 
% loaded and modified. Below are currently available SSIT models (2025). 
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% ModelChoice = 'BirthDeath';      % One species problem
% ModelChoice = 'CentralDogma';    % Two species problem
% ModelChoice = 'BurstingGene';    % Two species problem
% ModelChoice = 'ToggleSwitch';    % Two species problem 
%                                    (non-linear toggle switch)
% ModelChoice = 'ToggleSwitch2';   % Two species problem
% ModelChoice = 'CentralDogmaTV';  % Two species problem (mRNa and protein) 
%                                    with time-varying transcription rate
% ModelChoice = 'Repressilator';               % Three species problem
% ModelChoice = 'BurstingSpatialCentralDogma'; % Four species problem 
%                                            (gene state, nuclear mRNa, 
%                                            cytoplasmic mRNA, and protein)
ModelChoice = 'RepressilatorGenes'; % Nine species problem

% Let's load one of the pre-existing SSIT models:
RepGenes_Model = SSIT(ModelChoice);

% View a summary of the 'RepressilatorGenes' model:
RepGenes_Model.summarizeModel


%% Save the basic Bursting Gene model, simple STL1 model, and 4-state STL1 
%% model for later use:
saveNames = unique({'Model'
    'STL1'
    'STL1_4state'
    });
    
save('ExampleSaveFiles/example_1_CreateSSITModels',saveNames{:})