%% Testing of the U_L model 
% by Evgenii Kovrigin, 6/1/2011
%%

close all 
clear all

%%
% Here you will find all figures
figures_folder='U_L_testing_figures';


%% Equilibrium concentrations
% Set some meaningful parameters
Rtotal=1e-3; % Receptor concentration, M
LRratio_array=[0 : 0.02 : 1.45]; % Array of L/R 
K_a_A=1e6;  % Binding affinity constant
K_a_B=4;  % Isomerization constant

% Set appropriate options for the model (see model file for details)
model_numeric_solver='analytical'  ; 
model_numeric_options='none';                                




%%
% *Compute arrays for populations and plot*
concentrations_array=[];
for counter=1:length(LRratio_array)
    % compute
    [concentrations species_names] = equilibrium_thermodynamic_equations.U_L_model(...
        Rtotal, LRratio_array(counter), K_a_A, K_a_B,...
        model_numeric_solver, model_numeric_options);
    % collect
    concentrations_array = [concentrations_array ; concentrations];
end

%% 
% Plot
Figure_title= 'U-L model';
X_range=[0 max(LRratio_array)+0.1 ]; % extend X just a bit past last point
Y_range=[ ]; % keep automatic scaling for Y
% display figure
figure_handle=equilibrium_thermodynamic_equations.plot_populations(...
    LRratio_array, concentrations_array, species_names, Figure_title, X_range, Y_range);
% save it
results_output.output_figure(figure_handle, figures_folder, 'Concentrations_plot');

%% 
% *Observations*
% The result is exactly what we expect from this model. Free ligand is
% split between two forms: binding-competent L and incompetent L*. As the
% receptor approaches saturation, amount of free ligand increases but ratio
% between L and L* remains constant.
%
% *Conclusion*
%
% The model *|equilibrium_thermodynamic_equations.U_L_model()|* works
% well.




%% Test of NMR line shape calculations  
% We will generate line shapes in conditions where we can expect
% specific patterns. We will plot a spectrum at a specific solution
% conditions (we will not use Series of datasets for simplicity).
%
% (1) Ligand-free receptor gives one peak; 
% (2) Saturated receptor gives one peak;
% (3) A titration series in slow and fast exchange 


%% Create NMR line shape dataset
% Create data object for  1D NMR line shapes
test1=NMRLineShapes1D('Simulation','Test_1');  
test1.set_active_model('U_L-model', model_numeric_solver, model_numeric_options)  
test1.show_active_model() % to look up necessary parameters of the model
%%
% Set range for X to extend beyond resonances
w_min= -300;
w_max= 500;
datapoints=100;  % Does not matter much because smooth curve is anyway calculated
test1.set_X(linspace(w_min, w_max, datapoints));

%%
% *Set fixed plotting ranges for easier comparison*
% If we do not set ranges they will be chosen automatically for each graph
test1.X_range=[w_min w_max]; % this sets display range in the plots
test1.Y_range=[0 1e-4]; % this sets display range in the plots



%% 
% Calculate ideal data: set noise RMSD to 0 for both X and Y 
X_RMSD=0;
Y_RMSD=0;


%% Free receptor 
% Use the same thermodynamic parameters as above. Add spectral and kinetic parameters:
LRratio = 0.001 ;  
Log10_K_A = log10(K_a_A);
Log10_K_B = log10(K_a_B);
k_2_A    = 1;   % dissociation rate constant of the complex, 1/s
w0_R     = -200;    % NMR frequency of the free receptor R, 1/s
w0_RL    = 400;  % NMR frequency of the bound complex RL, 1/s
FWHH_R   = 20;     % line width at half height of the peak of R, 1/s
FWHH_RL  = 20;     % line width at half height of the peak of RL, 1/s
ScaleFactor = 1;  % a multiplier for spectral amplitude (used only when fitting data)

% compute equilbrium concentrations
[concentrations species_names] = equilibrium_thermodynamic_equations.U_L2_model(...
        Rtotal, LRratio, K_a_A, K_a_B,...
        model_numeric_solver, model_numeric_options)


% plot line shapes
parameters=[ Rtotal  LRratio   Log10_K_A   Log10_K_B  k_2_A  ...
     w0_R   w0_RL  FWHH_R   FWHH_RL  ScaleFactor ];


test1.simulate_noisy_data(parameters, X_RMSD, Y_RMSD);
figure_handle=test1.plot_simulation('Free R');
results_output.output_figure(figure_handle, figures_folder, 'Free_R');

%%
% Expected peak of free R


%% 
% *Prepare TotalFit session for easy display of many graphs*
storage_session=TotalFit('All_tests');
% set aside for plotting later
storage_session.copy_dataset_into_array(test1);



%% Bound receptor 
% Use the same thermodynamic parameters as above. Add spectral and kinetic parameters:
LRratio = 10 ;  


% plot line shapes
parameters=[ Rtotal  LRratio   Log10_K_A   Log10_K_B  k_2_A  ...
     w0_R   w0_RL  FWHH_R   FWHH_RL  ScaleFactor ];


test1.simulate_noisy_data(parameters, X_RMSD, Y_RMSD);
figure_handle=test1.plot_simulation('Bound R');
results_output.output_figure(figure_handle, figures_folder, 'Bound_R');
% set aside for plotting later
storage_session.copy_dataset_into_array(test1);

%%
% Expected peak of RL



%% Intermediate point: slow exchange 
% Use the same thermodynamic parameters as above. Add spectral and kinetic parameters:
LRratio = 0.5 ;  
% compute equilbrium concentrations
[concentrations species_names] = equilibrium_thermodynamic_equations.U_L_model(...
        Rtotal, LRratio, K_a_A, K_a_B,...
        model_numeric_solver, model_numeric_options)

% plot line shapes
parameters=[ Rtotal  LRratio   Log10_K_A   Log10_K_B  k_2_A  ...
     w0_R   w0_RL  FWHH_R   FWHH_RL  ScaleFactor ];


test1.simulate_noisy_data(parameters, X_RMSD, Y_RMSD);
figure_handle=test1.plot_simulation('Slow: int.point');
results_output.output_figure(figure_handle, figures_folder, 'Slow_int_pt');
% set aside for plotting later
storage_session.copy_dataset_into_array(test1);

%%
% Expected splitting 50-50 between bound and free forms


%% Intermediate point: slow exchange - really strong shift towards L* 
% Use the same thermodynamic parameters as above. Add spectral and kinetic parameters:
K_a_B = 700 ;  
Log10_K_B = log10(K_a_B);


%
% *Compute arrays for populations and plot*
concentrations_array=[];
for counter=1:length(LRratio_array)
    % compute
    [concentrations species_names] = equilibrium_thermodynamic_equations.U_L_model(...
        Rtotal, LRratio_array(counter), K_a_A, K_a_B,...
        model_numeric_solver, model_numeric_options);
    % collect
    concentrations_array = [concentrations_array ; concentrations];
end

% Plot
Figure_title= 'U-L model: high KB';
X_range=[0 max(LRratio_array)+0.1 ]; % extend X just a bit past last point
Y_range=[ ]; % keep automatic scaling for Y
% display figure
figure_handle=equilibrium_thermodynamic_equations.plot_populations(...
    LRratio_array, concentrations_array, species_names, Figure_title, X_range, Y_range);
% save it
results_output.output_figure(figure_handle, figures_folder, 'Concentrations_plot_high_KB');
    
% plot line shapes
parameters=[ Rtotal  LRratio   Log10_K_A   Log10_K_B  k_2_A  ...
     w0_R   w0_RL  FWHH_R   FWHH_RL  ScaleFactor ];


test1.simulate_noisy_data(parameters, X_RMSD, Y_RMSD);
figure_handle=test1.plot_simulation('Slow: int.point2');
results_output.output_figure(figure_handle, figures_folder, 'Slow_int_pt2');
% set aside for plotting later
storage_session.copy_dataset_into_array(test1);

%%
% We observe that increasing shift of isomerization equilibrium in L
% towards non-binding L* leads to reduced saturation of R: as if binding
% affinity becomes weaker.


%% Summary of individual plots
storage_session.define_series('All-tests',1:length(storage_session.dataset_array));
figure_handle=storage_session.plot_1D_series('All-tests', 'All tests', 0);
results_output.output_figure(figure_handle, figures_folder, 'All_tests');
%%
% This is a comparison graph of all previous plots





%% Create a titration series 
% (see Tutorial 3 for details)
% Choose parameters
ZERO_LRratio= 1e-3; % This number cannot be too small---the model is numeric and will NOT converge!
LRratio_vector=[ZERO_LRratio  0.2   0.4   0.6  0.8   1.0   1.2   1.4 ];

k_2_A=1000; % fast exchange
K_a_B = 4 ;  % weaker isomerization
Log10_K_B = log10(K_a_B);

% choose the same parameters as above
session_name='Series';
session=TotalFit(session_name);
series_name='U-L_simulation';  
dataset_class_name='NMRLineShapes1D';  
model_name='U_L-model';
number_of_datasets=length(LRratio_vector);
X_column=test1.X; % use the same range

% create TotalFit session
session.create_simulation_series(series_name, dataset_class_name, model_name, ...
                 model_numeric_solver, model_numeric_options,...
                 number_of_datasets, X_column);

session.initialize_relation_matrix(sprintf('%s.all_datasets.txt', session_name));

% link parameters
parameter_number_array=[ 1    3 4 5 6 7 8 9 10 ];
session.batch_link_parameters_in_series(series_name, parameter_number_array);
session.generate_fitting_environment(sprintf('%s.fitting_environment.txt', session_name))

% prepare parameters
first_dataset_index=session.dataset_index(series_name,1);
FAKE_VALUE=1E-6;  % individual (unlinked) parameters: to be assigned later

ideal_parameters=[ Rtotal  FAKE_VALUE   Log10_K_A   Log10_K_B  k_2_A    ...
     w0_R     w0_RL  FWHH_R     FWHH_RL  ScaleFactor ];
 
% prepare formal limits
fake_limits=zeros(1,length(ideal_parameters));
Monte_Carlo_range_min=fake_limits;
Monte_Carlo_range_max=fake_limits;
parameter_limits_min=fake_limits;
parameter_limits_max=fake_limits;

% assign
session.assign_parameter_values(first_dataset_index, ideal_parameters, ...
        Monte_Carlo_range_min, Monte_Carlo_range_max, parameter_limits_min, parameter_limits_max);

% Assign unlinked LRratio and ranges
parameter_number=2;
values_array=LRratio_vector;

unlinked_fake_ranges=zeros(1,number_of_datasets);
MonteCarlo_range_min_array=unlinked_fake_ranges;
MonteCarlo_range_max_array=unlinked_fake_ranges;
parameter_limits_min_array=unlinked_fake_ranges;
parameter_limits_max_array=unlinked_fake_ranges;

session.assign_unlinked_parameter_in_series(...
             series_name, parameter_number, values_array, ...
             MonteCarlo_range_min_array, MonteCarlo_range_max_array, ...
             parameter_limits_min_array, parameter_limits_max_array);

% Simulate series
X_standard_dev=0;
Y_standard_dev=1e-8;  % something really small
session.simulate_series(series_name, X_standard_dev, Y_standard_dev);

% Plot
Y_offset_percent=10;  % to offset figures vertically for easier viewing
% set plotting ranges
X_range=test1.X_range;  % use old
Y_range=[];  % automatic
session.set_XY_ranges_series(series_name, X_range, Y_range)
figure_handle=session.plot_1D_series(series_name, session.name, Y_offset_percent);
results_output.output_figure(figure_handle, figures_folder, 'Series');
%%
% This is expected behavior for fast-intermediate regime.



%% Conclusions
%
% The U-L model for NMR 1D line shapes works as expected.















