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dmitrisvetlovdmitrisvetlov
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Add OI and PI FSP scripts
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‎WorkSpace/Dmitri/OI_FSP.m

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%% Initial Setup
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close all
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clear all
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addpath(genpath('../../src'));
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M = [1 2 3 6]; % Number of intermediate states
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kOn = 0.1;
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kOff = kOn * 1000 / 2;
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tau = 50;
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delayPeriods = 10;
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tSpan = linspace(0, tau * delayPeriods, 1 + delayPeriods);
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Model = SSIT;
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Model.species = {'Dempty'; 'Dfull'; 'X'};
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initialConditionX = 44; % Result from actual SSA runs
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Model.initialCondition = [1; 0; initialConditionX];
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parameters = ({...
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'kPlus', 100; ... % (Delayed) rate of protein production
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'kMinus', 1; ... % Rate of protein degradation
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'kOn', kOn; ... % Rate of operator site emptying / activation
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'kOff', kOff; ... % Rate of operator site filling / deactivation
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'tau', tau ... % Delay in protein production
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});
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Model.parameters = parameters;
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Model.propensityFunctions = {
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'kOn*Dfull', ... % Operator site emptying
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'kOff*Dempty*X', ... % Operator site filling
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'kPlus*Dempty', ... % (Delayed) protein production
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'kMinus*X' ... % Protein degradation
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};
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Model.stoichiometry = [...
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1, -1, 0, 0; ... % Dempty
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-1, 1, 0, 0; ... % Dfull
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0, 0, 1, -1 ... % X
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];
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% The only delayed reaction in the system is the third, with a delay equal
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% to tau. It has a single reactant, one Dempty, so we must include it in
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% the delayed-reaction scheduling stochiometries:
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Model.delayedReactions = [3, Model.parameters{5,2}];
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Model.delayedReactionSchedulingS = [...
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0, 0, -1, 0; ... % Dempty
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0, 0, 0, 0; ... % Dfull
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0, 0, 0, 0 ... % X
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];
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datasetSizes = [1, 2, 5, 10, 15, 20, 30] * 1e3;
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datasetFractions = datasetSizes / 3e4;
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for dsCntr = 1:length(datasetSizes)
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datasetNames{dsCntr} = ['ssa_', num2str(datasetSizes(dsCntr)), '.csv'];
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end
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fspTimes = zeros(length(M), 1);
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fitTimes = zeros(length(M), length(datasetSizes));
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%% %% Operator intermediates
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paramsToFit = [1, 5]; % kPlus, tau
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fitParamValues = zeros(length(M), length(datasetSizes), length(paramsToFit));
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for Mcntr = 1:length(M)
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curM = M(Mcntr);
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oiModel = getNFModelWithOperatorIntermediates(...
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curM, initialConditionX, parameters);
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oiModel.tSpan = tSpan;
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oiModel.solutionScheme = 'FSP';
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tic;
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[oiFSPsoln, oiModel.fspOptions.bounds] = oiModel.solve;
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fspTimes(Mcntr) = toc;
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for dsCntr = 1:length(datasetSizes)
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curModel = oiModel; % Clone the model solved via FSP
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% Load the subset of the SSA data
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curModel = curModel.loadData(datasetNames{dsCntr}, ...
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{'Dempty','exp1_s1','Dfull','exp1_s2','X','exp1_s3'});
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% Only a subset of the parameters will be made "free"
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curModel.fittingOptions.modelVarsToFit = paramsToFit;
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fitOptions = optimset('Display', 'none', 'MaxIter', 1000);
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tic;
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fitParameters = curModel.maximizeLikelihood([], fitOptions);
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fitTimes(Mcntr,dsCntr) = toc;
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curModel.parameters(curModel.fittingOptions.modelVarsToFit, 2) = ...
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num2cell(fitParameters);
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for paramCntr = 1:length(paramsToFit)
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fitParamValues(Mcntr, dsCntr, paramCntr) = ...
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curModel.parameters(paramsToFit(paramCntr), 2);
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end
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end % for dsCntr = 1:length(datasetSizes) ...
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%piModel.makePlot(piFSPsoln,'meansAndDevs',[],[]) % Make plot of mean vs. time.
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%piModel.makePlot(piFSPsoln,'marginals',[],[]) % Make plot of marginals
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end % for Mcntr = 1:length(M)
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disp(fspTimes)
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save oiFSPtimes.mat fspTimes
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disp(fitTimes)
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save oiFitTimes.mat fitTimes
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disp(fitParamValues)
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save oiFitParamValues.mat fitParamValues

‎WorkSpace/Dmitri/PI_FSP.m

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%% Initial Setup
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close all
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clear all
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addpath(genpath('../../src'));
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M = [1 2 3 6]; % Number of intermediate states
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kOn = 0.1;
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kOff = kOn * 1000 / 2;
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tau = 50;
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delayPeriods = 10;
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tSpan = linspace(0, tau * delayPeriods, 1 + delayPeriods);
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Model = SSIT;
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Model.species = {'Dempty'; 'Dfull'; 'X'};
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initialConditionX = 44; % Result from actual SSA runs
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Model.initialCondition = [1; 0; initialConditionX];
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parameters = ({...
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'kPlus', 100; ... % (Delayed) rate of protein production
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'kMinus', 1; ... % Rate of protein degradation
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'kOn', kOn; ... % Rate of operator site emptying / activation
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'kOff', kOff; ... % Rate of operator site filling / deactivation
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'tau', tau ... % Delay in protein production
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});
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Model.parameters = parameters;
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Model.propensityFunctions = {
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'kOn*Dfull', ... % Operator site emptying
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'kOff*Dempty*X', ... % Operator site filling
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'kPlus*Dempty', ... % (Delayed) protein production
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'kMinus*X' ... % Protein degradation
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};
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Model.stoichiometry = [...
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1, -1, 0, 0; ... % Dempty
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-1, 1, 0, 0; ... % Dfull
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0, 0, 1, -1 ... % X
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];
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% The only delayed reaction in the system is the third, with a delay equal
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% to tau. It has a single reactant, one Dempty, so we must include it in
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% the delayed-reaction scheduling stochiometries:
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Model.delayedReactions = [3, Model.parameters{5,2}];
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Model.delayedReactionSchedulingS = [...
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0, 0, -1, 0; ... % Dempty
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0, 0, 0, 0; ... % Dfull
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0, 0, 0, 0 ... % X
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];
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datasetSizes = [1, 2, 5, 10, 15, 20, 30] * 1e3;
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datasetFractions = datasetSizes / 3e4;
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for dsCntr = 1:length(datasetSizes)
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datasetNames{dsCntr} = ['ssa_', num2str(datasetSizes(dsCntr)), '.csv'];
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end
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fspTimes = zeros(length(M), 1);
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fitTimes = zeros(length(M), length(datasetSizes));
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%% %% Protein intermediates
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paramsToFit = [1, 4, 5]; % kPlus, kOff, tau
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fitParamValues = zeros(length(M), length(datasetSizes), length(paramsToFit));
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for Mcntr = 1:length(M)
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curM = M(Mcntr);
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piModel = getNFModelWithProteinIntermediates(...
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curM, initialConditionX, parameters);
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piModel.tSpan = tSpan;
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piModel.solutionScheme = 'FSP';
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tic;
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[piFSPsoln, piModel.fspOptions.bounds] = piModel.solve;
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fspTimes(Mcntr) = toc;
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for dsCntr = 1:length(datasetSizes)
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curModel = piModel; % Clone the model solved via FSP
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% Load the subset of the SSA data
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curModel = curModel.loadData(datasetNames{dsCntr}, ...
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{'Dempty','exp1_s1','Dfull','exp1_s2','X','exp1_s3'});
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% Only a subset of the parameters will be made "free"
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curModel.fittingOptions.modelVarsToFit = paramsToFit;
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fitOptions = optimset('Display', 'none', 'MaxIter', 1000);
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tic;
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fitParameters = curModel.maximizeLikelihood([], fitOptions);
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fitTimes(Mcntr,dsCntr) = toc;
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curModel.parameters(curModel.fittingOptions.modelVarsToFit, 2) = ...
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num2cell(fitParameters);
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for paramCntr = 1:length(paramsToFit)
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fitParamValues(Mcntr, dsCntr, paramCntr) = ...
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curModel.parameters(paramsToFit(paramCntr), 2);
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end
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end % for dsCntr = 1:length(datasetSizes) ...
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%piModel.makePlot(piFSPsoln,'meansAndDevs',[],[]) % Make plot of mean vs. time.
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%piModel.makePlot(piFSPsoln,'marginals',[],[]) % Make plot of marginals
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end % for Mcntr = 1:length(M)
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disp(fspTimes)
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save piFSPtimes.mat fspTimes
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disp(fitTimes)
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save piFitTimes.mat fitTimes
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disp(fitParamValues)
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save piFitParamValues.mat fitParamValues

‎WorkSpace/Dmitri/oi_fsp.sh

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#!/bin/bash
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#SBATCH --nodes=1
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#SBATCH --time=3:00:00
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#SBATCH --partition=csu
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#SBATCH --qos=normal
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#SBATCH --ntasks=32
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#SBATCH --job-name=OI_FSP
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#SBATCH --output=OI_FSP.out
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#SBATCH --mail-type=ALL
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#SBATCH --mail-user=svetlov@colostate.edu
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module purge
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module load matlab/R2023b
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matlab -nodesktop -nodisplay -r 'clear; OI_FSP;'

‎WorkSpace/Dmitri/pi_fsp.sh

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#!/bin/bash
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#SBATCH --nodes=1
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#SBATCH --time=3:00:00
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#SBATCH --partition=csu
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#SBATCH --qos=normal
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#SBATCH --ntasks=32
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#SBATCH --job-name=PI_FSP
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#SBATCH --output=PI_FSP.out
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#SBATCH --mail-type=ALL
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#SBATCH --mail-user=svetlov@colostate.edu
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module purge
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module load matlab/R2023b
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matlab -nodesktop -nodisplay -r 'clear; PI_FSP;'

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