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DOTcvpSB: a Matlab Toolbox for Dynamic Optimization in Systems ...

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<strong>DOTcvpSB</strong>: a <strong>Matlab</strong> <strong>Toolbox</strong> <strong>for</strong> <strong>Dynamic</strong> <strong>Optimization</strong> <strong>in</strong> <strong>Systems</strong> BiologyContents1 Introduction 71.1 <strong>Toolbox</strong> description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2 Description of ma<strong>in</strong> modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2.1 Utility modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.2.2 <strong>Optimization</strong> modules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81.3 Numerical optimization methods (NLP and MINLP solvers) . . . . . . . . . . . . . . . . . . . . 81.4 Numerical simulation method (IVP solvers) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.5 Recommended operat<strong>in</strong>g procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.6 <strong>Toolbox</strong> download . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101.7 <strong>Toolbox</strong> <strong>in</strong>stallation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 Optimal control problem 122.1 System and cost description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Control vector parametrization 133.1 NLP <strong>for</strong>mulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133.2 Implemented gradient methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.2.1 Sensitivity equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143.2.2 F<strong>in</strong>ite differences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.3 Gradients with respect to time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.4 Algorithm outl<strong>in</strong>e . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153.5 Brief <strong>in</strong><strong>for</strong>mation about the implemented modules . . . . . . . . . . . . . . . . . . . . . . . . . . 163.5.1 S<strong>in</strong>gle optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.5.2 Hybrid optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.5.3 Sucessive re-optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.5.4 SBML2<strong>DOTcvpSB</strong> module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.5.5 Simulation module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 S<strong>in</strong>gle optimization 174.1 From the ’<strong>in</strong>put’ to the ’output’ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174.1.1 Problem <strong>for</strong>mulation <strong>for</strong> DOTcvp, a simple <strong>in</strong>put file . . . . . . . . . . . . . . . . . . . . 174.1.2 Problem <strong>for</strong>mulation <strong>for</strong> DOTcvp, a regular <strong>in</strong>put file . . . . . . . . . . . . . . . . . . . . 184.1.3 Initialization, f<strong>in</strong>al results, and optimal trajectories . . . . . . . . . . . . . . . . . . . . . 215 Sucessive Re-optimization 235.1 Application of the mesh ref<strong>in</strong>ement algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . 236 Import<strong>in</strong>g SBML models 256.1 Illustrative system biology example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Page – 3

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