Difference between revisions of "Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis"

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=== Summary ===
 
=== Summary ===
In this paper, the performance of multiple optimization approaches for estimating parameters in the context of ODE models in systems biology are investigated.
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This paper introduces the second-order adjoint sensitivity analysis for parameter estimation in ordinary differential equation (ODE) models.  
  
The following combinations of local and global search strategies were investigated:
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* The Hessian computational complexity scales linearly with the number of state variables and quadratically with the number of parameters -> good for low-dimensional problems.
* Local methods: Two deterministic optimization approaches (''fmincon'' with adjoint sensitivities vs. ''nl2sol'' with forward sensitivities) vs. gradient-free ''dynamic hill climbing'' vs. ''none'' (=only global)
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* The second-order adjoint sensitivity analysis for the computation of Hessians
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and a hybrid optimization-integration-based approach for profile likelihood computation introduced in this paper.
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* The second-order adjoint sensitivity analysis scales linearly with the number of parameters and state variables -> good for large scale ODE models.
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* it is shown that the hybrid computation method was more than 2-fold faster than the best competitor.

Revision as of 12:10, 25 February 2020

Citation

Paul Stapor, Fabian Fröhlich, and Jan Hasenauer, Optimization and profile calculation of ODE models using second order adjoint sensitivity analysis, 2018, Bioinformatics, Volume 34, Issue 13, Pages i151–i159

Summary

This paper introduces the second-order adjoint sensitivity analysis for parameter estimation in ordinary differential equation (ODE) models.

  • The Hessian computational complexity scales linearly with the number of state variables and quadratically with the number of parameters -> good for low-dimensional problems.
  • The second-order adjoint sensitivity analysis for the computation of Hessians

and a hybrid optimization-integration-based approach for profile likelihood computation introduced in this paper.

  • The second-order adjoint sensitivity analysis scales linearly with the number of parameters and state variables -> good for large scale ODE models.
  • it is shown that the hybrid computation method was more than 2-fold faster than the best competitor.